{"as_of":"2026-08-06T13:23:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:362ec3705bc8ca5a89be996580508f3a031dd01337489a5bd0659b9497c03de4","coverage":[{"denominator":299,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":100,"source":"paper_references, paper_reference_links","source_observed_at":"2026-07-13T17:08:58.831798Z","state":"measured"},{"denominator":102,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":102,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-06T06:34:29.942622+00:00","state":"measured"},{"denominator":2,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":2,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-01T04:17:44.004753Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.02900","snapshot_observed_at":"2026-08-01T04:17:44.004753Z","title":"arXiv preprint arXiv:2605.02900 , year =","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2607.22877","last_updated":"2026-07-24T19:40:48Z","snapshot_observed_at":"2026-08-01T04:17:41.186835Z","submitted_at":"2026-07-24T19:40:48Z","title":"Physical AI Governance: From Theory to Practice Across Life Cycle","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-01T04:17:44.004753Z"},"links":{"cited_paper":"/paper/2605.02900","citing_paper":"/paper/2607.22877"},"observation_digest":"sha256:93bbff8a2584f0564ab43449b2e690515deb2ef628dde29e495fc465d0ddcc45","observation_id":"8c99c184-9f68-4612-b446-7e0553840156","resolution":{"observed_at":"2026-08-01T04:17:44.004753Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.02900","snapshot_observed_at":"2026-08-01T00:48:40.551680Z","title":"Safety in embodied ai: A survey of risks, attacks, and defenses.arXiv preprint arXiv:2605.02900, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2607.26121","last_updated":"2026-07-28T17:50:44Z","snapshot_observed_at":"2026-08-01T23:17:18.763159Z","submitted_at":"2026-07-28T17:50:44Z","title":"Towards Trustworthy Embodied Intelligence: A Systems Framework and Graded Trustworthiness Levels","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-01T00:48:40.551680Z"},"links":{"cited_paper":"/paper/2605.02900","citing_paper":"/paper/2607.26121"},"observation_digest":"sha256:b50e83c662fe734ac9a4b2babce024db46e73b1b14cdaf28a1b4996f26319afe","observation_id":"0a312602-b0b3-40a6-a2d0-d66e0ed426ab","resolution":{"observed_at":"2026-08-01T00:48:40.551680Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"links":{"evidence":"/evidence","html":"/paper/2605.02900/citation-record","integrity":"/paper/2605.02900/integrity","json":"/paper/2605.02900/citation-record.json","paper":"/paper/2605.02900"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"1904.05734","last_updated":"2019-03-18T20:10:13Z","snapshot_observed_at":"2026-08-01T15:21:34.609217Z","submitted_at":"2019-03-18T20:10:13Z","title":"Practical Hidden Voice Attacks against Speech and Speaker Recognition Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05734","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Practical hidden voice attacks against speech and speaker recognition systems.arXiv preprint arXiv:1904.05734, 2019","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/1904.05734","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:f4c2a2eee55d36ded490ba643cea653b4d43dd21102014c73a4c7ca0ae810b29","observation_id":"fa5abc3f-f819-425a-837c-efa07ecd60fd","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Vision-onlyrobotnavigationinaneuralradianceworld.IEEERoboticsandAutomationLetters(RA-L), 2022","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:8d83d243eeb5316c61c210ec6367bda83e5878d2193769d3ed659b3d89efc560","observation_id":"05f2d849-ffac-48cf-a729-d3a736b7423a","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Cascading failures in agentic ai.https://adversa.ai/blog/cascading-failures-in-age ntic-ai-complete-owasp-asi08-security-guide-2026/, 2025","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:5fa0b51d88d8866f68c454c423855ee48e20dda818b4ae3f031f3e9a734cf3ba","observation_id":"93c8ba71-a4e9-42a3-8076-0befa6a2eef2","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Distributionally adaptive meta reinforcement learning","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:e70813359e3837baf9830d2f555a1fd109951cb4d04f1830922e0a19d8daaa12","observation_id":"3f8eca13-bbd1-4564-933f-90488c0f832d","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Ataxonomyoffactorsinfluencingperceived safety in human–robot interaction.Robotics, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:6ba200edf7370737c3e607ac4d8a0f61bb8c0d86797e7976442fb016efad52a4","observation_id":"1c94bd1b-4634-4418-bf8e-edb72025ad39","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.17830","last_updated":"2026-05-18T04:06:34Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2026-05-18T04:06:34Z","title":"Remembering More, Risking More: Longitudinal Safety Risks in Memory-Equipped LLM Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.17830","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Remembering more, risking more: Longitudinal safety risks in memory-equipped LLM agents.arXiv preprint arXiv:2605.17830, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2605.17830","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:53da446f1610cd098cbd9f31983de936e60d75ff153212839d797efa1e895468","observation_id":"21843cdc-7a07-478b-8826-34be5cb58858","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"The adolescence of technology.https://www.darioamodei.com/essay/the-adolescen ce-of-technology, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:ba3ed6abd47681f87447739ff0a1c3dc57cb05acaec1e7337099f4b448caadc6","observation_id":"f78c970a-890d-4a97-bbec-53752b9c2afa","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"FlowHĳack: Adynamics-aware backdoor attack on flow-matching vision-language-action models","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:a95509d086f651e7f988a855171a8f677460a0168c92b628fb0909dd727600e1","observation_id":"e4809d9a-a70c-48f4-a879-16cc5508948c","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Chips-messagerobustauthentication(chimera)forgpscivilian signals","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:23438cd43119b4f3ddfe0c0ac896116b8b8240dbb4b0be6abf3c48ac226e7b41","observation_id":"80fb9495-f751-442c-a6b7-32de9381f945","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Vision-and-language navigation: Interpreting visually-grounded navigation instructions in real environments","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:f610b648a7c1c1b8814fbceb344df87e2b76944f9c5e035dd3794162c038ada1","observation_id":"8901a8fb-04d6-44ea-81a0-9f71963940bb","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2507.21046","last_updated":"2026-01-16T20:59:08Z","snapshot_observed_at":"2026-08-01T06:32:44.461162Z","submitted_at":"2025-07-28T17:59:05Z","title":"A Survey of Self-Evolving Agents: What, When, How, and Where to Evolve on the Path to Artificial Super Intelligence","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2507.21046","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"A survey of self-evolving agents: What, when, how, and where to evolve on the path to artificial super intelligence.arXiv preprint arXiv:2507.21046, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2507.21046","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:f8f03c488a133a385955fa4886683bafb08ecb1775297b4c4fcc4329ecd732a6","observation_id":"a2037cc6-0740-43ab-a44e-0c4bded27524","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Ashcraft, Ted Staley, Josh Carney, Cameron Hickert, Derek Juba, Kiran Karra, and Nathan Drenkow","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:667e87e4b4a827d71b69b91d68e76b87136aee5614a13c193e77197e854a6b53","observation_id":"9148838f-a866-492a-bf3e-12cf655aedf2","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Mash-vlm: Mitigating action-scene hallucination in video-llms through disentangled spatial-temporal representations","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:645d460cc8e989e4f03d777280aac3766d12a5c86cfcfac35a3a646d21fab5be","observation_id":"f82a30d0-d60f-4e36-8301-fbcddb254566","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Multi-robot coordination with adversarial perception","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:d3e401a61ec720188064edae52252d441a7d8933305cfda4b234b99eff0af072","observation_id":"d77ae71f-89ec-4484-bf06-e8d758ce2f77","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Rat: Adversarial attacks on deep reinforcement agents for targeted behaviors","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:9602a24482a3227b197dc93d3537db92b029679c4f4b4c4ee496a687df726fa6","observation_id":"d6bba2e0-ad5c-4997-82e6-0e63a0c140cc","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Universal closed-box adversarial attack for trajectory representation via controlling high-dimensional iterative constraints.IEEE Internet of Things Journal (IoT-J), 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:918a8f4195f5a9be359606045b1ac70331d9e102c078acbd7fb2affecd57f220","observation_id":"c2b02963-d8a8-422e-aa28-af113f23d376","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"CleanCLIP: Mitigating data poisoning attacks in multimodal contrastive learning","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:8b2f91215e1488ed22c70b73561ffdc4199400666a6351684c1d519fc02160ca","observation_id":"79667f3b-b33d-459a-99fd-3cb88310a46c","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"The safety challenge of world models for embodied ai agents: A review.arXiv preprint arXiv:2510.05865, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:44e8dc5ee4ffdaf99d52430bb7ca6e6de0cf01d2795302c460a3dc2237372f5f","observation_id":"0e4979f0-36e6-4534-bb6f-8ec244d2e9bc","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"On minimizing adversarial counterfactual error","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:cb302f1957720723a2c137aacf9f1be8e22571eab6894fefab5804fd9eb4462b","observation_id":"9a137dec-315b-47dc-a3d2-9405a4e418a0","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Regret-based defense in adversarial reinforcement learning","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:77ba5345056c0a3f36b205d57f0073129390bf23f9240a29b57b64c75182e24f","observation_id":"0b719c0a-ec99-4a39-9df7-94e34658f3dd","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.20994","last_updated":"2026-04-22T18:32:38Z","snapshot_observed_at":"2026-08-02T13:22:16.885289Z","submitted_at":"2026-04-22T18:32:38Z","title":"Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.20994","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Kelleher","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2604.20994","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:b536728797e5ccb5e8b4551dc22dc263f1565e1b037ea864bee804f7499f76af","observation_id":"41f00fa2-d1d9-4298-8c44-af22c0bb7a4b","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"International ai safety report 2025: Second key update — technical safeguards and risk management.arXiv preprint arXiv:2511.19863, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:05c0096d6b0c3f3fc838e185f8e682a1e43aab6fb87471f42e654156b1082b0f","observation_id":"4d7c0755-abe0-4d3e-b9e2-ba813fc8206f","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.10328","last_updated":"2023-02-20T21:41:14Z","snapshot_observed_at":"2026-08-05T22:38:06.409078Z","submitted_at":"2023-02-20T21:41:14Z","title":"Hello Me, Meet the Real Me: Audio Deepfake Attacks on Voice Assistants","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.10328","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Hello me, meet the real me: Audio deepfake attacks on voice assistants.arXiv preprint arXiv:2302.10328, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2302.10328","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:4fdb0787fc97d54676019998124c85761062122c3c9965d2a2c399031a3c66b1","observation_id":"7a1bb945-88cd-4d5d-ae95-252d8c956a48","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2410.24164","last_updated":"2026-01-08T17:01:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2024-10-31T17:22:30Z","title":"$\\pi_0$: A Vision-Language-Action Flow Model for General Robot Control","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.24164","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2410.24164","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:ddd604247a3e660af6134fb60c853a2c9c657da87cb6cf10ea43dab42ceb1533","observation_id":"25c74575-ac32-46ab-90f7-a8c291ac8bb7","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Securing the lane: Defences against patch attacks on autonomous vehicle’s lane detection","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:f8f1f58885f4dc4c8c2b2b45d2b9ba09f7d9116e3154fa22482d06d51f956e88","observation_id":"d22b8089-740a-43d5-b076-056010af6a6a","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"The emergence of adversarial communication in multi-agent reinforcement learning","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:3f2c45935c787f4618a1270f8028888906c00a95caff318f5fa75102b60d8a77","observation_id":"a0cbaf86-d11c-4fc0-ab1c-0d34c8aa740f","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Stochastic model predictive control with a safety guarantee for automated driving.IEEE Transactions on Intelligent Vehicles, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:020f40ed127675e9e014b28dfe599e8d5b12b1f8dd7212e81f18a425e290c7c3","observation_id":"a96d0c0d-71e9-4dc4-b8b4-bf9dadc87a50","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Schoellig","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:9cefcf608e61784ca191407ba92faa2206389d9c2c517442d296e61f84bf4a35","observation_id":"03faf67c-47cb-41fe-9847-4b9a99787c9e","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:b69e5de54de5a775ff14977f4b5f3dad43bf909afaf52ff1c1b50f4fb2869628","observation_id":"0e389ac7-803b-4063-966d-e201d288678c","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Diffusion models-based purification for common corruptions on robust 3d object detection.Sensors, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:9c23cee69d9585d302605dbabcd8fddf72453597be8d995148da8303f7bd392f","observation_id":"e7a43be8-8667-49ad-8f7e-319ffca61573","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Summit: A simulator for urban driving in massive mixed traffic","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:9294a0c2d6fb4122283467ff31a19299afb1a15a6e2ca9beeacb135e5627d4c9","observation_id":"5b2a4dff-4daa-4428-aefd-1312540a0687","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.05418","last_updated":"2019-07-11T17:59:13Z","snapshot_observed_at":"2026-08-01T20:06:54.201030Z","submitted_at":"2019-07-11T17:59:13Z","title":"Adversarial Objects Against LiDAR-Based Autonomous Driving Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1907.05418","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Adversarial objects against lidar-based autonomous driving systems.arXiv preprint arXiv:1907.05418, 2019","venue":null,"work_id":null,"year":1907},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/1907.05418","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:bf1b1cf3dbac301a90def1867460c6bd77a53256d391bd0233e56cfe88ebcf8b","observation_id":"27ad799c-08b8-46a0-977e-1e538c7fa33f","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Invisible for both camera and lidar: Security of multi-sensor fusion based perception in autonomous driving under physical-world attacks","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:3cb9f17e9a336abc44513e180e9d8fc199cb811a15e3ba9502a0c52904baee77","observation_id":"eaa5cd61-4668-413c-ac06-e11e0aed094e","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Advdo: Realistic adversarial attacks for trajectory prediction","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:94ff59cafd6159778711bc4a02b0b338986670382699a38cbb2a07d31b77d0e9","observation_id":"4630aa1f-7dac-483e-b508-9381debb4294","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Hidden voice commands","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:0233f17fc40740c083e6e2bdf47db6c0f7801729424338d825d9a839dfa0cdef","observation_id":"95566bbf-eeff-4bed-969f-ec0558d192da","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Jeyapratap, Kaidi Xu, and Lifeng Zhou","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:13a1190ee8cdf95de191bcce19f087117e2e1013041aefb2046473dcf9d5f691","observation_id":"6e41de10-e7e8-4a18-a36f-4c01eceb73ae","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Heal: An empirical study on hallucinations in embodied agents driven by large language models.arXiv preprint arXiv:2506.15065, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:3240ea557ad2a4701c6cb136b58d8bc365f23eac433771ee2b33b01c0ca3f352","observation_id":"ef91c7c9-770c-4354-8dd5-18c75e606fa0","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.19844","last_updated":"2026-04-21T11:27:30Z","snapshot_observed_at":"2026-07-31T22:27:19.281459Z","submitted_at":"2026-04-21T11:27:30Z","title":"If you're waiting for a sign... that might not be it! Mitigating Trust Boundary Confusion from Visual Injections on Vision-Language Agentic Systems","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.19844","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Kanhere, and Hammond Pearce","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2604.19844","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:7ca6286cdafc44392c6503caba59fb2101e78b33d5605bf9bd6f208392c3353f","observation_id":"5dcb48e6-dc9e-45bd-ab7e-b503f0c03196","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Adversarial attacks on monocular pose estimation","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:b4951c3ad124d5a059b82e1150d14c99fd0be8f3e0371bc02aa2134670199f6f","observation_id":"758c4f41-4481-4d4f-bf5d-19bdb646ea7c","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Adversary is on the road: Attacks on visual{SLAM} using unnoticeable adversarial patch","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:fe23d0f76b47f5333be1f4c6072cf2dc4845ca990a311ea4146e5ce40e40d7c4","observation_id":"6e56db13-55bc-40db-b631-808d012d7340","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Alemzadeh, and Xugui Zhou","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:cd8a987dd7cfa7443b16ef87abcab7ab3205ea6aba1bf729951640ecfe88bf8a","observation_id":"5a00ea9e-e7e8-4e49-b109-94446b3535b4","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Tex3D: Objects as attack surfaces via adversarial 3D textures for vision-language-action models.arXiv preprint arXiv:2604.01618, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:e44139cd4b83f418b0a495e280e4a44266f33f572a8a7b7852815429d8ea9f14","observation_id":"5cc80e3d-aa62-4549-9e81-522fd4541f5b","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Lidattack: Robust black-box attack on lidar-based object detection","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:bad6dd2b6e2c7dd1935380500a6018422c5c9d06897064addae7d22e2e980209","observation_id":"82e816bd-de5a-46b5-a5ee-ae7e88aa151d","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Towardsphysically-realizable adversarial attacks in embodied vision navigation","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:68abf9faaf0265d85f516440ce691c84c2857385d9d24c51875286084a0eae25","observation_id":"dc75f095-0770-49b3-978a-d9ce6cb2c2f1","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Safemind: Benchmarking and mitigating safety risks in embodied llm agents.arXiv preprint arXiv:2509.25885, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:1753b0c695dd004774a8ce197e6c94194bd93dafd9776805e5dc15e03d5444e0","observation_id":"2d3d2350-247c-4e62-8892-8bb4ac873651","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Shapeshifter: Robust physical adversarial attack on faster r-cnn object detector","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:e3778a5e4beaa5602742573463b8182a252c7a9b0f103de4fe4416ab363b9507","observation_id":"d646946e-ca6d-4dfa-970d-257212892d6e","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Metamorph: Injecting inaudible commands into over-the-air voice controlled systems","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:a3c38e75b6c0c6d06a18e76d22c65811830b5e387081bf099cbc9d4556a8b3aa","observation_id":"4e4cd320-a621-4a99-9a00-ad398484d9b7","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Catnips: Collision avoidance through neural implicit probabilistic scenes.IEEE Transactions on Robotics (T-RO), 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:e9b8fe75986ae691b36eb75b1ff1ae5c1bcb5b23dffdf6fa89cb5dcf3eb49ee4","observation_id":"3a9f97ba-9005-4ae4-bc4d-c039ffeda039","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2409.09868","last_updated":"2025-03-17T20:42:50Z","snapshot_observed_at":"2026-07-06T19:15:43.173430Z","submitted_at":"2024-09-15T21:25:18Z","title":"SAFER-Splat: A Control Barrier Function for Safe Navigation with Online Gaussian Splatting Maps","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.09868","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Safer-splat: A control barrier function for safe navigation with online gaussian splatting maps.arXiv preprint arXiv:2409.09868, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2409.09868","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:213768b12fe96b0f05bec1a8c2f3e348c1a67afd49209ac918bc210480a1ddc4","observation_id":"98162ca2-4780-4a3f-86b2-8d244f95cd2a","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Splat-nav: Safe real-time robot navigation in gaussian splatting maps.IEEE Transactions on Robotics (T-RO), 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:bb2f58d9303657df8a6e3a274682cf53c6108e41f1c82a1e8180b151c73793b2","observation_id":"2a1d62ae-5ebd-44dc-80be-a03f2ac189d7","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Metawave: Attackingmmwavesensingwithmeta-material-enhancedtags","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:e345c80633df9340776110bf1544506b0a8eb87d2466d6ac77fe4148b31763b3","observation_id":"e124d44a-2285-4c62-842f-d162b2142ba0","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Fouhey, and Joyce Chai","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:310f2f6110f5184f8816d26da2642b155ec6d70bd38777f8936efba58e442ce3","observation_id":"fc1089ea-c5cf-4d80-bc6a-7bf15bb3bbba","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Marnet: Backdoor attacks against cooperative multi-agent reinforcement learning.IEEE Transactions on Dependable and Secure Computing (TDSC), 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:7f413cc7282fb4fbec979a4be4c5148dc58fc60a47c145a75044a68d11a538eb","observation_id":"7011f80d-5af1-492a-b5d1-53b5c7819c12","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Diffusion policy attacker: Crafting adversarial attacks for diffusion- based policies","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:dd0127a9a6790519b1afc8e512ce317398488c808f329c0a33e4b3bae3864cf6","observation_id":"fe140c86-8ddd-4145-98a5-ac778255f2bf","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.11532","last_updated":"2025-05-23T17:44:21Z","snapshot_observed_at":"2026-08-04T04:04:04.265144Z","submitted_at":"2025-05-14T02:05:34Z","title":"Revisiting Adversarial Perception Attacks and Defense Methods on Autonomous Driving Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.11532","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Revisiting adversarial perception attacks and defense methods on autonomous driving systems","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2505.11532","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:8b0613387dcaaf420199967499842e4c206b9b99a27f4b94c8b53451b58007c0","observation_id":"cfe3fa2a-947d-4ceb-88da-a7bc22d5faf6","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Devil’s whisper: A general approach for physical adversarial attacks against commercial black-box speech recognition devices","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:24be2d750f1aa5f230458aa86f1a0bcfa68004d96679c95f7160ef4cf355c7c2","observation_id":"c54f00a5-7956-4953-a218-7415397c3f99","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:cc2c3d4fa50e14b2050190dc057c303686a641653c5b725ccf1c208bead68a5f","observation_id":"9f7794a7-1040-4feb-8cd8-bc0715ad5afe","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.04808","last_updated":"2026-05-06T11:59:48Z","snapshot_observed_at":"2026-07-06T23:17:33.252539Z","submitted_at":"2026-05-06T11:59:48Z","title":"DecodingTrust-Agent Platform (DTap): A Controllable and Interactive Red-Teaming Platform for AI Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.04808","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"DecodingTrust-Agent platform (DTap): A controllable and interactive red-teaming platform for AI agents","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2605.04808","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:b4a24d7c4d54cee3db39b544afb99813b8fbe2ec29888863e289d1f015959efc","observation_id":"e6b85c10-3ca5-4cd9-b067-75352c5cbc56","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2604.12447","last_updated":"2026-04-14T08:32:02Z","snapshot_observed_at":"2026-07-06T23:00:37.144068Z","submitted_at":"2026-04-14T08:32:02Z","title":"HazardArena: Evaluating Semantic Safety in Vision-Language-Action Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2604.12447","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"HazardArena: Evaluating semantic safety in vision-language-action models.arXiv preprint arXiv:2604.12447, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":59,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2604.12447","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:fc4248d7e2f4abb6a7eb43936a3d9e16c92cc6f0a143e119f4036f98595bc44d","observation_id":"58a040f3-1a22-4412-a6d7-30dba030aa64","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Manipulation facing threats: Evaluating physical vulnerabilities in end-to-end vision language action models.arXiv preprint arXiv:2409.13174, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":60,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:7a9f20fef977afa3beca75a00bff105d7209ca1824251b648a8439855b0dc313","observation_id":"71584592-313e-4b69-93b3-27713a6a9db2","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Universal adversarial attack against 3d object tracking","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":61,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:9bc2ac78367855063d13ef02da56c73e1755e4ebef84086dbfc73d9bdca820d2","observation_id":"69c5a7c5-e289-46cf-bdb4-2d1e53b60d97","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Black-box explainability-guided adversarial attack for 3d object tracking.IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":62,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:9a0f2e09bf308fbd334396c335f8db50d0e4314f7e6979ba6702a03fcb095712","observation_id":"678d1c7a-3f39-477f-8621-e12919c558e9","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Physical attack on monocular depth estimation with optimal adversarial patches","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":63,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:df3a886958721a48a7aab566fe2160649e23938c98c7885af49788baa4d91179","observation_id":"bb4b3920-e727-44a4-9589-1afba20a3797","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.14504","last_updated":"2023-10-23T02:31:31Z","snapshot_observed_at":"2026-07-06T16:36:51.110530Z","submitted_at":"2023-10-23T02:31:31Z","title":"ADoPT: LiDAR Spoofing Attack Detection Based on Point-Level Temporal Consistency","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.14504","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Adopt: Lidar spoofing attack detection based on point-level temporal consistency.arXiv preprint arXiv:2310.14504, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":64,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2310.14504","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:33fa5e6f879ad92045bb75e31a4b2feb68bf053686b93dce939c509f06b9c87c","observation_id":"c31b3345-1958-47f4-831d-5d988eab8885","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Sentinet: Detecting localized universal attacks against deep learning systems","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":65,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:a41e1c7e654dc6b1696170483bfd1a96de1d3527efca1b26a781e5cb117d401c","observation_id":"2e7c7d5f-2b71-4870-87ca-870344188008","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Gupta, Mykel J","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":66,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:b96f655804984e3eabf1f44207b409f39b64f514960d861e26212ea5b41bce88","observation_id":"9e2730a3-905a-4c1a-96a7-24606cd59049","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Handover control for human-robot and robot-robot collaboration.Frontiers in Robotics and AI, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":67,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:415e7479612fa263429611b02a193ca8e8a15c3193835850088f316440c059d8","observation_id":"f9b855b1-68f8-4cf8-b21b-674fb688ffcf","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Pybullet, a python module for physics simulation for games, robotics and machine learning.http://pybullet.org, 2016–2021","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":68,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:42b300db6cec0491ad75d5c3224be5dbf206d68479b86df471b6c0149dd5f76e","observation_id":"167d61be-9a20-4063-892e-2ff5420716bf","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.01394","last_updated":"2024-01-02T17:36:07Z","snapshot_observed_at":"2026-07-06T17:10:56.398607Z","submitted_at":"2024-01-02T17:36:07Z","title":"Unveiling the Stealthy Threat: Analyzing Slow Drift GPS Spoofing Attacks for Autonomous Vehicles in Urban Environments and Enabling the Resilience","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.01394","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":null,"venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":69,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2401.01394","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:3183e43909a24d1e4713d3ce3d65e3471d065e0f5372d7a499efa6280ef5fda6","observation_id":"7234eee3-3b92-4538-a584-3786325a2593","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Navsim: Data-drivennon-reactiveautonomousvehiclesimulation and benchmarking","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":70,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:1e2339fde8313afde8a199b7fcbee16170474b40c264741f8a0316d25d25379a","observation_id":"7e59aaba-bdc2-44b9-af3e-163e8305fae4","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2505.02077","last_updated":"2026-04-29T10:47:33Z","snapshot_observed_at":"2026-07-06T21:18:41.237217Z","submitted_at":"2025-05-04T12:03:29Z","title":"Open Challenges in Multi-Agent Security: Towards Secure Systems of Interacting AI Agents","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2505.02077","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Open challenges in multi-agent security: Towards secure systems of interacting ai","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":71,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2505.02077","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:6ea1b000acfdc8935ff58464e760d2c7a005324683de2949699f59e91caa2101","observation_id":"7fd5d916-5edf-441d-8741-595344f4a076","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Ai agents under threat: A survey of key security challenges and future pathways.ACM Computing Surveys, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":72,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:fd4d8a4542d61a6f27282b0edffe1f32ec2f7360303ca86a9771119e579c674f","observation_id":"68911bbe-3cb3-467e-81ad-de390d30e705","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Learning to collide: An adaptive safety-critical scenarios generating method","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":73,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:5f99fea34555fe481b39633329044f600fa1832e1c329b880d2365bc2f81fe5c","observation_id":"da983f2f-3592-46c2-b5b2-603697299cbb","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Doan, Yingjie Lao, Peng Yang, and Ping Li","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":74,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:5f84787ce75290da7a0dfe92966d672de55dfc609025950038098351682b29a8","observation_id":"78916a14-065d-4d69-888f-c153e4e0ed5a","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Viewfool: Evaluating the robustness of visual recognition to adversarial viewpoints","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":75,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:031eea5245f74821ffa0c221f0a84b9be615ec3357cc3e44ce3d601da4425fb8","observation_id":"fd082a4f-d8e2-44ee-abcc-c09f3639b7f1","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Carla: An open urban driving simulator","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":76,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:516b9c42455e60b003739a18e30632a8bfb9c13ddd35f8e50f83da5392269bbf","observation_id":"aca29f42-57c1-4ba9-a504-8c56e780c08f","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Human–robot object handover: Recent progress and future direction.Robotics, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":77,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:8413a5c15f96f5d98eedd98d1f9e77a625e80867242e944a99963769d41335b4","observation_id":"5c767250-2703-47c8-a227-857ca5014c24","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.01950","last_updated":"2026-05-03T16:19:45Z","snapshot_observed_at":"2026-07-06T23:15:07.159340Z","submitted_at":"2026-05-03T16:19:45Z","title":"TRAP: Tail-aware Ranking Attack for World-Model Planning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.01950","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"TRAP: Tail-aware ranking attack for world-model planning.arXiv preprint arXiv:2605.01950, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":78,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2605.01950","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:015d7b006c2a92b7ca96565b33d4d444c9003a5b8e8eac1a98591a0c998364a4","observation_id":"602b054e-2d02-4e57-bcce-77dde3ecd453","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"A robust multi-sensor fusion model against adversarial patch attack.Wireless Networks, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":79,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:d9300342495f4a35386d61a439d3a13fb2cbfd8087541c79ce6f4b6fb0463e96","observation_id":"5a9d0ca3-c15c-4bde-b007-37ffcee448f7","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Drones in distress: A game-theoretic countermeasure for protecting uavs against gps spoofing.IEEE Internet of Things Journal (IoT-J), 2019","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":80,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:f52ef74cb2b9ee50882369ef3a19b198fa9919a87923619df406099a2376f0f3","observation_id":"3f9b5485-f63b-40c2-906d-85ceb794a2f8","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Robust physical-world attacks on deep learning visual classification","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":81,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:5bd368381a095081d1279cc24569eebefffc938c1a79aca2520182119e4d446f","observation_id":"ebc8b687-5da7-4549-992c-7ca5e03742b7","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Adualantennagnssspoofingdetectorbasedonthedispersion of double difference measurements","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":82,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:ff1a7d98779736357712eef1ef04f12c97c572fb34daa2e379b5838a4760be9f","observation_id":"042a58f3-0e26-4e62-a8eb-53802afccaf4","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Adversarial attack on trajectory prediction for autonomous vehicles with generative adversarial networks","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":83,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:f9abcf5d291c5d25ea0e77e8dd66ed793dac6b37e65832df96cde10af6035513","observation_id":"7688329a-e0ed-492b-b2b3-69c2ae12dc1c","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2605.05017","last_updated":"2026-05-06T15:16:05Z","snapshot_observed_at":"2026-07-06T23:17:43.402046Z","submitted_at":"2026-05-06T15:16:05Z","title":"Position: Embodied AI Requires a Privacy-Utility Trade-off","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2605.05017","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Position: Embodied AI requires a privacy-utility trade-off.arXiv preprint arXiv:2605.05017, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":84,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2605.05017","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:b97a6eb49f2e2ed4d594014393a54623f2ef98fdf3e504ead784ee8b9f64b49c","observation_id":"674fdbc2-f3e9-4d5b-900c-c0e9a47fc60e","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Pso-based black-box lane detection adversarial attack","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":85,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:f5ef7ea54bd1ed7329164ddd9caea72f2bef4513ba743772bf4a1f5c33168bf1","observation_id":"59a1cfb6-8d4e-455a-900e-518933166d4d","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2508.07407","last_updated":"2025-08-31T14:55:05Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2025-08-10T16:07:32Z","title":"A Comprehensive Survey of Self-Evolving AI Agents: A New Paradigm Bridging Foundation Models and Lifelong Agentic Systems","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2508.07407","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"A comprehensive survey of self-evolving AI agents: A new paradigm bridging foundation models and lifelong agentic systems.arXiv preprint arXiv:2508.07407, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":86,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2508.07407","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:30578752e5625f4a0e6d72904262a558ef7cecc03ecba6edb4a92d809e6b61c0","observation_id":"eb13064b-0201-4f5d-bf92-ca6041b3bfe8","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2510.13626","last_updated":"2025-12-26T12:19:56Z","snapshot_observed_at":"2026-07-06T22:32:42.381894Z","submitted_at":"2025-10-15T14:51:36Z","title":"LIBERO-Plus: In-depth Robustness Analysis of Vision-Language-Action Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2510.13626","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Libero-plus: In-depth robustness analysis of vision-language-action models.arXiv preprint arXiv:2510.13626, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":87,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2510.13626","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:b8e5009b111b6ac8891a79a9a15b50e322d7d5bb58345f8d123935697b129809","observation_id":"189075af-3422-4a77-b702-7a361582cfae","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"DECREE: Detecting backdoors in pre-trained encoders","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":88,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:53ea422ce545b3f98ad900746dfbe96a7539586cb0df26ce40ad1de8f151a9da","observation_id":"046702aa-bd19-4192-97e4-6c6bc9272a7c","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Intelligent driving intelligence test for autonomous vehicles with naturalistic and adversarial environment.Nature Communications, 2021","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":89,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:b9dfa6e30207cf251e69d6c0de615cdee21b76a36ad8cfc6e18463cc60a627e7","observation_id":"45cd9bbd-b0de-44ea-859f-4e945b680ac7","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Backdooragent: A unified framework for backdoor attacks on llm-based agents.arXiv preprint arXiv:2601.04566, 2026","venue":null,"work_id":null,"year":2026},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":90,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:6b642ec9548e924cebda8fb65287c2bd4d47eca5efd668e00149d44c458e574d","observation_id":"4d41a9f0-8858-4ee5-b0c2-5dddab6483a0","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"A navigation message authentication proposal for the galileo open service.NAVIGATION: Journal of the Institute of Navigation, 2016","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":91,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:0cc962c54a0168b0ff38939e32bdedbb540f82ac5ce1593839c0dea865272322","observation_id":"6fae9d48-6002-4fd4-bced-f2c437abe317","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Compliantblindhandovercontrolforhuman-robotcollaboration","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":92,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:7cd829ebd54fc145b08c91d64e11bf9c48606b60a5919a61aef5d5c24f2d9917","observation_id":"9d39c6f1-c7db-40ec-b9f2-118aa12b2541","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":null,"venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":93,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:74149790f39c86b8bdf3e32a002c4e8350e245d324075479125b350fe95f9ee4","observation_id":"4fac8b2d-1a83-4aac-8be9-5ad9b12da815","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Scenic: a language for scenario specification and data generation.Machine Learning (MLJ), 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":94,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:6ce8512ea5b74ced961179110eb31416a42b2e1e615c4bcdb412df15f6816974","observation_id":"7dc40168-333e-41e9-8d65-0a6853a1cde6","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Random spoofing attack against lidar-based scan matching slam","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":95,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:9ae6f85a29b608f9c3b3ff17982c5a4086c037bb302c82a6b91f9a7bb2fb2f58","observation_id":"2fb2f3b1-11d6-48ef-a598-b8c8d4014a5f","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"2025 ai safety index.https://futureoflife.org/ai-safety-index-summer-2 025/, 2025","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":96,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:ecfe053462c1c220231f1683286ce60959602fe00e2a6c3615cf9a1df253eb86","observation_id":"db385e75-44a1-47be-a38d-d99d64c3542b","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Bring your own (non-robust) algorithm to solve robust mdps by estimating the worst kernel","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":97,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:73004b00c62d8ebc4db72c29ce48a61fcd34984cd9524d25249f6b9082bee633","observation_id":"7dab03ef-76a2-4183-b129-b324921a3f6f","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.09523","last_updated":"2024-11-14T15:40:04Z","snapshot_observed_at":"2026-08-05T11:33:06.319013Z","submitted_at":"2024-11-14T15:40:04Z","title":"Navigating the Risks: A Survey of Security, Privacy, and Ethics Threats in LLM-Based Agents","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.09523","snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Navigatingtherisks: Asurveyofsecurity,privacy,andethicsthreatsinllm-basedagents.arXivpreprint arXiv:2411.09523, 2024","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":98,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"cited_paper":"/paper/2411.09523","citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:f835ed78265afde476b507c03fad01b81fad3e8492dcfeb04996c45a030c167a","observation_id":"f7472c65-bf2e-4511-8e26-8ceddbe65ecb","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":null,"venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":99,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:f99dfb6212d71623ad568dc2291ae09bbe4f451c5dbb0ca13f691abbcf99ab58","observation_id":"18928ca0-2ce0-47ed-80a3-8c24056bf466","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-07-13T17:08:58.831798Z","title":"Exploring practical acoustic transduction attacks on inertial sensors in mdof systems.IEEE Transactions on Mobile Computing, 2023","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses","version":2},"reference_index":100,"source":"pdf_text","source_observed_at":"2026-07-13T17:08:58.831798Z"},"links":{"citing_paper":"/paper/2605.02900"},"observation_digest":"sha256:52b510e2a09f660d5bf205fab427af94fe25da237580ece2e76691720b6d07ac","observation_id":"cd31e9b9-8321-4279-971c-73641f1f3a25","resolution":{"observed_at":"2026-07-13T17:08:58.831798Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2605.02900","last_updated":"2026-05-24T13:33:45Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-02T11:46:07.449862Z","submitted_at":"2026-03-28T13:21:44Z","title":"Safety in Embodied AI: A Survey of Risks, Attacks, and Defenses"},"reference_resolution":{"displayed":100,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":100,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":299},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-06T06:34:29.942622+00:00","source":"crossref"},{"observed_at":"2026-08-06T06:34:23.284952+00:00","source":"retraction_watch"}],"thesis":"As of 6 August 2026, this Paper Citation Record lists 100 of 299 outbound references and 2 inbound Pith citation observations for arXiv:2605.02900."}