{"as_of":"2026-08-18T13:52:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:acda5055988368350813e7a19655d15259b42c6f820dc95ab8242be70bcef79e","coverage":[{"denominator":39,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":39,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-15T17:06:09.003652Z","state":"measured"},{"denominator":39,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":39,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-18T06:34:40.430872+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"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":[],"links":{"evidence":"/evidence","html":"/paper/2508.17674/citation-record","integrity":"/paper/2508.17674/integrity","json":"/paper/2508.17674/citation-record.json","paper":"/paper/2508.17674"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-15T17:06:08.725427Z","title":"Language models are few-shot learners,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.725427Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:21fdcc166a84757c71049acedaa25da47f1d9b142db5b266c8384e38fabf9620","observation_id":"a2b61f2e-dd63-456d-8654-e60897578326","resolution":{"observed_at":"2026-08-15T17:06:08.725427Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2108.07258","last_updated":"2022-07-12T23:45:14Z","snapshot_observed_at":"2026-08-02T09:20:40.804790Z","submitted_at":"2021-08-16T17:50:08Z","title":"On the Opportunities and Risks of Foundation Models","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2108.07258","snapshot_observed_at":"2026-08-15T17:06:08.732532Z","title":"On the opportunities and risks of foundation models,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.732532Z"},"links":{"cited_paper":"/paper/2108.07258","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:fc16d261b1ba0bb9fc33ad65edee332fd9e38906fb7bb19379f3254b38eba896","observation_id":"b2325057-d3f5-47d9-880b-ee806dfbda5f","resolution":{"observed_at":"2026-08-15T17:06:08.732532Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2303.12712","last_updated":"2023-04-13T20:41:31Z","snapshot_observed_at":"2026-08-03T04:49:15.195814Z","submitted_at":"2023-03-22T16:51:28Z","title":"Sparks of Artificial General Intelligence: Early experiments with GPT-4","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.12712","snapshot_observed_at":"2026-08-15T17:06:08.738933Z","title":"Sparks of artificial general intelligence: Early experiments with gpt-4,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.738933Z"},"links":{"cited_paper":"/paper/2303.12712","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:2abf6db45ec71ac40bc09c77312a02b1c7e13eac7cfd723b0dcfa2c8e5de4346","observation_id":"7640f49f-2074-4465-bca2-aba9d6e1a756","resolution":{"observed_at":"2026-08-15T17:06:08.738933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2302.13971","last_updated":"2023-02-27T17:11:15Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-02-27T17:11:15Z","title":"LLaMA: Open and Efficient Foundation Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2302.13971","snapshot_observed_at":"2026-08-15T17:06:08.747077Z","title":"Llama: Open and efficient foundation language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.747077Z"},"links":{"cited_paper":"/paper/2302.13971","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:7dba50e1beff06de0560cfcda1c0b29cda232069a6db8467bba2655e72c07c11","observation_id":"26080ef8-3fb1-42a5-82c9-f4b2ef05a622","resolution":{"observed_at":"2026-08-15T17:06:08.747077Z","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":"2026-08-15T17:06:08.753632Z","title":"Soull- mate: An application enhancing diverse mental health support with adaptive llms, prompt engineering, and rag techniques,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.753632Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:cde62311b87cfdda8f3e5e3bc5f30522f5b4e7b50cbf979cc72c875f47fe1fd2","observation_id":"7788d717-67c2-4ed9-8674-7066cac99a7f","resolution":{"observed_at":"2026-08-15T17:06:08.753632Z","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":"2026-08-15T17:06:08.759569Z","title":"Soullmate: An adaptive llm-driven system for advanced mental health support and assessment, based on a systematic application survey,","venue":null,"work_id":null,"year":null},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.759569Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:484a3eca1f32bf1fa6626462b2191c6a65a66c98729f84bbd54ffb2564842a93","observation_id":"057784fb-9d1d-478e-bc3f-44eeb6c8a15b","resolution":{"observed_at":"2026-08-15T17:06:08.759569Z","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":"2026-08-15T17:06:08.765653Z","title":"A layered multi-expert framework for long-context mental health assessments,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.765653Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:2bbbc094cb682e01d8a6b177d7efe2b2988b0b24c34280505af3838c50ce057b","observation_id":"a7d41ebb-cbe8-43ee-aa2b-3cc33bb46ee0","resolution":{"observed_at":"2026-08-15T17:06:08.765653Z","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":"2026-08-15T17:06:08.772645Z","title":"Advancing mental health pre-screening: A new custom gpt for psychological distress assessment,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.772645Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:a1f5dbcb97844a79782b1aec7bbe1a467ac7dd51679f7f1dfbc14639e86255a8","observation_id":"11977003-975c-449f-9b20-f275c6605097","resolution":{"observed_at":"2026-08-15T17:06:08.772645Z","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":"2026-08-15T17:06:08.781389Z","title":"Chatdoctor: A medical chat model fine-tuned on a large language model meta-ai (llama) using medical domain knowledge,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.781389Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:fedd637c835017d006fdb84d0c8ece5faed11df0ee99344ce438d160ff3b83c9","observation_id":"b8b2126a-001f-4279-bede-a570a1d349b5","resolution":{"observed_at":"2026-08-15T17:06:08.781389Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.15764","last_updated":"2024-11-24T09:24:04Z","snapshot_observed_at":"2026-08-15T23:42:53.972346Z","submitted_at":"2024-11-24T09:24:04Z","title":"LLM Online Spatial-temporal Signal Reconstruction Under Noise","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.15764","snapshot_observed_at":"2026-08-15T17:06:08.787070Z","title":"Llm online spatial-temporal signal reconstruction under noise,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.787070Z"},"links":{"cited_paper":"/paper/2411.15764","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:93ae82d7cb418849b7ab76249717109745806165bc09fa597bd2066bcdf17574","observation_id":"27f7bc2f-e47c-4ee0-a128-eb6d52b936ac","resolution":{"observed_at":"2026-08-15T17:06:08.787070Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-12T17:13:46.394331Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-15T17:06:08.796083Z","title":"Explaining and harnessing adversarial examples,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.796083Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:7ad51d9159f28986db22b2b0e756266378d382225547567d4121c3f44276ddb8","observation_id":"79e0d6d7-e99f-4936-a9b1-a713e8fac8d4","resolution":{"observed_at":"2026-08-15T17:06:08.796083Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1708.06733","last_updated":"2019-03-11T20:45:33Z","snapshot_observed_at":"2026-08-12T15:43:59.037380Z","submitted_at":"2017-08-22T17:31:54Z","title":"BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1708.06733","snapshot_observed_at":"2026-08-15T17:06:08.803161Z","title":"Badnets: Identifying vulnera- bilities in the machine learning model supply chain,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.803161Z"},"links":{"cited_paper":"/paper/1708.06733","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:afda4641b4c76a21904c830539fa3e72b365aedc0f05f7624d07533a18e7fb7b","observation_id":"471c9fd8-aeae-49e3-bfa0-9889119d101e","resolution":{"observed_at":"2026-08-15T17:06:08.803161Z","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":"2026-08-15T17:06:08.811352Z","title":"Membership inference attacks against machine learning models,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.811352Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:4bcb4b6c690e14a28243a8caf2e5a5d0ce3b0207e3f18db21ef813b366111e7e","observation_id":"41b84694-01aa-4414-9a25-5a1ff15ab8bc","resolution":{"observed_at":"2026-08-15T17:06:08.811352Z","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":"2026-08-15T17:06:08.817686Z","title":"Stealing machine learning models via prediction APIs,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.817686Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:fa04070e9d10ddc8972fca9503d48aacfa1125f9df1919c2d89425cff935c9e3","observation_id":"0c298087-073b-4df3-9cb9-cf1f38017d33","resolution":{"observed_at":"2026-08-15T17:06:08.817686Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.06660","last_updated":"2020-04-14T16:51:42Z","snapshot_observed_at":"2026-08-09T15:24:12.006566Z","submitted_at":"2020-04-14T16:51:42Z","title":"Weight Poisoning Attacks on Pre-trained Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.06660","snapshot_observed_at":"2026-08-15T17:06:08.823092Z","title":"Weight poisoning attacks on pre-trained models,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.823092Z"},"links":{"cited_paper":"/paper/2004.06660","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:06945bfc7e78144d7d6288ea4fa861d2ef015087e1b0bc6579ea99585f90db4e","observation_id":"02011c41-4532-45fa-9e94-3d3073c99147","resolution":{"observed_at":"2026-08-15T17:06:08.823092Z","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":"2026-08-15T17:06:08.831362Z","title":"Huynh and J","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.831362Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:9dbc074f5ecc870539bdcc5add0056d5e413f9c4645d0d9c1ecf4ff99cdfe449","observation_id":"b80cd584-0c40-48be-a578-a78e8c036c51","resolution":{"observed_at":"2026-08-15T17:06:08.831362Z","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":"2026-08-15T17:06:08.839934Z","title":"Poisonprompt: Backdoor attack on prompt- based large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.839934Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:55471be60382b449912510d0692ac6213a8e1ca39018f780d599ba3e914a2ae5","observation_id":"54e9b452-bd43-4a2f-afa7-80b0194f7eaf","resolution":{"observed_at":"2026-08-15T17:06:08.839934Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.05566","last_updated":"2024-01-17T20:26:01Z","snapshot_observed_at":"2026-08-15T14:10:05.296241Z","submitted_at":"2024-01-10T22:14:35Z","title":"Sleeper Agents: Training Deceptive LLMs that Persist Through Safety Training","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.05566","snapshot_observed_at":"2026-08-15T17:06:08.848335Z","title":"Sleeper agents: Training deceptive llms that persist through safety training,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.848335Z"},"links":{"cited_paper":"/paper/2401.05566","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:5cb9c999b49891364d1eb55379d2d1e76aa2483d05d57dc0f0b6f324ceabec64","observation_id":"f2497ce0-96b9-4101-872d-3b0787379db7","resolution":{"observed_at":"2026-08-15T17:06:08.848335Z","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":"2026-08-15T17:06:08.855694Z","title":"Membership inference attacks on machine learning: a survey,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.855694Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:786cfec6eae50fc4eb9039bbb34afea222010fb202f713f3ff80fdde907463ef","observation_id":"60d01c43-bdfe-40b7-8994-bf2d71875010","resolution":{"observed_at":"2026-08-15T17:06:08.855694Z","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":"2026-08-15T17:06:08.861466Z","title":"A survey on membership inference attacks and defenses in machine learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.861466Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:1628fdd1cddcdae70881fbe8264239682b92d48ab11eb7659bfd2fda818fae2c","observation_id":"aa11fcdc-3776-460e-a9da-09124201d424","resolution":{"observed_at":"2026-08-15T17:06:08.861466Z","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":"2026-08-15T17:06:08.868145Z","title":"I know what you trained last summer: A survey on stealing machine learning models and defences,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.868145Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:95ba067aa27e904660157c995d058aaa91732b62d2c0638c9ba1e35deaad38dd","observation_id":"ac7d38ae-00e1-4fa2-94d1-5596edac40c9","resolution":{"observed_at":"2026-08-15T17:06:08.868145Z","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":"2026-08-15T17:06:08.879574Z","title":"Sok: All you need to know about on-device ml model extraction-the gap between research and practice,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.879574Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:c9345b7731f60eaef4d376a65fcea446807222a4880c0eb1daaa6a9305736f20","observation_id":"ca336346-cb25-49e4-bb9e-38cf699380f6","resolution":{"observed_at":"2026-08-15T17:06:08.879574Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1312.6199","last_updated":"2014-02-19T16:33:14Z","snapshot_observed_at":"2026-08-15T16:41:15.505782Z","submitted_at":"2013-12-21T03:36:08Z","title":"Intriguing properties of neural networks","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1312.6199","snapshot_observed_at":"2026-08-15T17:06:08.888145Z","title":"Intriguing properties of neural networks,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.888145Z"},"links":{"cited_paper":"/paper/1312.6199","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:f3917912af6a2a187958ba238bc671fa356af9553346d55f5ca97d6728884cf1","observation_id":"4726218e-7e4e-4f88-80d3-af42f1adf87b","resolution":{"observed_at":"2026-08-15T17:06:08.888145Z","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":"2026-08-15T17:06:08.896392Z","title":"Robust physical-world attacks on deep learning visual classification,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.896392Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:f2dd007fd9a9fa51ffceec4e3d75741cd93f7bf5ec631d33d3ae73e679c688f0","observation_id":"a9369774-23a7-4342-972c-331384011a36","resolution":{"observed_at":"2026-08-15T17:06:08.896392Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1802.06430","last_updated":"2018-05-31T20:05:27Z","snapshot_observed_at":"2026-08-14T19:44:46.968870Z","submitted_at":"2018-02-18T19:39:28Z","title":"DARTS: Deceiving Autonomous Cars with Toxic Signs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.06430","snapshot_observed_at":"2026-08-15T17:06:08.906325Z","title":"Darts: Deceiving autonomous cars with toxic signs,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.906325Z"},"links":{"cited_paper":"/paper/1802.06430","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:d6f9dbdfa9af54555ce5155516131f0230b44463a18b50418a8e055de5b1d156","observation_id":"bd597678-dc29-45f0-bc7e-4dba55b085b3","resolution":{"observed_at":"2026-08-15T17:06:08.906325Z","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":"2026-08-15T17:06:08.913566Z","title":"Trojaning attack on neural networks,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.913566Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:081c3512fc31f51ea634c179fcf47b579c9999ab50129945ad397211b129d4eb","observation_id":"fbac02a9-e894-473c-ada8-d9615a21b212","resolution":{"observed_at":"2026-08-15T17:06:08.913566Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05526","last_updated":"2017-12-15T04:26:26Z","snapshot_observed_at":"2026-07-06T06:14:30.795326Z","submitted_at":"2017-12-15T04:26:26Z","title":"Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1712.05526","snapshot_observed_at":"2026-08-15T17:06:08.919800Z","title":"Targeted backdoor attacks on deep learning systems using data poisoning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.919800Z"},"links":{"cited_paper":"/paper/1712.05526","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:b5f2db8c8ec34e54ed94566136f088252da3fc43e7a5ecb9a8c2967357f827d3","observation_id":"38808ae0-148b-42df-887a-35fd2633a4d1","resolution":{"observed_at":"2026-08-15T17:06:08.919800Z","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":"2026-08-15T17:06:08.927844Z","title":"The secret sharer: Evaluating and testing unintended memorization in neural networks,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.927844Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:1959a5064ae1644b2fcec46ef0ab5163934f233a25dd3a8c3e5f3914a002e6c9","observation_id":"74a9ac8b-61b9-410d-af7f-7c8f72acacc5","resolution":{"observed_at":"2026-08-15T17:06:08.927844Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2311.17035","last_updated":"2023-11-28T18:47:03Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2023-11-28T18:47:03Z","title":"Scalable Extraction of Training Data from (Production) Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.17035","snapshot_observed_at":"2026-08-15T17:06:08.934584Z","title":"Scalable extraction of training data from (production) language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.934584Z"},"links":{"cited_paper":"/paper/2311.17035","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:2d865daae98af9551b8996e6819330f99141bb9facbc8bac9d53fc7c50a77e59","observation_id":"e77e7502-615e-473e-98c3-9d78ad8a0762","resolution":{"observed_at":"2026-08-15T17:06:08.934584Z","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":"2026-08-15T17:06:08.941514Z","title":"A survey on evaluation of large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.941514Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:0317380f42743a321d95ab6fd06d9f8191ec6a813d1d7490f68e329bd857ea6a","observation_id":"a9bb2a80-dec6-4688-8cca-ebf2baae0950","resolution":{"observed_at":"2026-08-15T17:06:08.941514Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2307.15043","last_updated":"2023-12-20T20:48:57Z","snapshot_observed_at":"2026-08-12T09:06:50.363435Z","submitted_at":"2023-07-27T17:49:12Z","title":"Universal and Transferable Adversarial Attacks on Aligned Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.15043","snapshot_observed_at":"2026-08-15T17:06:08.949106Z","title":"Universal and transferable adversarial attacks on aligned language models,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.949106Z"},"links":{"cited_paper":"/paper/2307.15043","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:b4f5daa56cdcb06213c03e98743752fdf4d795cf771750c423f5a490e4cfbd64","observation_id":"0ea083d4-6141-44e2-8203-878395e43fb3","resolution":{"observed_at":"2026-08-15T17:06:08.949106Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2004.08994","last_updated":"2020-04-29T21:16:31Z","snapshot_observed_at":"2026-07-06T09:13:32.098523Z","submitted_at":"2020-04-20T00:07:18Z","title":"Adversarial Training for Large Neural Language Models","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2004.08994","snapshot_observed_at":"2026-08-15T17:06:08.957836Z","title":"Adversarial training for large neural language models,","venue":null,"work_id":null,"year":2004},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.957836Z"},"links":{"cited_paper":"/paper/2004.08994","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:3c1a3a39d169fedac9d79e9642171d6cbe25e1c4753ea948400d62799ed67c7d","observation_id":"21fd57f3-5298-476c-bb37-fa29b200f333","resolution":{"observed_at":"2026-08-15T17:06:08.957836Z","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":"2026-08-15T17:06:08.963710Z","title":"Jailbroken: How does llm safety training fail?","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.963710Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:27d5ba752f36d3d27140b945d1cae9724be7531bc7e6a26369171196ea8c0857","observation_id":"bcfcc478-0126-4475-9449-61e7a215df19","resolution":{"observed_at":"2026-08-15T17:06:08.963710Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2305.13860","last_updated":"2024-03-10T13:58:08Z","snapshot_observed_at":"2026-07-06T15:31:18.144952Z","submitted_at":"2023-05-23T09:33:38Z","title":"Jailbreaking ChatGPT via Prompt Engineering: An Empirical Study","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2305.13860","snapshot_observed_at":"2026-08-15T17:06:08.971742Z","title":"Jailbreaking chatgpt via prompt engineering: An empirical study,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.971742Z"},"links":{"cited_paper":"/paper/2305.13860","citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:bce9857a2ba3d9a6ce3a553c5d56e6f9f67f9ff9792337efae3ac6b20ed92593","observation_id":"87ffcd33-4a32-4053-be3c-df9d34ca7bd5","resolution":{"observed_at":"2026-08-15T17:06:08.971742Z","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":"2026-08-15T17:06:08.977940Z","title":"Not what you’ve signed up for: Compromising real-world llm-integrated applications with indirect prompt injection,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.977940Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:e864c7c73179c8e231b465d61baa21c55331045c4b31968509ce7b3b3ada55f6","observation_id":"0ba70b2d-b6bc-498c-a350-a75fc477a1c2","resolution":{"observed_at":"2026-08-15T17:06:08.977940Z","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":"2026-08-15T17:06:08.983134Z","title":"Online display ad- vertising markets: A literature review and future directions,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.983134Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:ddb9b377c14091508cb3ad599d198be5a59881a25142cc1437a1237622bb57ca","observation_id":"3088c257-1149-40af-93a6-4d66a4c75fb4","resolution":{"observed_at":"2026-08-15T17:06:08.983134Z","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":"2026-08-15T17:06:08.988850Z","title":"The dark alleys of madison avenue: Understanding malicious advertisements,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.988850Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:dcfb01100255890ccaf81765317dfc8c5c33ba495ab765243bcd1753ff32ffbd","observation_id":"54ffbcd3-3ba9-45c9-8994-228470822c66","resolution":{"observed_at":"2026-08-15T17:06:08.988850Z","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":"2026-08-15T17:06:08.994366Z","title":"How to backdoor federated learning,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:08.994366Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:d4e2305439627785bc33a298f031c501c7d560cacdb062d8a7643fb8203a547c","observation_id":"92c4b772-f5af-4961-b565-20ad6b345983","resolution":{"observed_at":"2026-08-15T17:06:08.994366Z","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":"2026-08-15T17:06:09.003652Z","title":"Badnl: Backdoor attacks against nlp models with semantic- preserving improvements,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-15T17:06:09.003652Z"},"links":{"citing_paper":"/paper/2508.17674"},"observation_digest":"sha256:7d610f5f9077e88ad8992f7f1d2402127676a188f874e01ec3e99adcb5337a54","observation_id":"b6e7e95c-4594-4bc2-b73f-d587272028bc","resolution":{"observed_at":"2026-08-15T17:06:09.003652Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2508.17674","last_updated":"2025-09-08T18:05:43Z","latest_version":2,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-17T06:19:19.902288Z","submitted_at":"2025-08-25T05:13:23Z","title":"Attacking LLMs and AI Agents: Advertisement Embedding Attacks Against Large Language Models"},"reference_resolution":{"displayed":39,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":39,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":39},"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":"2026-08-18T06:34:40.430872+00:00","source":"crossref"},{"observed_at":"2026-08-18T06:34:34.496301+00:00","source":"retraction_watch"}],"thesis":"As of 18 August 2026, this Paper Citation Record lists 39 of 39 outbound references and 0 inbound Pith citation observations for arXiv:2508.17674."}