{"as_of":"2026-08-08T05:08:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:239969a28cada92f1d154ea29d5db9b396cd209f435ce5c25115a5760937878f","coverage":[{"denominator":38,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":38,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T10:19:29.189081Z","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":"2026-08-07T06:34:17.273281+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-06-29T06:34:52.596684Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"arxiv_reference","source_observed_at":"2026-06-29T14:33:31.438836Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"cited_work":{"arxiv_id":"2506.05739","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"2506.05739","snapshot_observed_at":"2026-06-29T14:33:31.438836Z","title":"To protect the LLM agent against the prompt injection attack with polymorphic prompt assembling,","venue":null,"work_id":"318c548f-a507-44db-aa64-84bc095d44ce","year":2025},"citing_paper":{"arxiv_id":"2605.30534","last_updated":"2026-05-28T20:10:04Z","snapshot_observed_at":"2026-08-07T21:14:16.649096Z","submitted_at":"2026-05-28T20:10:04Z","title":"Strengthening Polymorphic Prompt Assembling: Dynamic Separator Generation Against Emerging Prompt Injection Attacks","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-06-29T06:34:52.596684Z"},"links":{"cited_paper":"/paper/2506.05739","citing_paper":"/paper/2605.30534"},"observation_digest":"sha256:078e3f0eb6c33a9d1bd15a8304f98e484b08825ce6ceb6c5fe4c0e2d6ebbcf60","observation_id":"f9f5549e-2dd7-45dd-860a-6858e7ce6e1a","resolution":{"observed_at":"2026-06-29T14:33:31.440394Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2506.05739/citation-record","integrity":"/paper/2506.05739/integrity","json":"/paper/2506.05739/citation-record.json","paper":"/paper/2506.05739"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2307.12488","last_updated":"2024-07-28T07:48:44Z","snapshot_observed_at":"2026-07-06T15:57:36.128928Z","submitted_at":"2023-07-24T02:38:24Z","title":"How Does Naming Affect LLMs on Code Analysis Tasks?","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2307.12488","snapshot_observed_at":"2026-08-07T10:19:25.445223Z","title":"Chatgpt for soft- ware security: Exploring the strengths and limitations of chatgpt in the security applications,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:25.445223Z"},"links":{"cited_paper":"/paper/2307.12488","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:2f2fe6eb23ece2c807c9cb6ade896270c95eae0750bfbb1ac441eb024d5ccf3b","observation_id":"0649b10d-f881-454c-9e28-2c901c6b6628","resolution":{"observed_at":"2026-08-07T10:19:25.445223Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:33.097030Z","title":"Repair Is Nearly Generation: Multi- lingual Program Repair with LLMs,","venue":null,"work_id":"338b89e5-6611-4f73-8b9f-0c025579afef","year":2023},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:25.521645Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:d24f4099cbe11c28ef79b7e82b718e449fd3014b97a8d866d1dd0d70d1448744","observation_id":"e00af381-01fe-497c-a447-cbe3e9586384","resolution":{"observed_at":"2026-08-07T10:19:33.165141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:32.926060Z","title":"Evaluating large language models for real-world vul- nerability repair in c/c++ code,","venue":null,"work_id":"76567139-6b4c-40b1-8fdf-31ae6e67a1d1","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:25.701234Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:8520277e0a89065220c4630707df6ebdc32b952723d5ea98b47ce579104c26c1","observation_id":"1a18f318-faa0-418a-b15f-77f92f3464ad","resolution":{"observed_at":"2026-08-07T10:19:33.003046Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2211.09527","last_updated":"2022-11-17T13:43:20Z","snapshot_observed_at":"2026-07-06T14:19:47.424778Z","submitted_at":"2022-11-17T13:43:20Z","title":"Ignore Previous Prompt: Attack Techniques For Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2211.09527","snapshot_observed_at":"2026-08-07T10:19:25.879894Z","title":"Ignore previous prompt: Attack techniques for language models,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:25.879894Z"},"links":{"cited_paper":"/paper/2211.09527","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:ed414895b63de59406f624dde4e4e4f03c7c4e463a81bd541005c2e53ffb0787","observation_id":"131add71-39e1-404f-99da-ec36a718da2e","resolution":{"observed_at":"2026-08-07T10:19:25.879894Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2406.06622","last_updated":"2024-06-07T15:37:15Z","snapshot_observed_at":"2026-08-08T00:47:02.920080Z","submitted_at":"2024-06-07T15:37:15Z","title":"Adversarial Tuning: Defending Against Jailbreak Attacks for LLMs","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2406.06622","snapshot_observed_at":"2026-08-07T10:19:26.049365Z","title":"Adversarial tuning: Defending against jailbreak attacks for llms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:26.049365Z"},"links":{"cited_paper":"/paper/2406.06622","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:5dfa43dbd5072fe87c3ffe32a5f6c6925e6f865b69e9982c57a3a55cc169a7b7","observation_id":"4cf7a326-7fb5-4c32-9a4e-b4b441dc7f5a","resolution":{"observed_at":"2026-08-07T10:19:26.049365Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2310.12773","last_updated":"2023-10-19T14:22:03Z","snapshot_observed_at":"2026-08-02T16:56:38.535065Z","submitted_at":"2023-10-19T14:22:03Z","title":"Safe RLHF: Safe Reinforcement Learning from Human Feedback","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2310.12773","snapshot_observed_at":"2026-08-07T10:19:26.179955Z","title":"Safe rlhf: Safe reinforcement learning from human feedback,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:26.179955Z"},"links":{"cited_paper":"/paper/2310.12773","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:3bd73824efd02379632f76516400653d5b1ee45281bf24682d84076d4da9e082","observation_id":"7baee7c6-bd20-4af9-9ff4-c19d12886313","resolution":{"observed_at":"2026-08-07T10:19:26.179955Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:32.713736Z","title":"Training language models to follow instructions with human feedback,","venue":null,"work_id":"7cf74264-0b99-4fb1-8f14-148b4186653b","year":2022},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:26.350394Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:c688d1c0887435678719c6200c6c59b1421cfa476e2ac2898fd2c15b0ddb95a5","observation_id":"45b0e32c-199c-4331-bb9c-b0ebaf8c72fe","resolution":{"observed_at":"2026-08-07T10:19:32.804618Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00888","last_updated":"2024-11-14T22:20:49Z","snapshot_observed_at":"2026-07-06T17:23:55.166594Z","submitted_at":"2024-01-30T04:00:54Z","title":"Security and Privacy Challenges of Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00888","snapshot_observed_at":"2026-08-07T10:19:26.510759Z","title":"Security and privacy challenges of large language models: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:26.510759Z"},"links":{"cited_paper":"/paper/2402.00888","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:b1a3d2d4f9a7bd4d0cd90c718932dbca4e2778e47c3a12f086226769aa28cdae","observation_id":"47741064-91e7-4d67-ab2a-e61c9f05b7f6","resolution":{"observed_at":"2026-08-07T10:19:26.510759Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2306.05499","last_updated":"2025-12-29T02:25:27Z","snapshot_observed_at":"2026-07-06T15:40:27.639368Z","submitted_at":"2023-06-08T18:43:11Z","title":"Prompt Injection attack against LLM-integrated Applications","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2306.05499","snapshot_observed_at":"2026-08-07T10:19:26.602943Z","title":"Prompt injection attack against llm-integrated applications,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:26.602943Z"},"links":{"cited_paper":"/paper/2306.05499","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:66b5bda76c364c88905402294f39966503a304a9e004abb501046e8517957db8","observation_id":"274ad45c-598b-49a1-80be-6d60cbf83b65","resolution":{"observed_at":"2026-08-07T10:19:26.602943Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2404.01318","last_updated":"2024-10-31T22:26:40Z","snapshot_observed_at":"2026-08-02T14:59:12.115203Z","submitted_at":"2024-03-28T02:44:02Z","title":"JailbreakBench: An Open Robustness Benchmark for Jailbreaking Large Language Models","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.01318","snapshot_observed_at":"2026-08-07T10:19:26.737973Z","title":"Jailbreakbench: An open robustness benchmark for jailbreaking large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:26.737973Z"},"links":{"cited_paper":"/paper/2404.01318","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:aad10044c715b6814a6eea4d9716008f92f58792a97fc55826fdd7beb6986061","observation_id":"0e037b44-6e8d-436d-84f2-70ab36640d69","resolution":{"observed_at":"2026-08-07T10:19:26.737973Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:32.560418Z","title":"Adversarial Prompting in LLMs,","venue":null,"work_id":"55d6c3ff-5c0b-42a1-8185-05ae7c59d6ac","year":2023},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:26.828202Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:024b9552b2531b3cb509a881508d9c2882f468866cf648894f6030c2418927ef","observation_id":"26009b1c-491e-4704-98fe-6318e07a0af2","resolution":{"observed_at":"2026-08-07T10:19:32.627984Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.04295","last_updated":"2024-08-30T11:57:47Z","snapshot_observed_at":"2026-08-04T23:34:13.332065Z","submitted_at":"2024-07-05T06:57:30Z","title":"Jailbreak Attacks and Defenses Against Large Language Models: A Survey","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.04295","snapshot_observed_at":"2026-08-07T10:19:26.977002Z","title":"Jailbreak attacks and defenses against large language models: A survey,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:26.977002Z"},"links":{"cited_paper":"/paper/2407.04295","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:c06c1ee42b75b6a1f0487f7c3f7e53eb0ebb9fb25dc4c9f4b698a0666c5ff094","observation_id":"bfae5d97-6b45-440f-a63b-d4395614c408","resolution":{"observed_at":"2026-08-07T10:19:26.977002Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:32.410668Z","title":"Prompt Injection: A Comprehensive Guide,","venue":null,"work_id":"4c9d083c-7ca5-44eb-84da-72fb25817134","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:27.094083Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:e1ef27493ac3c4f27e541801917cd75e9f824ffc928c480a3e55952cd8bb7d64","observation_id":"bdba7935-8599-4b6a-b737-0ce10d3b098e","resolution":{"observed_at":"2026-08-07T10:19:32.473632Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2407.03234","last_updated":"2024-08-06T11:15:00Z","snapshot_observed_at":"2026-07-06T18:41:01.720889Z","submitted_at":"2024-07-03T16:03:42Z","title":"Self-Evaluation as a Defense Against Adversarial Attacks on LLMs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2407.03234","snapshot_observed_at":"2026-08-07T10:19:27.248071Z","title":"Self-evaluation as a defense against adversarial attacks on llms,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:27.248071Z"},"links":{"cited_paper":"/paper/2407.03234","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:09e0b8beed24afd5b9dfe3dc6c63306999bc359bb5f159596ce86339ba1bd7fc","observation_id":"abe78824-f39b-45df-9ce0-ec3ffd45b8a9","resolution":{"observed_at":"2026-08-07T10:19:27.248071Z","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":"2026-08-07T10:19:27.389402Z","title":"Formalizing and benchmarking prompt injection attacks and defenses,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:27.389402Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:61ddb6157107aafa63f89da9005c287489646f32c71cad316102dc9d271d251e","observation_id":"45b0194a-561d-43d9-8c08-6267c1a28bc2","resolution":{"observed_at":"2026-08-07T10:19:27.389402Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:32.211296Z","title":"Prompt injection attacks against gpt- 3,","venue":null,"work_id":"1325af17-a384-4a11-8f92-1da031ae01ab","year":2022},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:27.566690Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:226abcdd4fed5a399bf815debf925d2f1e78fb9dc312cac04e659a4f50f65a1c","observation_id":"96ab407a-8a48-4913-b7da-bcf187ffe516","resolution":{"observed_at":"2026-08-07T10:19:32.298393Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2402.00898","last_updated":"2024-01-31T19:52:00Z","snapshot_observed_at":"2026-08-06T01:29:20.953609Z","submitted_at":"2024-01-31T19:52:00Z","title":"An Early Categorization of Prompt Injection Attacks on Large Language Models","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2402.00898","snapshot_observed_at":"2026-08-07T10:19:27.647166Z","title":"An early categorization of prompt injection attacks on large language models,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:27.647166Z"},"links":{"cited_paper":"/paper/2402.00898","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:5721431bc7c0489db2a0ab5ff80347483d1aca5a2748191ea88d2171ee43e447","observation_id":"b8587df2-1373-4b61-ae40-16046a07e868","resolution":{"observed_at":"2026-08-07T10:19:27.647166Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2308.07702","last_updated":"2024-03-14T02:07:11Z","snapshot_observed_at":"2026-08-02T18:42:43.546865Z","submitted_at":"2023-08-15T11:08:30Z","title":"Better Zero-Shot Reasoning with Role-Play Prompting","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2308.07702","snapshot_observed_at":"2026-08-07T10:19:27.745795Z","title":"Better zero-shot reasoning with role-play prompting,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:27.745795Z"},"links":{"cited_paper":"/paper/2308.07702","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:ef2457140d660e31146e66f3e182fe42ff326c0761106b10f556b32c24d0146e","observation_id":"e0a259b1-25a2-45b8-9082-ea88c08fdef6","resolution":{"observed_at":"2026-08-07T10:19:27.745795Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:32.054252Z","title":"Lakera pint benchmark,","venue":null,"work_id":"7bfda203-8731-466b-876e-2009025ef28a","year":2025},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:27.850498Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:6fc5e4c204ebae6d2018f098d330bd23583453371dc109328244d6b4ebf97aa3","observation_id":"e58c5703-b5c2-48b3-9d39-2d17cd797252","resolution":{"observed_at":"2026-08-07T10:19:32.120677Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:31.863260Z","title":"Lakera guard,","venue":null,"work_id":"0da80a19-f707-42b4-9d15-83ca31e7a893","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:27.913862Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:9bf9d26f29413257508f5b2da9d8d2400aeb0821b7cab8ab13d12f8fb0b044e6","observation_id":"500c2f87-c31b-4e10-9d1c-90c022980452","resolution":{"observed_at":"2026-08-07T10:19:31.932251Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:31.684946Z","title":"Amazon bedrock guardrails,","venue":null,"work_id":"67bad729-90d9-482b-8889-e92e16cb4493","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.005174Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:f8ef43d994341301c4cc593bf84b49e11e4afd88e7ba80ea089d215a3e418adf","observation_id":"a0b7df2b-7ab4-4cb2-82e0-12e05a47fa72","resolution":{"observed_at":"2026-08-07T10:19:31.774757Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:31.524323Z","title":"deberta-v3-base-prompt-injection-v2,","venue":null,"work_id":"dbc24512-d787-48f8-a130-84a11ac51b3d","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.131590Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:1c8f3677e2f7ed1a2708102984eda678fec659aabdf7c040c9e03298d615460b","observation_id":"32122546-539e-4fd2-bf09-6d0a96c12ef7","resolution":{"observed_at":"2026-08-07T10:19:31.609448Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:31.313861Z","title":"Prompt-guard-86m,","venue":null,"work_id":"45d4fb50-b8b5-434d-b18b-62bf6ecb2245","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.214660Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:826726dd9d4e4bc5c1f9cdefdbae04f1e806c0398a034cbfde417117a44955c8","observation_id":"558f4c3e-8610-458e-813a-a0b1ce25db54","resolution":{"observed_at":"2026-08-07T10:19:31.400317Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:31.145720Z","title":"deberta-v3-base-prompt-injection,","venue":null,"work_id":"e3ab2250-e441-4a4d-be5a-3204c11e02ab","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.295069Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:037ce8ceb8a7b8cc46cc62fe07f8884506724734e80fe36216c838eea91bd164","observation_id":"bf94cae6-80c4-4d2b-b631-fbef783bcb0c","resolution":{"observed_at":"2026-08-07T10:19:31.206818Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:30.947765Z","title":"Jailbreak detection in azure ai content safety,","venue":null,"work_id":"477f02d4-e617-4378-8efa-c80a21e4d318","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.406950Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:1d7f1fe99b8d4600f6bb2b337de82ab2237137ed6c261d58572b47acd64e555d","observation_id":"33d26895-6d97-408e-b2a4-db4629d80590","resolution":{"observed_at":"2026-08-07T10:19:31.063676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:30.859310Z","title":"Langkit,","venue":null,"work_id":"b3b9b309-820e-4352-9646-321f54f2d5f0","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.479974Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:412fd66e0dca469c072fd459c5c22ff430fc661bf91382f7eb8fe4bb556c1f65","observation_id":"45c6ec60-6a61-4521-971c-5940c0e0831e","resolution":{"observed_at":"2026-08-07T10:19:30.900752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:30.600051Z","title":"Hyperion,","venue":null,"work_id":"2eb3d6f5-5386-4f94-b857-3ee9fba69464","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.518088Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:9f853fe1d6eff19105ca3b097f6e736934b2d80fc5585480fec08d51627ef31b","observation_id":"ac9fb702-1e5d-4f88-865f-1eb06f38ddf9","resolution":{"observed_at":"2026-08-07T10:19:30.716482Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:30.384086Z","title":"distilbert-prompt-injection,","venue":null,"work_id":"1a46899e-a8f9-4b5f-9bbb-427d86ae9547","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.622804Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:a0970944995c18321168413ec49f628460eca9b2b291bb165f7d7c11a1761329","observation_id":"eb8735e1-7ae1-4dfa-98ab-e2737ffc901b","resolution":{"observed_at":"2026-08-07T10:19:30.453066Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:30.206315Z","title":"deepset/deberta-v3-base-injection,","venue":null,"work_id":"9108b1d2-beb8-43d4-ac7c-9837304042bb","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.682887Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:6fbd188e06a34cf57e0c79b9c1b1b122553950452aef0d55e8597887dad59699","observation_id":"7c7281b8-4d39-4244-9f8d-1b1cb2ffca47","resolution":{"observed_at":"2026-08-07T10:19:30.287872Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:30.037810Z","title":"setfit-prompt-injection-minilm-l3-v2,","venue":null,"work_id":"2b7467c8-3215-4aa1-a2ed-b8544613ee84","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.720904Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:d2e3c1afb0cc67105a2248098a232960e0a9fff2066b30555ffd3e1161a78735","observation_id":"0afe1ba4-9550-4ca1-95fe-40353059ef9d","resolution":{"observed_at":"2026-08-07T10:19:30.103815Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2409.19521","last_updated":"2024-09-29T02:35:38Z","snapshot_observed_at":"2026-08-06T01:53:51.051373Z","submitted_at":"2024-09-29T02:35:38Z","title":"GenTel-Safe: A Unified Benchmark and Shielding Framework for Defending Against Prompt Injection Attacks","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2409.19521","snapshot_observed_at":"2026-08-07T10:19:28.764757Z","title":"Gentel-safe: A unified benchmark and shielding frame- work for defending against prompt injection attacks,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.764757Z"},"links":{"cited_paper":"/paper/2409.19521","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:756768b7db07ad18d0cbebe031193e20bdb7fbe47a3db8f9f96fb25c15114465","observation_id":"1603b887-b505-4a34-8d6e-dbdfaf762a35","resolution":{"observed_at":"2026-08-07T10:19:28.764757Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:29.944868Z","title":"Hyperion,","venue":null,"work_id":"2d2e5036-8cc2-44b5-a6fa-e77253ea335d","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.809662Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:a7702a0ed2d521f4565a71da63edd3afae175d51cbe516b5aabcc116aff755ea","observation_id":"5debd830-fed0-465a-82e6-9663f7ce0a6f","resolution":{"observed_at":"2026-08-07T10:19:29.979847Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:29.850428Z","title":"Whylabs langkit,","venue":null,"work_id":"eff243ae-7468-450d-bf00-2a5b782dd179","year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.876771Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:5a7ca41784f6b93b9a55357633153187dce687f44aa3159c3e8e9de2693e775e","observation_id":"0790fb08-a6b7-4c05-9399-4a2322168d67","resolution":{"observed_at":"2026-08-07T10:19:29.897487Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:29.758526Z","title":"Baseline defenses for adversarial attacks against aligned language models,","venue":null,"work_id":"567de07e-5463-4dd1-8848-d318b4a7f257","year":null},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.928523Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:0c900e8750f54cd7bff430d118ddf659330bab4b13a0c639e2b4835f061ca16d","observation_id":"108b383e-74e9-4d8f-96c8-25e9dfffed09","resolution":{"observed_at":"2026-08-07T10:19:29.801080Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2410.13236","last_updated":"2024-10-17T05:40:54Z","snapshot_observed_at":"2026-07-06T19:35:04.087584Z","submitted_at":"2024-10-17T05:40:54Z","title":"SPIN: Self-Supervised Prompt INjection","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2410.13236","snapshot_observed_at":"2026-08-07T10:19:29.037606Z","title":"Spin: Self-supervised prompt injection,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:29.037606Z"},"links":{"cited_paper":"/paper/2410.13236","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:9982a82e93afa086c07bf6ab21f8c956b8a76c1a015ac41b8bad6e347423dc96","observation_id":"3c8fb94b-4777-46e7-94ff-26744ae080af","resolution":{"observed_at":"2026-08-07T10:19:29.037606Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2411.00459","last_updated":"2025-08-02T13:44:03Z","snapshot_observed_at":"2026-08-04T09:37:10.818143Z","submitted_at":"2024-11-01T09:14:21Z","title":"Defense Against Prompt Injection Attack by Leveraging Attack Techniques","version":6},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2411.00459","snapshot_observed_at":"2026-08-07T10:19:29.117892Z","title":"Defense against prompt injection attack by leveraging attack techniques,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:29.117892Z"},"links":{"cited_paper":"/paper/2411.00459","citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:e6e7f02a99ad5502cccbc2f3fe2b193ae19cb1621a18e6d7662fbfbd1c05d4c7","observation_id":"2f2592d3-4b80-4756-bf2a-7f45da7bafdc","resolution":{"observed_at":"2026-08-07T10:19:29.117892Z","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":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:29.540720Z","title":"Promptshield: Deployable detection for prompt in- jection attacks,","venue":null,"work_id":"1afdc326-4cd9-45b7-994a-dc7f16011a69","year":2025},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:29.189081Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:1747e81603bfb3dca36ffb75a77d45f1d0a6ce57ada88f079185b3dcb0e6a854","observation_id":"8e62fd7b-6eb0-491e-bc52-fff747fe3892","resolution":{"observed_at":"2026-08-07T10:19:29.616807Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-07T10:19:29.679340Z","title":"Available: https://arxiv.org/abs/2309","venue":null,"work_id":"8df9975b-97a6-417c-a28e-f7ccac5ba170","year":null},"citing_paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt","version":1},"reference_index":2023,"source":"pdf_text","source_observed_at":"2026-08-07T10:19:28.980854Z"},"links":{"citing_paper":"/paper/2506.05739"},"observation_digest":"sha256:6702acb90ce190da9afb159637a37ca1f76df9d41c86ff8a87a6f523d9ad3cfa","observation_id":"aca54fdb-28d2-4006-a64f-b22406a03e97","resolution":{"observed_at":"2026-08-07T10:19:29.713100Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-07T06:34:17.273281+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2506.05739","last_updated":"2025-06-06T04:50:57Z","latest_version":1,"primary_category":"cs.CR","snapshot_observed_at":"2026-08-08T00:47:37.037083Z","submitted_at":"2025-06-06T04:50:57Z","title":"To Protect the LLM Agent Against the Prompt Injection Attack with Polymorphic Prompt"},"reference_resolution":{"displayed":38,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":0,"verified_fuzzy":23},"total_outbound_references":38},"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":"2026-08-07T06:34:17.273281+00:00","source":"crossref"},{"observed_at":"2026-08-07T06:34:11.927384+00:00","source":"retraction_watch"}],"thesis":"As of 8 August 2026, this Paper Citation Record lists 38 of 38 outbound references and 1 inbound Pith citation observation for arXiv:2506.05739."}