{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:BA53WSLVGJB5ZK3LU5UVDY53W4","short_pith_number":"pith:BA53WSLV","schema_version":"1.0","canonical_sha256":"083bbb49753243dcab6ba76951e3bbb7188beeae9575c61309aa37be8f8df215","source":{"kind":"arxiv","id":"2410.09097","version":2},"attestation_state":"computed","paper":{"title":"Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"F.D.C.M. Curie, Nilay Pochhi, Tarun Raheja","submitted_at":"2024-10-09T01:35:38Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks. This survey paper presents a comprehensive analysis of recent advancements in attack strategies and defense mechanisms within the field of Large Language Model (LLM) red-teaming. We analyze various attack methods, including gradient-based optimization, reinforcement learning, and prompt engineering approaches. We discuss the implications of these attacks on LLM safety and the need for improved defense mechani"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2410.09097","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2024-10-09T01:35:38Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"7ac7d26af8bb76e3b849470b089f05d7f271e44400158c55b3c5fa67c3a4bc1a","abstract_canon_sha256":"149f7f9ae6f3f4895ddb97b3335a9b2cfc11850f95cd997ecaa48bfcd2dcf52d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:50:06.122783Z","signature_b64":"c7NixZuZEfnYxha735TPcapzfLUBHYdcL6cTJgY7Rr74+7J/1VUxAvuzeKG0FJkDMXOPuFKrgI4VX/HTjJgaAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"083bbb49753243dcab6ba76951e3bbb7188beeae9575c61309aa37be8f8df215","last_reissued_at":"2026-07-05T09:50:06.122347Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:50:06.122347Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Recent advancements in LLM Red-Teaming: Techniques, Defenses, and Ethical Considerations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"F.D.C.M. Curie, Nilay Pochhi, Tarun Raheja","submitted_at":"2024-10-09T01:35:38Z","abstract_excerpt":"Large Language Models (LLMs) have demonstrated remarkable capabilities in natural language processing tasks, but their vulnerability to jailbreak attacks poses significant security risks. This survey paper presents a comprehensive analysis of recent advancements in attack strategies and defense mechanisms within the field of Large Language Model (LLM) red-teaming. We analyze various attack methods, including gradient-based optimization, reinforcement learning, and prompt engineering approaches. We discuss the implications of these attacks on LLM safety and the need for improved defense mechani"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.09097","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2410.09097/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2410.09097","created_at":"2026-07-05T09:50:06.122406+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.09097v2","created_at":"2026-07-05T09:50:06.122406+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.09097","created_at":"2026-07-05T09:50:06.122406+00:00"},{"alias_kind":"pith_short_12","alias_value":"BA53WSLVGJB5","created_at":"2026-07-05T09:50:06.122406+00:00"},{"alias_kind":"pith_short_16","alias_value":"BA53WSLVGJB5ZK3L","created_at":"2026-07-05T09:50:06.122406+00:00"},{"alias_kind":"pith_short_8","alias_value":"BA53WSLV","created_at":"2026-07-05T09:50:06.122406+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02883","citing_title":"LLM-Assisted Reranking to Operationalize Nuanced Objectives in Recommender Systems","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2512.05929","citing_title":"LLM Harms: A Taxonomy and Discussion","ref_index":207,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/BA53WSLVGJB5ZK3LU5UVDY53W4","json":"https://pith.science/pith/BA53WSLVGJB5ZK3LU5UVDY53W4.json","graph_json":"https://pith.science/api/pith-number/BA53WSLVGJB5ZK3LU5UVDY53W4/graph.json","events_json":"https://pith.science/api/pith-number/BA53WSLVGJB5ZK3LU5UVDY53W4/events.json","paper":"https://pith.science/paper/BA53WSLV"},"agent_actions":{"view_html":"https://pith.science/pith/BA53WSLVGJB5ZK3LU5UVDY53W4","download_json":"https://pith.science/pith/BA53WSLVGJB5ZK3LU5UVDY53W4.json","view_paper":"https://pith.science/paper/BA53WSLV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.09097&json=true","fetch_graph":"https://pith.science/api/pith-number/BA53WSLVGJB5ZK3LU5UVDY53W4/graph.json","fetch_events":"https://pith.science/api/pith-number/BA53WSLVGJB5ZK3LU5UVDY53W4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BA53WSLVGJB5ZK3LU5UVDY53W4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BA53WSLVGJB5ZK3LU5UVDY53W4/action/storage_attestation","attest_author":"https://pith.science/pith/BA53WSLVGJB5ZK3LU5UVDY53W4/action/author_attestation","sign_citation":"https://pith.science/pith/BA53WSLVGJB5ZK3LU5UVDY53W4/action/citation_signature","submit_replication":"https://pith.science/pith/BA53WSLVGJB5ZK3LU5UVDY53W4/action/replication_record"}},"created_at":"2026-07-05T09:50:06.122406+00:00","updated_at":"2026-07-05T09:50:06.122406+00:00"}