{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:5GPJQJ7RAMOLTPJD6UPFISG73U","short_pith_number":"pith:5GPJQJ7R","schema_version":"1.0","canonical_sha256":"e99e9827f1031cb9bd23f51e5448dfdd38c05a937a163d878f66838cc84a1051","source":{"kind":"arxiv","id":"2411.08088","version":1},"attestation_state":"computed","paper":{"title":"Safety case template for frontier AI: A cyber inability argument","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CY","authors_text":"Arthur Goemans, Benjamin Hilton, Geoffrey Irving, Jessica Wang, Jonas Schuett, Marie Davidsen Buhl, Tomek Korbak","submitted_at":"2024-11-12T18:45:08Z","abstract_excerpt":"Frontier artificial intelligence (AI) systems pose increasing risks to society, making it essential for developers to provide assurances about their safety. One approach to offering such assurances is through a safety case: a structured, evidence-based argument aimed at demonstrating why the risk associated with a safety-critical system is acceptable. In this article, we propose a safety case template for offensive cyber capabilities. We illustrate how developers could argue that a model does not have capabilities posing unacceptable cyber risks by breaking down the main claim into progressive"},"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":"2411.08088","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CY","submitted_at":"2024-11-12T18:45:08Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"d3de9177876c82159ec89567ca199435890d425c2b8a5535d4a427ed082ed766","abstract_canon_sha256":"3ff5fd68f761c204c2e3705214af3a5c5da75a777c41a73f5eff2dfd0e2063b6"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:34:41.302432Z","signature_b64":"ueZtgBCFt1YAWrk/xLjnd6KKqzWrZTY5Ldt3fio/UXbxwKEj0dt6MpjXY6R5pvCGVCDC6mnM+WTS02yL99AGBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e99e9827f1031cb9bd23f51e5448dfdd38c05a937a163d878f66838cc84a1051","last_reissued_at":"2026-07-05T09:34:41.301817Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:34:41.301817Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Safety case template for frontier AI: A cyber inability argument","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.CY","authors_text":"Arthur Goemans, Benjamin Hilton, Geoffrey Irving, Jessica Wang, Jonas Schuett, Marie Davidsen Buhl, Tomek Korbak","submitted_at":"2024-11-12T18:45:08Z","abstract_excerpt":"Frontier artificial intelligence (AI) systems pose increasing risks to society, making it essential for developers to provide assurances about their safety. One approach to offering such assurances is through a safety case: a structured, evidence-based argument aimed at demonstrating why the risk associated with a safety-critical system is acceptable. In this article, we propose a safety case template for offensive cyber capabilities. We illustrate how developers could argue that a model does not have capabilities posing unacceptable cyber risks by breaking down the main claim into progressive"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2411.08088","kind":"arxiv","version":1},"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/2411.08088/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":"2411.08088","created_at":"2026-07-05T09:34:41.301872+00:00"},{"alias_kind":"arxiv_version","alias_value":"2411.08088v1","created_at":"2026-07-05T09:34:41.301872+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2411.08088","created_at":"2026-07-05T09:34:41.301872+00:00"},{"alias_kind":"pith_short_12","alias_value":"5GPJQJ7RAMOL","created_at":"2026-07-05T09:34:41.301872+00:00"},{"alias_kind":"pith_short_16","alias_value":"5GPJQJ7RAMOLTPJD","created_at":"2026-07-05T09:34:41.301872+00:00"},{"alias_kind":"pith_short_8","alias_value":"5GPJQJ7R","created_at":"2026-07-05T09:34:41.301872+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.00047","citing_title":"Comprehensive AI governance requires addressing non-model gains","ref_index":41,"is_internal_anchor":false},{"citing_arxiv_id":"2507.11473","citing_title":"Chain of Thought Monitorability: A New and Fragile Opportunity for AI Safety","ref_index":26,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5GPJQJ7RAMOLTPJD6UPFISG73U","json":"https://pith.science/pith/5GPJQJ7RAMOLTPJD6UPFISG73U.json","graph_json":"https://pith.science/api/pith-number/5GPJQJ7RAMOLTPJD6UPFISG73U/graph.json","events_json":"https://pith.science/api/pith-number/5GPJQJ7RAMOLTPJD6UPFISG73U/events.json","paper":"https://pith.science/paper/5GPJQJ7R"},"agent_actions":{"view_html":"https://pith.science/pith/5GPJQJ7RAMOLTPJD6UPFISG73U","download_json":"https://pith.science/pith/5GPJQJ7RAMOLTPJD6UPFISG73U.json","view_paper":"https://pith.science/paper/5GPJQJ7R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2411.08088&json=true","fetch_graph":"https://pith.science/api/pith-number/5GPJQJ7RAMOLTPJD6UPFISG73U/graph.json","fetch_events":"https://pith.science/api/pith-number/5GPJQJ7RAMOLTPJD6UPFISG73U/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5GPJQJ7RAMOLTPJD6UPFISG73U/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5GPJQJ7RAMOLTPJD6UPFISG73U/action/storage_attestation","attest_author":"https://pith.science/pith/5GPJQJ7RAMOLTPJD6UPFISG73U/action/author_attestation","sign_citation":"https://pith.science/pith/5GPJQJ7RAMOLTPJD6UPFISG73U/action/citation_signature","submit_replication":"https://pith.science/pith/5GPJQJ7RAMOLTPJD6UPFISG73U/action/replication_record"}},"created_at":"2026-07-05T09:34:41.301872+00:00","updated_at":"2026-07-05T09:34:41.301872+00:00"}