{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:TTHQKZEZNF4BMR4G2WVWJSFS3C","short_pith_number":"pith:TTHQKZEZ","schema_version":"1.0","canonical_sha256":"9ccf0564996978164786d5ab64c8b2d8825cb4f222cbc5c55817fe4de0e4b460","source":{"kind":"arxiv","id":"2506.13726","version":1},"attestation_state":"computed","paper":{"title":"Weakest Link in the Chain: Security Vulnerabilities in Advanced Reasoning Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.AI","authors_text":"Aaditya Rastogi, Arjun Krishna, Erick Galinkin","submitted_at":"2025-06-16T17:32:18Z","abstract_excerpt":"The introduction of advanced reasoning capabilities have improved the problem-solving performance of large language models, particularly on math and coding benchmarks. However, it remains unclear whether these reasoning models are more or less vulnerable to adversarial prompt attacks than their non-reasoning counterparts. In this work, we present a systematic evaluation of weaknesses in advanced reasoning models compared to similar non-reasoning models across a diverse set of prompt-based attack categories. Using experimental data, we find that on average the reasoning-augmented models are \\em"},"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":"2506.13726","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.AI","submitted_at":"2025-06-16T17:32:18Z","cross_cats_sorted":["cs.CR","cs.LG"],"title_canon_sha256":"cd5db915349ef71eaa31a86edade139017ddb8f8d9e61636012ea04bf50fb2be","abstract_canon_sha256":"c4429f74c0789433dcb618628a695d2d78c0c7b7f9d1c851d76d0beb60d73b1f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:22:25.563362Z","signature_b64":"Q02syUGeJAedvCg8WbPGE+hWn/QRrdshB68VDD5cuWIJXWOCJalqI9DcFreyoGGp4Y+Zag9+JunF+1QwiNB5AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9ccf0564996978164786d5ab64c8b2d8825cb4f222cbc5c55817fe4de0e4b460","last_reissued_at":"2026-07-05T11:22:25.562803Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:22:25.562803Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Weakest Link in the Chain: Security Vulnerabilities in Advanced Reasoning Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CR","cs.LG"],"primary_cat":"cs.AI","authors_text":"Aaditya Rastogi, Arjun Krishna, Erick Galinkin","submitted_at":"2025-06-16T17:32:18Z","abstract_excerpt":"The introduction of advanced reasoning capabilities have improved the problem-solving performance of large language models, particularly on math and coding benchmarks. However, it remains unclear whether these reasoning models are more or less vulnerable to adversarial prompt attacks than their non-reasoning counterparts. In this work, we present a systematic evaluation of weaknesses in advanced reasoning models compared to similar non-reasoning models across a diverse set of prompt-based attack categories. Using experimental data, we find that on average the reasoning-augmented models are \\em"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.13726","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/2506.13726/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":"2506.13726","created_at":"2026-07-05T11:22:25.562863+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.13726v1","created_at":"2026-07-05T11:22:25.562863+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.13726","created_at":"2026-07-05T11:22:25.562863+00:00"},{"alias_kind":"pith_short_12","alias_value":"TTHQKZEZNF4B","created_at":"2026-07-05T11:22:25.562863+00:00"},{"alias_kind":"pith_short_16","alias_value":"TTHQKZEZNF4BMR4G","created_at":"2026-07-05T11:22:25.562863+00:00"},{"alias_kind":"pith_short_8","alias_value":"TTHQKZEZ","created_at":"2026-07-05T11:22:25.562863+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/TTHQKZEZNF4BMR4G2WVWJSFS3C","json":"https://pith.science/pith/TTHQKZEZNF4BMR4G2WVWJSFS3C.json","graph_json":"https://pith.science/api/pith-number/TTHQKZEZNF4BMR4G2WVWJSFS3C/graph.json","events_json":"https://pith.science/api/pith-number/TTHQKZEZNF4BMR4G2WVWJSFS3C/events.json","paper":"https://pith.science/paper/TTHQKZEZ"},"agent_actions":{"view_html":"https://pith.science/pith/TTHQKZEZNF4BMR4G2WVWJSFS3C","download_json":"https://pith.science/pith/TTHQKZEZNF4BMR4G2WVWJSFS3C.json","view_paper":"https://pith.science/paper/TTHQKZEZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.13726&json=true","fetch_graph":"https://pith.science/api/pith-number/TTHQKZEZNF4BMR4G2WVWJSFS3C/graph.json","fetch_events":"https://pith.science/api/pith-number/TTHQKZEZNF4BMR4G2WVWJSFS3C/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TTHQKZEZNF4BMR4G2WVWJSFS3C/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TTHQKZEZNF4BMR4G2WVWJSFS3C/action/storage_attestation","attest_author":"https://pith.science/pith/TTHQKZEZNF4BMR4G2WVWJSFS3C/action/author_attestation","sign_citation":"https://pith.science/pith/TTHQKZEZNF4BMR4G2WVWJSFS3C/action/citation_signature","submit_replication":"https://pith.science/pith/TTHQKZEZNF4BMR4G2WVWJSFS3C/action/replication_record"}},"created_at":"2026-07-05T11:22:25.562863+00:00","updated_at":"2026-07-05T11:22:25.562863+00:00"}