{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LGSZREDKVGA4WEECRZSFTVTSF4","short_pith_number":"pith:LGSZREDK","schema_version":"1.0","canonical_sha256":"59a598906aa981cb10828e6459d6722f10aa5baf3ac2207ea4de674f40f8da23","source":{"kind":"arxiv","id":"2602.14345","version":2},"attestation_state":"computed","paper":{"title":"AXE: Grey-Box Exploitability Confirmation for Localized Vulnerability Reports","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee, Tu Nguyen","submitted_at":"2026-02-15T23:25:14Z","abstract_excerpt":"Vulnerability detection tools are widely adopted in software projects, yet they often overwhelm maintainers with false positives and non-actionable reports. Automated exploitation systems can help validate these reports; however, existing approaches typically operate in isolation from detection pipelines, failing to leverage readily available metadata such as vulnerability type and source-code location. In this paper, we investigate how reported security vulnerabilities can be assessed in a realistic grey-box exploitation setting that leverages minimal vulnerability metadata, specifically a CW"},"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":"2602.14345","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2026-02-15T23:25:14Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"2f36ed4973fd48c1bce5484fe8d9a6a4781d5a4c8828680ed250efc62cfc0f0c","abstract_canon_sha256":"580cfca00aaacf9bd1ccb5f0f9efb291adbfc72ce586c20027f9af4ae55eecd3"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:21.147333Z","signature_b64":"vouFKIcgKniKMqMjmZYGM/P8C+yA7R56Q5MM09XNmPNkTsNNOpBGdDoqwQF/V4jXov996iIjgi7YC47azRzTAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"59a598906aa981cb10828e6459d6722f10aa5baf3ac2207ea4de674f40f8da23","last_reissued_at":"2026-06-23T02:13:21.146889Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:21.146889Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"AXE: Grey-Box Exploitability Confirmation for Localized Vulnerability Reports","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Amirali Sajadi, Kostadin Damevski, Preetha Chatterjee, Tu Nguyen","submitted_at":"2026-02-15T23:25:14Z","abstract_excerpt":"Vulnerability detection tools are widely adopted in software projects, yet they often overwhelm maintainers with false positives and non-actionable reports. Automated exploitation systems can help validate these reports; however, existing approaches typically operate in isolation from detection pipelines, failing to leverage readily available metadata such as vulnerability type and source-code location. In this paper, we investigate how reported security vulnerabilities can be assessed in a realistic grey-box exploitation setting that leverages minimal vulnerability metadata, specifically a CW"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2602.14345","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/2602.14345/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":"2602.14345","created_at":"2026-06-23T02:13:21.146946+00:00"},{"alias_kind":"arxiv_version","alias_value":"2602.14345v2","created_at":"2026-06-23T02:13:21.146946+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2602.14345","created_at":"2026-06-23T02:13:21.146946+00:00"},{"alias_kind":"pith_short_12","alias_value":"LGSZREDKVGA4","created_at":"2026-06-23T02:13:21.146946+00:00"},{"alias_kind":"pith_short_16","alias_value":"LGSZREDKVGA4WEEC","created_at":"2026-06-23T02:13:21.146946+00:00"},{"alias_kind":"pith_short_8","alias_value":"LGSZREDK","created_at":"2026-06-23T02:13:21.146946+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2605.23243","citing_title":"Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerability Benchmarks","ref_index":3,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/LGSZREDKVGA4WEECRZSFTVTSF4","json":"https://pith.science/pith/LGSZREDKVGA4WEECRZSFTVTSF4.json","graph_json":"https://pith.science/api/pith-number/LGSZREDKVGA4WEECRZSFTVTSF4/graph.json","events_json":"https://pith.science/api/pith-number/LGSZREDKVGA4WEECRZSFTVTSF4/events.json","paper":"https://pith.science/paper/LGSZREDK"},"agent_actions":{"view_html":"https://pith.science/pith/LGSZREDKVGA4WEECRZSFTVTSF4","download_json":"https://pith.science/pith/LGSZREDKVGA4WEECRZSFTVTSF4.json","view_paper":"https://pith.science/paper/LGSZREDK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2602.14345&json=true","fetch_graph":"https://pith.science/api/pith-number/LGSZREDKVGA4WEECRZSFTVTSF4/graph.json","fetch_events":"https://pith.science/api/pith-number/LGSZREDKVGA4WEECRZSFTVTSF4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LGSZREDKVGA4WEECRZSFTVTSF4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LGSZREDKVGA4WEECRZSFTVTSF4/action/storage_attestation","attest_author":"https://pith.science/pith/LGSZREDKVGA4WEECRZSFTVTSF4/action/author_attestation","sign_citation":"https://pith.science/pith/LGSZREDKVGA4WEECRZSFTVTSF4/action/citation_signature","submit_replication":"https://pith.science/pith/LGSZREDKVGA4WEECRZSFTVTSF4/action/replication_record"}},"created_at":"2026-06-23T02:13:21.146946+00:00","updated_at":"2026-06-23T02:13:21.146946+00:00"}