{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:Z7FLMSHIKDDTGNH77NUK72UMKG","short_pith_number":"pith:Z7FLMSHI","schema_version":"1.0","canonical_sha256":"cfcab648e850c73334fffb68afea8c51934fbeb95388736d258d8ad5d5b6dcb8","source":{"kind":"arxiv","id":"2311.11796","version":2},"attestation_state":"computed","paper":{"title":"Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.CR","authors_text":"Bocheng Chen, Ce Zhou, Guangjing Wang, Hanqing Guo, Qiben Yan, Yuanda Wang","submitted_at":"2023-11-20T14:29:45Z","abstract_excerpt":"As Artificial Intelligence (AI) systems increasingly underpin critical applications, from autonomous vehicles to biometric authentication, their vulnerability to transferable attacks presents a growing concern. These attacks, designed to generalize across instances, domains, models, tasks, modalities, or even hardware platforms, pose severe risks to security, privacy, and system integrity. This survey delivers the first comprehensive review of transferable attacks across seven major categories, including evasion, backdoor, data poisoning, model stealing, model inversion, membership inference, "},"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":"2311.11796","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CR","submitted_at":"2023-11-20T14:29:45Z","cross_cats_sorted":["cs.AI","cs.CL","cs.CV"],"title_canon_sha256":"d7638b9697afa54ba254caca267a3d1315833f30acc66b81f342f2e6c23e2419","abstract_canon_sha256":"d85c35c0179e65b1ce3c3a42dde3b4876a5e6d3330d991534f4ed802a598fb61"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:01:22.549851Z","signature_b64":"oGMr72ZJbyOuCUT9ejeUA+YAqfQJqbjUYvyGOodD4hEGnnQZ05ZsmyBwQXIgpbNtY7LcmZgR1/iLKRn+RfXgDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cfcab648e850c73334fffb68afea8c51934fbeb95388736d258d8ad5d5b6dcb8","last_reissued_at":"2026-07-05T11:01:22.549353Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:01:22.549353Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Beyond Boundaries: A Comprehensive Survey of Transferable Attacks on AI Systems","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CL","cs.CV"],"primary_cat":"cs.CR","authors_text":"Bocheng Chen, Ce Zhou, Guangjing Wang, Hanqing Guo, Qiben Yan, Yuanda Wang","submitted_at":"2023-11-20T14:29:45Z","abstract_excerpt":"As Artificial Intelligence (AI) systems increasingly underpin critical applications, from autonomous vehicles to biometric authentication, their vulnerability to transferable attacks presents a growing concern. These attacks, designed to generalize across instances, domains, models, tasks, modalities, or even hardware platforms, pose severe risks to security, privacy, and system integrity. This survey delivers the first comprehensive review of transferable attacks across seven major categories, including evasion, backdoor, data poisoning, model stealing, model inversion, membership inference, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2311.11796","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/2311.11796/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":"2311.11796","created_at":"2026-07-05T11:01:22.549409+00:00"},{"alias_kind":"arxiv_version","alias_value":"2311.11796v2","created_at":"2026-07-05T11:01:22.549409+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2311.11796","created_at":"2026-07-05T11:01:22.549409+00:00"},{"alias_kind":"pith_short_12","alias_value":"Z7FLMSHIKDDT","created_at":"2026-07-05T11:01:22.549409+00:00"},{"alias_kind":"pith_short_16","alias_value":"Z7FLMSHIKDDTGNH7","created_at":"2026-07-05T11:01:22.549409+00:00"},{"alias_kind":"pith_short_8","alias_value":"Z7FLMSHI","created_at":"2026-07-05T11:01:22.549409+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":3,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.10571","citing_title":"Improving Adversarial Transferability on Vision-Language Pre-training Models via Surrogate-Specific Bias Correction","ref_index":30,"is_internal_anchor":false},{"citing_arxiv_id":"2601.02947","citing_title":"Quality Degradation Attack in Synthetic Data","ref_index":13,"is_internal_anchor":false},{"citing_arxiv_id":"2604.02937","citing_title":"If It's Good Enough for You, It's Good Enough for Me: Transferability of Audio Sufficiencies across Models","ref_index":31,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/Z7FLMSHIKDDTGNH77NUK72UMKG","json":"https://pith.science/pith/Z7FLMSHIKDDTGNH77NUK72UMKG.json","graph_json":"https://pith.science/api/pith-number/Z7FLMSHIKDDTGNH77NUK72UMKG/graph.json","events_json":"https://pith.science/api/pith-number/Z7FLMSHIKDDTGNH77NUK72UMKG/events.json","paper":"https://pith.science/paper/Z7FLMSHI"},"agent_actions":{"view_html":"https://pith.science/pith/Z7FLMSHIKDDTGNH77NUK72UMKG","download_json":"https://pith.science/pith/Z7FLMSHIKDDTGNH77NUK72UMKG.json","view_paper":"https://pith.science/paper/Z7FLMSHI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2311.11796&json=true","fetch_graph":"https://pith.science/api/pith-number/Z7FLMSHIKDDTGNH77NUK72UMKG/graph.json","fetch_events":"https://pith.science/api/pith-number/Z7FLMSHIKDDTGNH77NUK72UMKG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Z7FLMSHIKDDTGNH77NUK72UMKG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Z7FLMSHIKDDTGNH77NUK72UMKG/action/storage_attestation","attest_author":"https://pith.science/pith/Z7FLMSHIKDDTGNH77NUK72UMKG/action/author_attestation","sign_citation":"https://pith.science/pith/Z7FLMSHIKDDTGNH77NUK72UMKG/action/citation_signature","submit_replication":"https://pith.science/pith/Z7FLMSHIKDDTGNH77NUK72UMKG/action/replication_record"}},"created_at":"2026-07-05T11:01:22.549409+00:00","updated_at":"2026-07-05T11:01:22.549409+00:00"}