{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:SDO3P7DNJFZQETPLMN7JO7PD54","short_pith_number":"pith:SDO3P7DN","schema_version":"1.0","canonical_sha256":"90ddb7fc6d4973024deb637e977de3ef3cd0f7e726e18e03a906b2bf45745d73","source":{"kind":"arxiv","id":"2403.12693","version":1},"attestation_state":"computed","paper":{"title":"As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anjun Hu, Francesco Pinto, Jindong Gu, Konstantinos Kamnitsas, Philip Torr","submitted_at":"2024-03-19T12:51:39Z","abstract_excerpt":"Foundation models pre-trained on web-scale vision-language data, such as CLIP, are widely used as cornerstones of powerful machine learning systems. While pre-training offers clear advantages for downstream learning, it also endows downstream models with shared adversarial vulnerabilities that can be easily identified through the open-sourced foundation model. In this work, we expose such vulnerabilities in CLIP's downstream models and show that foundation models can serve as a basis for attacking their downstream systems. In particular, we propose a simple yet effective adversarial attack str"},"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":"2403.12693","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2024-03-19T12:51:39Z","cross_cats_sorted":[],"title_canon_sha256":"ea74ca794b2f6ec642c0fdef35c30ef97ffba7a79d1a1be982e25808eec7a289","abstract_canon_sha256":"e568fc9b6ebc48b397a2c941201d54affbef42b93166b8515de9ddd031852302"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:58:05.531905Z","signature_b64":"PHTdw7SqVof1VCDY823lgT0bCHB0Rnwh5imMMh1cWCLAwcMKqEwp0gOcw0u1t3ZwlQipXqciaineiQCPC4OEBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"90ddb7fc6d4973024deb637e977de3ef3cd0f7e726e18e03a906b2bf45745d73","last_reissued_at":"2026-07-05T07:58:05.531398Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:58:05.531398Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"As Firm As Their Foundations: Can open-sourced foundation models be used to create adversarial examples for downstream tasks?","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anjun Hu, Francesco Pinto, Jindong Gu, Konstantinos Kamnitsas, Philip Torr","submitted_at":"2024-03-19T12:51:39Z","abstract_excerpt":"Foundation models pre-trained on web-scale vision-language data, such as CLIP, are widely used as cornerstones of powerful machine learning systems. While pre-training offers clear advantages for downstream learning, it also endows downstream models with shared adversarial vulnerabilities that can be easily identified through the open-sourced foundation model. In this work, we expose such vulnerabilities in CLIP's downstream models and show that foundation models can serve as a basis for attacking their downstream systems. In particular, we propose a simple yet effective adversarial attack str"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.12693","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/2403.12693/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":"2403.12693","created_at":"2026-07-05T07:58:05.531462+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.12693v1","created_at":"2026-07-05T07:58:05.531462+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.12693","created_at":"2026-07-05T07:58:05.531462+00:00"},{"alias_kind":"pith_short_12","alias_value":"SDO3P7DNJFZQ","created_at":"2026-07-05T07:58:05.531462+00:00"},{"alias_kind":"pith_short_16","alias_value":"SDO3P7DNJFZQETPL","created_at":"2026-07-05T07:58:05.531462+00:00"},{"alias_kind":"pith_short_8","alias_value":"SDO3P7DN","created_at":"2026-07-05T07:58:05.531462+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03730","citing_title":"Beyond False Stability: High-Noise Drift Gating for Test-Time Adversarial Defenses in Vision-Language Models","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2502.05206","citing_title":"Safety at Scale: A Comprehensive Survey of Large Model and Agent Safety","ref_index":227,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SDO3P7DNJFZQETPLMN7JO7PD54","json":"https://pith.science/pith/SDO3P7DNJFZQETPLMN7JO7PD54.json","graph_json":"https://pith.science/api/pith-number/SDO3P7DNJFZQETPLMN7JO7PD54/graph.json","events_json":"https://pith.science/api/pith-number/SDO3P7DNJFZQETPLMN7JO7PD54/events.json","paper":"https://pith.science/paper/SDO3P7DN"},"agent_actions":{"view_html":"https://pith.science/pith/SDO3P7DNJFZQETPLMN7JO7PD54","download_json":"https://pith.science/pith/SDO3P7DNJFZQETPLMN7JO7PD54.json","view_paper":"https://pith.science/paper/SDO3P7DN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.12693&json=true","fetch_graph":"https://pith.science/api/pith-number/SDO3P7DNJFZQETPLMN7JO7PD54/graph.json","fetch_events":"https://pith.science/api/pith-number/SDO3P7DNJFZQETPLMN7JO7PD54/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SDO3P7DNJFZQETPLMN7JO7PD54/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SDO3P7DNJFZQETPLMN7JO7PD54/action/storage_attestation","attest_author":"https://pith.science/pith/SDO3P7DNJFZQETPLMN7JO7PD54/action/author_attestation","sign_citation":"https://pith.science/pith/SDO3P7DNJFZQETPLMN7JO7PD54/action/citation_signature","submit_replication":"https://pith.science/pith/SDO3P7DNJFZQETPLMN7JO7PD54/action/replication_record"}},"created_at":"2026-07-05T07:58:05.531462+00:00","updated_at":"2026-07-05T07:58:05.531462+00:00"}