{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:63Y5Q4PSMGSLFAGOVBCJPMMMUU","short_pith_number":"pith:63Y5Q4PS","schema_version":"1.0","canonical_sha256":"f6f1d871f261a4b280cea84497b18ca514a2281cf257a46c0aaf89c752488cd7","source":{"kind":"arxiv","id":"2501.18624","version":2},"attestation_state":"computed","paper":{"title":"Membership Inference Attacks Against Vision-Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Chun Chen, Kui Ren, Yang Zhang, Yuke Hu, Zhan Qin, Zheng Li, Zhihao Liu","submitted_at":"2025-01-27T05:44:58Z","abstract_excerpt":"Vision-Language Models (VLMs), built on pre-trained vision encoders and large language models (LLMs), have shown exceptional multi-modal understanding and dialog capabilities, positioning them as catalysts for the next technological revolution. However, while most VLM research focuses on enhancing multi-modal interaction, the risks of data misuse and leakage have been largely unexplored. This prompts the need for a comprehensive investigation of such risks in VLMs. In this paper, we conduct the first analysis of misuse and leakage detection in VLMs through the lens of membership inference atta"},"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":"2501.18624","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-01-27T05:44:58Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"e36c8eda08547ad79aebb852727be10929680c0c36b0f82b938e5c899792a4ab","abstract_canon_sha256":"581d2d1e53ebf80aab45b97c11d98e9cbdec82307c4cf2b0a1fbaeccca23581a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:10:48.517380Z","signature_b64":"51l+1jt7ALdywF0PbdoGUHuvgqQmKD9+nJCGL+O/WndCRIk2/h88L9Qj/kF+cCl6TvSdpFhtPAUvbiSO2ZcnAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f6f1d871f261a4b280cea84497b18ca514a2281cf257a46c0aaf89c752488cd7","last_reissued_at":"2026-07-05T10:10:48.516875Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:10:48.516875Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Membership Inference Attacks Against Vision-Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Chun Chen, Kui Ren, Yang Zhang, Yuke Hu, Zhan Qin, Zheng Li, Zhihao Liu","submitted_at":"2025-01-27T05:44:58Z","abstract_excerpt":"Vision-Language Models (VLMs), built on pre-trained vision encoders and large language models (LLMs), have shown exceptional multi-modal understanding and dialog capabilities, positioning them as catalysts for the next technological revolution. However, while most VLM research focuses on enhancing multi-modal interaction, the risks of data misuse and leakage have been largely unexplored. This prompts the need for a comprehensive investigation of such risks in VLMs. In this paper, we conduct the first analysis of misuse and leakage detection in VLMs through the lens of membership inference atta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.18624","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/2501.18624/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":"2501.18624","created_at":"2026-07-05T10:10:48.516934+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.18624v2","created_at":"2026-07-05T10:10:48.516934+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.18624","created_at":"2026-07-05T10:10:48.516934+00:00"},{"alias_kind":"pith_short_12","alias_value":"63Y5Q4PSMGSL","created_at":"2026-07-05T10:10:48.516934+00:00"},{"alias_kind":"pith_short_16","alias_value":"63Y5Q4PSMGSLFAGO","created_at":"2026-07-05T10:10:48.516934+00:00"},{"alias_kind":"pith_short_8","alias_value":"63Y5Q4PS","created_at":"2026-07-05T10:10:48.516934+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.16090","citing_title":"A Cross-Modal Prompt Injection Attack against Large Vision-Language Models with Image-Only Perturbation","ref_index":17,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/63Y5Q4PSMGSLFAGOVBCJPMMMUU","json":"https://pith.science/pith/63Y5Q4PSMGSLFAGOVBCJPMMMUU.json","graph_json":"https://pith.science/api/pith-number/63Y5Q4PSMGSLFAGOVBCJPMMMUU/graph.json","events_json":"https://pith.science/api/pith-number/63Y5Q4PSMGSLFAGOVBCJPMMMUU/events.json","paper":"https://pith.science/paper/63Y5Q4PS"},"agent_actions":{"view_html":"https://pith.science/pith/63Y5Q4PSMGSLFAGOVBCJPMMMUU","download_json":"https://pith.science/pith/63Y5Q4PSMGSLFAGOVBCJPMMMUU.json","view_paper":"https://pith.science/paper/63Y5Q4PS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.18624&json=true","fetch_graph":"https://pith.science/api/pith-number/63Y5Q4PSMGSLFAGOVBCJPMMMUU/graph.json","fetch_events":"https://pith.science/api/pith-number/63Y5Q4PSMGSLFAGOVBCJPMMMUU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/63Y5Q4PSMGSLFAGOVBCJPMMMUU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/63Y5Q4PSMGSLFAGOVBCJPMMMUU/action/storage_attestation","attest_author":"https://pith.science/pith/63Y5Q4PSMGSLFAGOVBCJPMMMUU/action/author_attestation","sign_citation":"https://pith.science/pith/63Y5Q4PSMGSLFAGOVBCJPMMMUU/action/citation_signature","submit_replication":"https://pith.science/pith/63Y5Q4PSMGSLFAGOVBCJPMMMUU/action/replication_record"}},"created_at":"2026-07-05T10:10:48.516934+00:00","updated_at":"2026-07-05T10:10:48.516934+00:00"}