{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:AFD52LUVBHKWUW6QQ7IW5FJTHH","short_pith_number":"pith:AFD52LUV","schema_version":"1.0","canonical_sha256":"0147dd2e9509d56a5bd087d16e953339cdc84ae7f187dea79931a449235163dd","source":{"kind":"arxiv","id":"2408.00523","version":3},"attestation_state":"computed","paper":{"title":"Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Ning Yu, Shanqing Guo, Xiangtao Meng, Yingkai Dong, Zheng Li","submitted_at":"2024-08-01T12:54:46Z","abstract_excerpt":"Text-to-image (T2I) generative models have revolutionized content creation by transforming textual descriptions into high-quality images. However, these models are vulnerable to jailbreaking attacks, where carefully crafted prompts bypass safety mechanisms to produce unsafe content. While researchers have developed various jailbreak attacks to expose this risk, these methods face significant limitations, including impractical access requirements, easily detectable unnatural prompts, restricted search spaces, and high query demands on the target system. In this paper, we propose JailFuzzer, a n"},"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":"2408.00523","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-08-01T12:54:46Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"63818208d0d762641321cf826e49c8fc2fee8df0b4849d3143b3a43608026258","abstract_canon_sha256":"38dc4d069c9589bce98103bd675d655f90804ccdc8ca8809b5ed5063e270b20d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:26:43.284308Z","signature_b64":"bibOYzKeNGWdacyKF97JnfW3q/XBdSmHv2U4vuPCKyIlA3ljyFh5RhKP3dDacxQj8Bswk5Mje/a6lAGu0b/lDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0147dd2e9509d56a5bd087d16e953339cdc84ae7f187dea79931a449235163dd","last_reissued_at":"2026-07-05T11:26:43.283820Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:26:43.283820Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fuzz-Testing Meets LLM-Based Agents: An Automated and Efficient Framework for Jailbreaking Text-To-Image Generation Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Ning Yu, Shanqing Guo, Xiangtao Meng, Yingkai Dong, Zheng Li","submitted_at":"2024-08-01T12:54:46Z","abstract_excerpt":"Text-to-image (T2I) generative models have revolutionized content creation by transforming textual descriptions into high-quality images. However, these models are vulnerable to jailbreaking attacks, where carefully crafted prompts bypass safety mechanisms to produce unsafe content. While researchers have developed various jailbreak attacks to expose this risk, these methods face significant limitations, including impractical access requirements, easily detectable unnatural prompts, restricted search spaces, and high query demands on the target system. In this paper, we propose JailFuzzer, a n"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2408.00523","kind":"arxiv","version":3},"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/2408.00523/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":"2408.00523","created_at":"2026-07-05T11:26:43.283876+00:00"},{"alias_kind":"arxiv_version","alias_value":"2408.00523v3","created_at":"2026-07-05T11:26:43.283876+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2408.00523","created_at":"2026-07-05T11:26:43.283876+00:00"},{"alias_kind":"pith_short_12","alias_value":"AFD52LUVBHKW","created_at":"2026-07-05T11:26:43.283876+00:00"},{"alias_kind":"pith_short_16","alias_value":"AFD52LUVBHKWUW6Q","created_at":"2026-07-05T11:26:43.283876+00:00"},{"alias_kind":"pith_short_8","alias_value":"AFD52LUV","created_at":"2026-07-05T11:26:43.283876+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":5,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.26332","citing_title":"Erased but Exploitable: Black-box Embedding-Aware Prompting Against Unlearned Text-to-Image Diffusion Models","ref_index":9,"is_internal_anchor":false},{"citing_arxiv_id":"2606.01481","citing_title":"SafeGen-Bench: Benchmarking Safety in Image-Conditioned Text-to-Video Generation","ref_index":12,"is_internal_anchor":false},{"citing_arxiv_id":"2503.21460","citing_title":"Large Language Model Agent: A Survey on Methodology, Applications and Challenges","ref_index":185,"is_internal_anchor":false},{"citing_arxiv_id":"2505.16120","citing_title":"LLM-Powered AI Agent Systems and Their Applications in Industry","ref_index":114,"is_internal_anchor":false},{"citing_arxiv_id":"2604.11309","citing_title":"The Salami Slicing Threat: Exploiting Cumulative Risks in LLM Systems","ref_index":44,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/AFD52LUVBHKWUW6QQ7IW5FJTHH","json":"https://pith.science/pith/AFD52LUVBHKWUW6QQ7IW5FJTHH.json","graph_json":"https://pith.science/api/pith-number/AFD52LUVBHKWUW6QQ7IW5FJTHH/graph.json","events_json":"https://pith.science/api/pith-number/AFD52LUVBHKWUW6QQ7IW5FJTHH/events.json","paper":"https://pith.science/paper/AFD52LUV"},"agent_actions":{"view_html":"https://pith.science/pith/AFD52LUVBHKWUW6QQ7IW5FJTHH","download_json":"https://pith.science/pith/AFD52LUVBHKWUW6QQ7IW5FJTHH.json","view_paper":"https://pith.science/paper/AFD52LUV","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2408.00523&json=true","fetch_graph":"https://pith.science/api/pith-number/AFD52LUVBHKWUW6QQ7IW5FJTHH/graph.json","fetch_events":"https://pith.science/api/pith-number/AFD52LUVBHKWUW6QQ7IW5FJTHH/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/AFD52LUVBHKWUW6QQ7IW5FJTHH/action/timestamp_anchor","attest_storage":"https://pith.science/pith/AFD52LUVBHKWUW6QQ7IW5FJTHH/action/storage_attestation","attest_author":"https://pith.science/pith/AFD52LUVBHKWUW6QQ7IW5FJTHH/action/author_attestation","sign_citation":"https://pith.science/pith/AFD52LUVBHKWUW6QQ7IW5FJTHH/action/citation_signature","submit_replication":"https://pith.science/pith/AFD52LUVBHKWUW6QQ7IW5FJTHH/action/replication_record"}},"created_at":"2026-07-05T11:26:43.283876+00:00","updated_at":"2026-07-05T11:26:43.283876+00:00"}