{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:4GJKQB6D7LCOQYXKOZQR4JCR74","short_pith_number":"pith:4GJKQB6D","schema_version":"1.0","canonical_sha256":"e192a807c3fac4e862ea76611e2451ff087dee0b8f92041f4a9e56d026e53aa1","source":{"kind":"arxiv","id":"2506.12551","version":2},"attestation_state":"computed","paper":{"title":"MEraser: An Effective Fingerprint Erasure Approach for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Jingxuan Zhang, Meng Han, Rui Hu, Wenpeng Xing, Xuhong Zhang, Zhenhua Xu","submitted_at":"2025-06-14T15:48:53Z","abstract_excerpt":"Large Language Models (LLMs) have become increasingly prevalent across various sectors, raising critical concerns about model ownership and intellectual property protection. Although backdoor-based fingerprinting has emerged as a promising solution for model authentication, effective attacks for removing these fingerprints remain largely unexplored. Therefore, we present Mismatched Eraser (MEraser), a novel method for effectively removing backdoor-based fingerprints from LLMs while maintaining model performance. Our approach leverages a two-phase fine-tuning strategy utilizing carefully constr"},"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":"2506.12551","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-06-14T15:48:53Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"1ee61011ad3b7a14b368316d153cf755141cefe77bbd794368f4ad6cfa262bbd","abstract_canon_sha256":"789e5fe9f85a020639fc0c42a10566980e6097276987a0ca47a6d55764c811b5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:00:02.399538Z","signature_b64":"n78XD8Q/DFYStuSjF0DOa04ndIwkA8Q9eDv6cp/ICE9oxZ3aCX+8favvVPqzrOuOla9vmGY5+OXhRQ3WubbmBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e192a807c3fac4e862ea76611e2451ff087dee0b8f92041f4a9e56d026e53aa1","last_reissued_at":"2026-07-05T12:00:02.399023Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:00:02.399023Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MEraser: An Effective Fingerprint Erasure Approach for Large Language Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Jingxuan Zhang, Meng Han, Rui Hu, Wenpeng Xing, Xuhong Zhang, Zhenhua Xu","submitted_at":"2025-06-14T15:48:53Z","abstract_excerpt":"Large Language Models (LLMs) have become increasingly prevalent across various sectors, raising critical concerns about model ownership and intellectual property protection. Although backdoor-based fingerprinting has emerged as a promising solution for model authentication, effective attacks for removing these fingerprints remain largely unexplored. Therefore, we present Mismatched Eraser (MEraser), a novel method for effectively removing backdoor-based fingerprints from LLMs while maintaining model performance. Our approach leverages a two-phase fine-tuning strategy utilizing carefully constr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.12551","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/2506.12551/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":"2506.12551","created_at":"2026-07-05T12:00:02.399084+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.12551v2","created_at":"2026-07-05T12:00:02.399084+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.12551","created_at":"2026-07-05T12:00:02.399084+00:00"},{"alias_kind":"pith_short_12","alias_value":"4GJKQB6D7LCO","created_at":"2026-07-05T12:00:02.399084+00:00"},{"alias_kind":"pith_short_16","alias_value":"4GJKQB6D7LCOQYXK","created_at":"2026-07-05T12:00:02.399084+00:00"},{"alias_kind":"pith_short_8","alias_value":"4GJKQB6D","created_at":"2026-07-05T12:00:02.399084+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.11548","citing_title":"Copyright Protection for Large Language Models: A Survey of Methods, Challenges, and Trends","ref_index":181,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4GJKQB6D7LCOQYXKOZQR4JCR74","json":"https://pith.science/pith/4GJKQB6D7LCOQYXKOZQR4JCR74.json","graph_json":"https://pith.science/api/pith-number/4GJKQB6D7LCOQYXKOZQR4JCR74/graph.json","events_json":"https://pith.science/api/pith-number/4GJKQB6D7LCOQYXKOZQR4JCR74/events.json","paper":"https://pith.science/paper/4GJKQB6D"},"agent_actions":{"view_html":"https://pith.science/pith/4GJKQB6D7LCOQYXKOZQR4JCR74","download_json":"https://pith.science/pith/4GJKQB6D7LCOQYXKOZQR4JCR74.json","view_paper":"https://pith.science/paper/4GJKQB6D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.12551&json=true","fetch_graph":"https://pith.science/api/pith-number/4GJKQB6D7LCOQYXKOZQR4JCR74/graph.json","fetch_events":"https://pith.science/api/pith-number/4GJKQB6D7LCOQYXKOZQR4JCR74/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4GJKQB6D7LCOQYXKOZQR4JCR74/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4GJKQB6D7LCOQYXKOZQR4JCR74/action/storage_attestation","attest_author":"https://pith.science/pith/4GJKQB6D7LCOQYXKOZQR4JCR74/action/author_attestation","sign_citation":"https://pith.science/pith/4GJKQB6D7LCOQYXKOZQR4JCR74/action/citation_signature","submit_replication":"https://pith.science/pith/4GJKQB6D7LCOQYXKOZQR4JCR74/action/replication_record"}},"created_at":"2026-07-05T12:00:02.399084+00:00","updated_at":"2026-07-05T12:00:02.399084+00:00"}