{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:LO6W6GQ2LAK47HTW6UYUNDSTLV","short_pith_number":"pith:LO6W6GQ2","canonical_record":{"source":{"id":"2503.07384","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-10T14:32:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"560a0f3544309142d5b912fcf021af1209ed4b3ea8b061dace46a163d3fb3190","abstract_canon_sha256":"1aae26df01d41d72c3cbd395350d0e2c1c4abd8cd4c8b13f2cc16255f659b344"},"schema_version":"1.0"},"canonical_sha256":"5bbd6f1a1a5815cf9e76f531468e535d43a30f48de663a2d437e68f50505df7e","source":{"kind":"arxiv","id":"2503.07384","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.07384","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"arxiv_version","alias_value":"2503.07384v2","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.07384","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"pith_short_12","alias_value":"LO6W6GQ2LAK4","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"pith_short_16","alias_value":"LO6W6GQ2LAK47HTW","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"pith_short_8","alias_value":"LO6W6GQ2","created_at":"2026-07-05T10:30:33Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:LO6W6GQ2LAK47HTW6UYUNDSTLV","target":"record","payload":{"canonical_record":{"source":{"id":"2503.07384","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-10T14:32:56Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"560a0f3544309142d5b912fcf021af1209ed4b3ea8b061dace46a163d3fb3190","abstract_canon_sha256":"1aae26df01d41d72c3cbd395350d0e2c1c4abd8cd4c8b13f2cc16255f659b344"},"schema_version":"1.0"},"canonical_sha256":"5bbd6f1a1a5815cf9e76f531468e535d43a30f48de663a2d437e68f50505df7e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:30:33.109360Z","signature_b64":"0A4f3QeKALxxVIJNGr/IOyRAMVlzBOZZHwTA2PiOOpf7S6CHkZD/fqJH6T2m7IVghysvapDDTppyNa7VEZnBBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5bbd6f1a1a5815cf9e76f531468e535d43a30f48de663a2d437e68f50505df7e","last_reissued_at":"2026-07-05T10:30:33.108884Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:30:33.108884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2503.07384","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:30:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xJMnr0i6aShEXor3fPPIe5LKLgL5vw2o04Zxd+n1v21USa9vLMhOFnSs3VfkyvFajiGgxUFGuDEji0t3aHEDCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T17:41:45.908962Z"},"content_sha256":"b0ad03db36462ecd5b15ae76f16b850b8bd147abc7866d25213cc54cc32abc17","schema_version":"1.0","event_id":"sha256:b0ad03db36462ecd5b15ae76f16b850b8bd147abc7866d25213cc54cc32abc17"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:LO6W6GQ2LAK47HTW6UYUNDSTLV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Aythami Morales, Daniel DeAlcala, Gonzalo Mancera, Julian Fierrez, Ruben Tolosana","submitted_at":"2025-03-10T14:32:56Z","abstract_excerpt":"This work adapts and studies the gradient-based Membership Inference Test (gMINT) to the classification of text based on LLMs. MINT is a general approach intended to determine if given data was used for training machine learning models, and this work focuses on its application to the domain of Natural Language Processing. Using gradient-based analysis, the MINT model identifies whether particular data samples were included during the language model training phase, addressing growing concerns about data privacy in machine learning. The method was evaluated in seven Transformer-based models and "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.07384","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/2503.07384/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T10:30:33Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Q7aPJMJiSZWs5ICA4X23DyuLSHGz6ZYFTB7i3V99a0zDci58d7Np1hLEmBDz8Ss5d0AJSbe/LGShUZktuMKOCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T17:41:45.909980Z"},"content_sha256":"34fb7fa4f6b16230d95e0cb58cb3b1806f6913481befe84b8f6945a2fdafb493","schema_version":"1.0","event_id":"sha256:34fb7fa4f6b16230d95e0cb58cb3b1806f6913481befe84b8f6945a2fdafb493"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/LO6W6GQ2LAK47HTW6UYUNDSTLV/bundle.json","state_url":"https://pith.science/pith/LO6W6GQ2LAK47HTW6UYUNDSTLV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/LO6W6GQ2LAK47HTW6UYUNDSTLV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-08T17:41:45Z","links":{"resolver":"https://pith.science/pith/LO6W6GQ2LAK47HTW6UYUNDSTLV","bundle":"https://pith.science/pith/LO6W6GQ2LAK47HTW6UYUNDSTLV/bundle.json","state":"https://pith.science/pith/LO6W6GQ2LAK47HTW6UYUNDSTLV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/LO6W6GQ2LAK47HTW6UYUNDSTLV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:LO6W6GQ2LAK47HTW6UYUNDSTLV","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1aae26df01d41d72c3cbd395350d0e2c1c4abd8cd4c8b13f2cc16255f659b344","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-10T14:32:56Z","title_canon_sha256":"560a0f3544309142d5b912fcf021af1209ed4b3ea8b061dace46a163d3fb3190"},"schema_version":"1.0","source":{"id":"2503.07384","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2503.07384","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"arxiv_version","alias_value":"2503.07384v2","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.07384","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"pith_short_12","alias_value":"LO6W6GQ2LAK4","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"pith_short_16","alias_value":"LO6W6GQ2LAK47HTW","created_at":"2026-07-05T10:30:33Z"},{"alias_kind":"pith_short_8","alias_value":"LO6W6GQ2","created_at":"2026-07-05T10:30:33Z"}],"graph_snapshots":[{"event_id":"sha256:34fb7fa4f6b16230d95e0cb58cb3b1806f6913481befe84b8f6945a2fdafb493","target":"graph","created_at":"2026-07-05T10:30:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2503.07384/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This work adapts and studies the gradient-based Membership Inference Test (gMINT) to the classification of text based on LLMs. MINT is a general approach intended to determine if given data was used for training machine learning models, and this work focuses on its application to the domain of Natural Language Processing. Using gradient-based analysis, the MINT model identifies whether particular data samples were included during the language model training phase, addressing growing concerns about data privacy in machine learning. The method was evaluated in seven Transformer-based models and ","authors_text":"Aythami Morales, Daniel DeAlcala, Gonzalo Mancera, Julian Fierrez, Ruben Tolosana","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-10T14:32:56Z","title":"Is My Text in Your AI Model? Gradient-based Membership Inference Test applied to LLMs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.07384","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:b0ad03db36462ecd5b15ae76f16b850b8bd147abc7866d25213cc54cc32abc17","target":"record","created_at":"2026-07-05T10:30:33Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1aae26df01d41d72c3cbd395350d0e2c1c4abd8cd4c8b13f2cc16255f659b344","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CL","submitted_at":"2025-03-10T14:32:56Z","title_canon_sha256":"560a0f3544309142d5b912fcf021af1209ed4b3ea8b061dace46a163d3fb3190"},"schema_version":"1.0","source":{"id":"2503.07384","kind":"arxiv","version":2}},"canonical_sha256":"5bbd6f1a1a5815cf9e76f531468e535d43a30f48de663a2d437e68f50505df7e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5bbd6f1a1a5815cf9e76f531468e535d43a30f48de663a2d437e68f50505df7e","first_computed_at":"2026-07-05T10:30:33.108884Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:30:33.108884Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0A4f3QeKALxxVIJNGr/IOyRAMVlzBOZZHwTA2PiOOpf7S6CHkZD/fqJH6T2m7IVghysvapDDTppyNa7VEZnBBA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:30:33.109360Z","signed_message":"canonical_sha256_bytes"},"source_id":"2503.07384","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b0ad03db36462ecd5b15ae76f16b850b8bd147abc7866d25213cc54cc32abc17","sha256:34fb7fa4f6b16230d95e0cb58cb3b1806f6913481befe84b8f6945a2fdafb493"],"state_sha256":"3a4339aa4f526b54ea176f43b79df9e7cf05e0fc75086299dd4a3cab29aebd5f"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xdNia7HfvmXcP8L2Wr9RyeSn4mF8yTSr9do98HQbiHU9ClLxDWQM0WtLUqJVHAyFE8xEi3+jOUx4eng+mHvWCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T17:41:45.915916Z","bundle_sha256":"6c5b003b5c26ee8b4d9f1fbafc6594e4cd0e1c6ad334c1e30e98f6506ac6eaa6"}}