{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:JDNRQ4CCKXJSLSF73RK4VMHKZJ","short_pith_number":"pith:JDNRQ4CC","schema_version":"1.0","canonical_sha256":"48db18704255d325c8bfdc55cab0eaca56eaa83ea4dc1df69ed4d0cae9a6c69d","source":{"kind":"arxiv","id":"2509.25448","version":3},"attestation_state":"computed","paper":{"title":"Fingerprinting LLMs via Prompt Injection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CR","authors_text":"Cheng Hong, Mengyuan Li, Neil Gong, Osama Ahmed, Yuepeng Hu, Zhengyuan Jiang, Zhicong Huang","submitted_at":"2025-09-29T19:54:36Z","abstract_excerpt":"Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization, which makes it challenging to determine whether one model is derived from another. Existing provenance detection methods have two main limitations: (1) they embed signals into the base model before release, which is infeasible for already published models, or (2) they compare outputs across models using hand-crafted or random prompts, which are not robust to post-processing. In this work, we propose LLMPrint, a novel detection framework that constructs fingerprints by ex"},"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":"2509.25448","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2025-09-29T19:54:36Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"c19c28db0a4a773d374595fa34879bf8df26f96ed8732fc18af4c3d2142f254b","abstract_canon_sha256":"a5a1a2c172c3e3c503a9ca9ed3e529109ee2a1d2256f6541687b8105a80a1018"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:04:58.766883Z","signature_b64":"BQPbzMMxnzhhTSNL2q+FF2LetXvZ0w+4J5yrWbjZsdSg48Pko6TILxlAsdpblnU2BpYoGB+HLpT1yJbZCoiNBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"48db18704255d325c8bfdc55cab0eaca56eaa83ea4dc1df69ed4d0cae9a6c69d","last_reissued_at":"2026-05-20T01:04:58.766082Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:04:58.766082Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Fingerprinting LLMs via Prompt Injection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CR","authors_text":"Cheng Hong, Mengyuan Li, Neil Gong, Osama Ahmed, Yuepeng Hu, Zhengyuan Jiang, Zhicong Huang","submitted_at":"2025-09-29T19:54:36Z","abstract_excerpt":"Large language models (LLMs) are often modified after release through post-processing such as post-training or quantization, which makes it challenging to determine whether one model is derived from another. Existing provenance detection methods have two main limitations: (1) they embed signals into the base model before release, which is infeasible for already published models, or (2) they compare outputs across models using hand-crafted or random prompts, which are not robust to post-processing. In this work, we propose LLMPrint, a novel detection framework that constructs fingerprints by ex"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.25448","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/2509.25448/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":"2509.25448","created_at":"2026-05-20T01:04:58.766177+00:00"},{"alias_kind":"arxiv_version","alias_value":"2509.25448v3","created_at":"2026-05-20T01:04:58.766177+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.25448","created_at":"2026-05-20T01:04:58.766177+00:00"},{"alias_kind":"pith_short_12","alias_value":"JDNRQ4CCKXJS","created_at":"2026-05-20T01:04:58.766177+00:00"},{"alias_kind":"pith_short_16","alias_value":"JDNRQ4CCKXJSLSF7","created_at":"2026-05-20T01:04:58.766177+00:00"},{"alias_kind":"pith_short_8","alias_value":"JDNRQ4CC","created_at":"2026-05-20T01:04:58.766177+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JDNRQ4CCKXJSLSF73RK4VMHKZJ","json":"https://pith.science/pith/JDNRQ4CCKXJSLSF73RK4VMHKZJ.json","graph_json":"https://pith.science/api/pith-number/JDNRQ4CCKXJSLSF73RK4VMHKZJ/graph.json","events_json":"https://pith.science/api/pith-number/JDNRQ4CCKXJSLSF73RK4VMHKZJ/events.json","paper":"https://pith.science/paper/JDNRQ4CC"},"agent_actions":{"view_html":"https://pith.science/pith/JDNRQ4CCKXJSLSF73RK4VMHKZJ","download_json":"https://pith.science/pith/JDNRQ4CCKXJSLSF73RK4VMHKZJ.json","view_paper":"https://pith.science/paper/JDNRQ4CC","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2509.25448&json=true","fetch_graph":"https://pith.science/api/pith-number/JDNRQ4CCKXJSLSF73RK4VMHKZJ/graph.json","fetch_events":"https://pith.science/api/pith-number/JDNRQ4CCKXJSLSF73RK4VMHKZJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JDNRQ4CCKXJSLSF73RK4VMHKZJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JDNRQ4CCKXJSLSF73RK4VMHKZJ/action/storage_attestation","attest_author":"https://pith.science/pith/JDNRQ4CCKXJSLSF73RK4VMHKZJ/action/author_attestation","sign_citation":"https://pith.science/pith/JDNRQ4CCKXJSLSF73RK4VMHKZJ/action/citation_signature","submit_replication":"https://pith.science/pith/JDNRQ4CCKXJSLSF73RK4VMHKZJ/action/replication_record"}},"created_at":"2026-05-20T01:04:58.766177+00:00","updated_at":"2026-05-20T01:04:58.766177+00:00"}