{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:INDPCBGX2LK5FJMQKRD6GOPZTD","short_pith_number":"pith:INDPCBGX","schema_version":"1.0","canonical_sha256":"4346f104d7d2d5d2a5905447e339f998d33a24de9cc2a5562054590ae2a41048","source":{"kind":"arxiv","id":"2606.05725","version":1},"attestation_state":"computed","paper":{"title":"An Embarrassingly Simple Detector for Model Extraction Attacks in Large Language Model API Traffic","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CR","authors_text":"Qianwen Guo, Shuze Liu, Yushun Dong","submitted_at":"2026-06-04T05:33:49Z","abstract_excerpt":"Large language models (LLMs) are increasingly deployed through hosted APIs, making model extraction a practical threat to model ownership and service security. However, individual extraction queries often resemble benign requests, and existing evaluations often focus on single-query anomaly scoring or pure benign-versus-attacker user settings. We formulate model extraction monitoring as benign-calibrated traffic-window distribution testing and show that an embarrassingly simple detector is effective: embed incoming queries into a semantic space and test whether their aggregate distribution dev"},"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":"2606.05725","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2026-06-04T05:33:49Z","cross_cats_sorted":["cs.CL"],"title_canon_sha256":"f4c1d8c583d03fcbc6d4f8e45cce689e7a704995f8e22b3643b752311941b99b","abstract_canon_sha256":"87ea2e6a065d69e523bd23cf62bda6b7bf62b10005b18ecd8cbbe818110316a1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-05T01:15:00.825029Z","signature_b64":"GauyTnV1w3Lz7NN01fnCEwCUFRy49fmlr6cipohsT8Y5Mi56QrfcGFHjMktDd8TZ6or16MrMVreaGwnS9qF0Bw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4346f104d7d2d5d2a5905447e339f998d33a24de9cc2a5562054590ae2a41048","last_reissued_at":"2026-06-05T01:15:00.824475Z","signature_status":"signed_v1","first_computed_at":"2026-06-05T01:15:00.824475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An Embarrassingly Simple Detector for Model Extraction Attacks in Large Language Model API Traffic","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CL"],"primary_cat":"cs.CR","authors_text":"Qianwen Guo, Shuze Liu, Yushun Dong","submitted_at":"2026-06-04T05:33:49Z","abstract_excerpt":"Large language models (LLMs) are increasingly deployed through hosted APIs, making model extraction a practical threat to model ownership and service security. However, individual extraction queries often resemble benign requests, and existing evaluations often focus on single-query anomaly scoring or pure benign-versus-attacker user settings. We formulate model extraction monitoring as benign-calibrated traffic-window distribution testing and show that an embarrassingly simple detector is effective: embed incoming queries into a semantic space and test whether their aggregate distribution dev"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.05725","kind":"arxiv","version":1},"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/2606.05725/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":"2606.05725","created_at":"2026-06-05T01:15:00.824550+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.05725v1","created_at":"2026-06-05T01:15:00.824550+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.05725","created_at":"2026-06-05T01:15:00.824550+00:00"},{"alias_kind":"pith_short_12","alias_value":"INDPCBGX2LK5","created_at":"2026-06-05T01:15:00.824550+00:00"},{"alias_kind":"pith_short_16","alias_value":"INDPCBGX2LK5FJMQ","created_at":"2026-06-05T01:15:00.824550+00:00"},{"alias_kind":"pith_short_8","alias_value":"INDPCBGX","created_at":"2026-06-05T01:15:00.824550+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/INDPCBGX2LK5FJMQKRD6GOPZTD","json":"https://pith.science/pith/INDPCBGX2LK5FJMQKRD6GOPZTD.json","graph_json":"https://pith.science/api/pith-number/INDPCBGX2LK5FJMQKRD6GOPZTD/graph.json","events_json":"https://pith.science/api/pith-number/INDPCBGX2LK5FJMQKRD6GOPZTD/events.json","paper":"https://pith.science/paper/INDPCBGX"},"agent_actions":{"view_html":"https://pith.science/pith/INDPCBGX2LK5FJMQKRD6GOPZTD","download_json":"https://pith.science/pith/INDPCBGX2LK5FJMQKRD6GOPZTD.json","view_paper":"https://pith.science/paper/INDPCBGX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.05725&json=true","fetch_graph":"https://pith.science/api/pith-number/INDPCBGX2LK5FJMQKRD6GOPZTD/graph.json","fetch_events":"https://pith.science/api/pith-number/INDPCBGX2LK5FJMQKRD6GOPZTD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/INDPCBGX2LK5FJMQKRD6GOPZTD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/INDPCBGX2LK5FJMQKRD6GOPZTD/action/storage_attestation","attest_author":"https://pith.science/pith/INDPCBGX2LK5FJMQKRD6GOPZTD/action/author_attestation","sign_citation":"https://pith.science/pith/INDPCBGX2LK5FJMQKRD6GOPZTD/action/citation_signature","submit_replication":"https://pith.science/pith/INDPCBGX2LK5FJMQKRD6GOPZTD/action/replication_record"}},"created_at":"2026-06-05T01:15:00.824550+00:00","updated_at":"2026-06-05T01:15:00.824550+00:00"}