{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:QSCMUGVZTQVYDUN5YIK5D66CTN","short_pith_number":"pith:QSCMUGVZ","canonical_record":{"source":{"id":"2506.05867","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2025-06-06T08:34:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4dd732d205db2854da493b5ce1d4467729738323f9974179213acd0a2f6b9870","abstract_canon_sha256":"916dfbae6b6df03b2371d66fa53e336e97f7ac26fbb2febcb1eb5b597f2b8f8b"},"schema_version":"1.0"},"canonical_sha256":"8484ca1ab99c2b81d1bdc215d1fbc29b602683bdd2a240832d5b60a265faadec","source":{"kind":"arxiv","id":"2506.05867","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.05867","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"arxiv_version","alias_value":"2506.05867v1","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05867","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"pith_short_12","alias_value":"QSCMUGVZTQVY","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"pith_short_16","alias_value":"QSCMUGVZTQVYDUN5","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"pith_short_8","alias_value":"QSCMUGVZ","created_at":"2026-07-05T11:17:11Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:QSCMUGVZTQVYDUN5YIK5D66CTN","target":"record","payload":{"canonical_record":{"source":{"id":"2506.05867","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2025-06-06T08:34:00Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"4dd732d205db2854da493b5ce1d4467729738323f9974179213acd0a2f6b9870","abstract_canon_sha256":"916dfbae6b6df03b2371d66fa53e336e97f7ac26fbb2febcb1eb5b597f2b8f8b"},"schema_version":"1.0"},"canonical_sha256":"8484ca1ab99c2b81d1bdc215d1fbc29b602683bdd2a240832d5b60a265faadec","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:17:11.676644Z","signature_b64":"TJtsMZ98u245/gz5uvv6Q/iFbQ0xER2pIn/MdO6U1xTqw6hJv4Oi8Jugpo+NjsPIuPV9iPBnKVbeLGJY+iqoBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8484ca1ab99c2b81d1bdc215d1fbc29b602683bdd2a240832d5b60a265faadec","last_reissued_at":"2026-07-05T11:17:11.676134Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:17:11.676134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.05867","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T11:17:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HFKcIFARy2hhvWFAgdaeHCpgs4QW91OJOaT8FVGYVb/UQOH7LhgWt1V7lekBzXowsLPSBuTvtioJaBH/8O38DQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:20:18.782425Z"},"content_sha256":"d4b8cfecdae9d6a7ac876e20e3ac8813a54a96b6b5a5204b9ea7252b4eeb92eb","schema_version":"1.0","event_id":"sha256:d4b8cfecdae9d6a7ac876e20e3ac8813a54a96b6b5a5204b9ea7252b4eeb92eb"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:QSCMUGVZTQVYDUN5YIK5D66CTN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Stealix: Model Stealing via Prompt Evolution","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CR","authors_text":"Hui-Po Wang, Maria-Irina Nicolae, Mario Fritz, Zhixiong Zhuang","submitted_at":"2025-06-06T08:34:00Z","abstract_excerpt":"Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing intellectual property and exposing sensitive information. Recent methods that use pre-trained diffusion models for data synthesis improve efficiency and performance but rely heavily on manually crafted prompts, limiting automation and scalability, especially for attackers with little expertise. To assess the risks posed by open-source pre-trained models, we propose a more realistic threat model that eliminates the need "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05867","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/2506.05867/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":"2026-07-05T11:17:11Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FJ87m6ECh4znNHW9H2c7GPx99fGV5Go65AhTD4oab29YzRnP5fPwaj9CnwjUvLW2DSA3CmMwFp1MBYPX/7+tAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T00:20:18.782942Z"},"content_sha256":"7860e7547956bdfc3a3451ab973df6b2a70c4a7dd3a360007dad5f1d99a233bc","schema_version":"1.0","event_id":"sha256:7860e7547956bdfc3a3451ab973df6b2a70c4a7dd3a360007dad5f1d99a233bc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QSCMUGVZTQVYDUN5YIK5D66CTN/bundle.json","state_url":"https://pith.science/pith/QSCMUGVZTQVYDUN5YIK5D66CTN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QSCMUGVZTQVYDUN5YIK5D66CTN/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":"2026-08-09T00:20:18Z","links":{"resolver":"https://pith.science/pith/QSCMUGVZTQVYDUN5YIK5D66CTN","bundle":"https://pith.science/pith/QSCMUGVZTQVYDUN5YIK5D66CTN/bundle.json","state":"https://pith.science/pith/QSCMUGVZTQVYDUN5YIK5D66CTN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QSCMUGVZTQVYDUN5YIK5D66CTN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:QSCMUGVZTQVYDUN5YIK5D66CTN","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":"916dfbae6b6df03b2371d66fa53e336e97f7ac26fbb2febcb1eb5b597f2b8f8b","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2025-06-06T08:34:00Z","title_canon_sha256":"4dd732d205db2854da493b5ce1d4467729738323f9974179213acd0a2f6b9870"},"schema_version":"1.0","source":{"id":"2506.05867","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.05867","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"arxiv_version","alias_value":"2506.05867v1","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.05867","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"pith_short_12","alias_value":"QSCMUGVZTQVY","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"pith_short_16","alias_value":"QSCMUGVZTQVYDUN5","created_at":"2026-07-05T11:17:11Z"},{"alias_kind":"pith_short_8","alias_value":"QSCMUGVZ","created_at":"2026-07-05T11:17:11Z"}],"graph_snapshots":[{"event_id":"sha256:7860e7547956bdfc3a3451ab973df6b2a70c4a7dd3a360007dad5f1d99a233bc","target":"graph","created_at":"2026-07-05T11:17:11Z","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/2506.05867/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Model stealing poses a significant security risk in machine learning by enabling attackers to replicate a black-box model without access to its training data, thus jeopardizing intellectual property and exposing sensitive information. Recent methods that use pre-trained diffusion models for data synthesis improve efficiency and performance but rely heavily on manually crafted prompts, limiting automation and scalability, especially for attackers with little expertise. To assess the risks posed by open-source pre-trained models, we propose a more realistic threat model that eliminates the need ","authors_text":"Hui-Po Wang, Maria-Irina Nicolae, Mario Fritz, Zhixiong Zhuang","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2025-06-06T08:34:00Z","title":"Stealix: Model Stealing via Prompt Evolution"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.05867","kind":"arxiv","version":1},"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:d4b8cfecdae9d6a7ac876e20e3ac8813a54a96b6b5a5204b9ea7252b4eeb92eb","target":"record","created_at":"2026-07-05T11:17:11Z","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":"916dfbae6b6df03b2371d66fa53e336e97f7ac26fbb2febcb1eb5b597f2b8f8b","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CR","submitted_at":"2025-06-06T08:34:00Z","title_canon_sha256":"4dd732d205db2854da493b5ce1d4467729738323f9974179213acd0a2f6b9870"},"schema_version":"1.0","source":{"id":"2506.05867","kind":"arxiv","version":1}},"canonical_sha256":"8484ca1ab99c2b81d1bdc215d1fbc29b602683bdd2a240832d5b60a265faadec","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8484ca1ab99c2b81d1bdc215d1fbc29b602683bdd2a240832d5b60a265faadec","first_computed_at":"2026-07-05T11:17:11.676134Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:17:11.676134Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TJtsMZ98u245/gz5uvv6Q/iFbQ0xER2pIn/MdO6U1xTqw6hJv4Oi8Jugpo+NjsPIuPV9iPBnKVbeLGJY+iqoBA==","signature_status":"signed_v1","signed_at":"2026-07-05T11:17:11.676644Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.05867","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d4b8cfecdae9d6a7ac876e20e3ac8813a54a96b6b5a5204b9ea7252b4eeb92eb","sha256:7860e7547956bdfc3a3451ab973df6b2a70c4a7dd3a360007dad5f1d99a233bc"],"state_sha256":"1408d208455f1d43342b0004a78291825b40acc4eca43a3e3e03f845c882503c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6R9qZCvZs+56p2l1evfewUGrhgeluQo800ZlV1ZWj9Jzmzl45M612hvBQkNmriF3gmuriKp1uRDJVTVIoB8pBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T00:20:18.788097Z","bundle_sha256":"9da2a29828a5f714ddcee3632047ebe5dfabc3dde72efea72505504511bff308"}}