{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:FWSYTUPDIDL5YERYXALZBG6QJN","short_pith_number":"pith:FWSYTUPD","canonical_record":{"source":{"id":"2310.09652","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-10-14T19:49:02Z","cross_cats_sorted":[],"title_canon_sha256":"e0c5d9b9f5a7d663ff1af3e5117ed87f4ef91d2922f08e7ed61911e18b135401","abstract_canon_sha256":"bb39d2a268ab6fafac62dd7e35b64e1cecc9c5a48a52003a3544925ab25d2d8a"},"schema_version":"1.0"},"canonical_sha256":"2da589d1e340d7dc1238b817909bd04b40fa6d04db95f8cccbe59a36eae04e66","source":{"kind":"arxiv","id":"2310.09652","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.09652","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"arxiv_version","alias_value":"2310.09652v1","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.09652","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"pith_short_12","alias_value":"FWSYTUPDIDL5","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"pith_short_16","alias_value":"FWSYTUPDIDL5YERY","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"pith_short_8","alias_value":"FWSYTUPD","created_at":"2026-07-05T07:01:09Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:FWSYTUPDIDL5YERYXALZBG6QJN","target":"record","payload":{"canonical_record":{"source":{"id":"2310.09652","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-10-14T19:49:02Z","cross_cats_sorted":[],"title_canon_sha256":"e0c5d9b9f5a7d663ff1af3e5117ed87f4ef91d2922f08e7ed61911e18b135401","abstract_canon_sha256":"bb39d2a268ab6fafac62dd7e35b64e1cecc9c5a48a52003a3544925ab25d2d8a"},"schema_version":"1.0"},"canonical_sha256":"2da589d1e340d7dc1238b817909bd04b40fa6d04db95f8cccbe59a36eae04e66","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:01:09.188056Z","signature_b64":"uSohsLV7F5//JnK0BuK9/gxgsxjGXB6wkqRSD6gbUe72qetAXUVjsRKTojCfUV14+kPOBethn4rRzoGEEuGhDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2da589d1e340d7dc1238b817909bd04b40fa6d04db95f8cccbe59a36eae04e66","last_reissued_at":"2026-07-05T07:01:09.187594Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:01:09.187594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.09652","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":"2026-07-05T07:01:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iOhTYhwk38Ysz3xo+gv5OldtWNX0VR/pEj/ZlR5FRoFxyNN8Z5VyRMl77gSkHRu+rHRreyfilY0xz6lP3DK/AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T23:28:26.062598Z"},"content_sha256":"9e58b75e03d9ebfd9469231deb7494a6788214f9208ab7ef7abd71279443b1fe","schema_version":"1.0","event_id":"sha256:9e58b75e03d9ebfd9469231deb7494a6788214f9208ab7ef7abd71279443b1fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:FWSYTUPDIDL5YERYXALZBG6QJN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Qi Xuan, Tianyi Chen, Wenjie Lv, Yitao Zheng, Zhehua Zhong, Zhen Wang","submitted_at":"2023-10-14T19:49:02Z","abstract_excerpt":"Machine learning security has recently become a prominent topic in the natural language processing (NLP) area. The existing black-box adversarial attack suffers prohibitively from the high model querying complexity, resulting in easily being captured by anti-attack monitors. Meanwhile, how to eliminate redundant model queries is rarely explored. In this paper, we propose a query-efficient approach BufferSearch to effectively attack general intelligent NLP systems with the minimal number of querying requests. In general, BufferSearch makes use of historical information and conducts statistical "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.09652","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/2310.09652/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":"2026-07-05T07:01:09Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"UK/1WFhjWMpMGYmW5RbPuFF1oXjtMJM7sea2sjnIjJKLQOyOch8sedOTqBJOmhG0Ad/TbU7Wkm+pc862MZ2EAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T23:28:26.063124Z"},"content_sha256":"b65efcd9fc87d02f6e7b4f42b6a07ebedfd7cdf2726dad6c7695ddf92a6bd816","schema_version":"1.0","event_id":"sha256:b65efcd9fc87d02f6e7b4f42b6a07ebedfd7cdf2726dad6c7695ddf92a6bd816"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/FWSYTUPDIDL5YERYXALZBG6QJN/bundle.json","state_url":"https://pith.science/pith/FWSYTUPDIDL5YERYXALZBG6QJN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/FWSYTUPDIDL5YERYXALZBG6QJN/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":"2026-08-21T23:28:26Z","links":{"resolver":"https://pith.science/pith/FWSYTUPDIDL5YERYXALZBG6QJN","bundle":"https://pith.science/pith/FWSYTUPDIDL5YERYXALZBG6QJN/bundle.json","state":"https://pith.science/pith/FWSYTUPDIDL5YERYXALZBG6QJN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/FWSYTUPDIDL5YERYXALZBG6QJN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:FWSYTUPDIDL5YERYXALZBG6QJN","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":"bb39d2a268ab6fafac62dd7e35b64e1cecc9c5a48a52003a3544925ab25d2d8a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-10-14T19:49:02Z","title_canon_sha256":"e0c5d9b9f5a7d663ff1af3e5117ed87f4ef91d2922f08e7ed61911e18b135401"},"schema_version":"1.0","source":{"id":"2310.09652","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.09652","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"arxiv_version","alias_value":"2310.09652v1","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.09652","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"pith_short_12","alias_value":"FWSYTUPDIDL5","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"pith_short_16","alias_value":"FWSYTUPDIDL5YERY","created_at":"2026-07-05T07:01:09Z"},{"alias_kind":"pith_short_8","alias_value":"FWSYTUPD","created_at":"2026-07-05T07:01:09Z"}],"graph_snapshots":[{"event_id":"sha256:b65efcd9fc87d02f6e7b4f42b6a07ebedfd7cdf2726dad6c7695ddf92a6bd816","target":"graph","created_at":"2026-07-05T07:01:09Z","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/2310.09652/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine learning security has recently become a prominent topic in the natural language processing (NLP) area. The existing black-box adversarial attack suffers prohibitively from the high model querying complexity, resulting in easily being captured by anti-attack monitors. Meanwhile, how to eliminate redundant model queries is rarely explored. In this paper, we propose a query-efficient approach BufferSearch to effectively attack general intelligent NLP systems with the minimal number of querying requests. In general, BufferSearch makes use of historical information and conducts statistical ","authors_text":"Qi Xuan, Tianyi Chen, Wenjie Lv, Yitao Zheng, Zhehua Zhong, Zhen Wang","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-10-14T19:49:02Z","title":"BufferSearch: Generating Black-Box Adversarial Texts With Lower Queries"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.09652","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":"sha256:9e58b75e03d9ebfd9469231deb7494a6788214f9208ab7ef7abd71279443b1fe","target":"record","created_at":"2026-07-05T07:01:09Z","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":"bb39d2a268ab6fafac62dd7e35b64e1cecc9c5a48a52003a3544925ab25d2d8a","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-10-14T19:49:02Z","title_canon_sha256":"e0c5d9b9f5a7d663ff1af3e5117ed87f4ef91d2922f08e7ed61911e18b135401"},"schema_version":"1.0","source":{"id":"2310.09652","kind":"arxiv","version":1}},"canonical_sha256":"2da589d1e340d7dc1238b817909bd04b40fa6d04db95f8cccbe59a36eae04e66","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2da589d1e340d7dc1238b817909bd04b40fa6d04db95f8cccbe59a36eae04e66","first_computed_at":"2026-07-05T07:01:09.187594Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:01:09.187594Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uSohsLV7F5//JnK0BuK9/gxgsxjGXB6wkqRSD6gbUe72qetAXUVjsRKTojCfUV14+kPOBethn4rRzoGEEuGhDg==","signature_status":"signed_v1","signed_at":"2026-07-05T07:01:09.188056Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.09652","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9e58b75e03d9ebfd9469231deb7494a6788214f9208ab7ef7abd71279443b1fe","sha256:b65efcd9fc87d02f6e7b4f42b6a07ebedfd7cdf2726dad6c7695ddf92a6bd816"],"state_sha256":"86c1662eb6110eaf450a5bb21fbf7907c886ca2aeeb60f1d703e0c03588752db"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HlAFIFhGyGWwL/gUd3r+Ga8Kk4VHdLoCQliUr/x9l7HrCApLntV4Wpnue/GJ+PMyE9yaQMjsbayz1WKpO4XQCQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T23:28:26.068591Z","bundle_sha256":"5003982039714b5f031aac3db3ddb960e009fc0460efe4e2d08f816bb31a7508"}}