{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:K23JHMZZJUNGDF2EZB4ML7X6IV","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":"fb2ef7488b2e7a84c6df4063ef215a228f51ae7da219e3eeac75a484383c743f","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-01-27T07:57:20Z","title_canon_sha256":"15c3257401f54c73c0f65ce8c14fb0db7eab2f5bfd7f04afd0f7a49f19cc3b82"},"schema_version":"1.0","source":{"id":"2401.15335","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2401.15335","created_at":"2026-07-05T08:21:48Z"},{"alias_kind":"arxiv_version","alias_value":"2401.15335v2","created_at":"2026-07-05T08:21:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.15335","created_at":"2026-07-05T08:21:48Z"},{"alias_kind":"pith_short_12","alias_value":"K23JHMZZJUNG","created_at":"2026-07-05T08:21:48Z"},{"alias_kind":"pith_short_16","alias_value":"K23JHMZZJUNGDF2E","created_at":"2026-07-05T08:21:48Z"},{"alias_kind":"pith_short_8","alias_value":"K23JHMZZ","created_at":"2026-07-05T08:21:48Z"}],"graph_snapshots":[{"event_id":"sha256:2f1f8d704b7cbbc42feda5a50251b4bd8d0d451b3a0e9aeb0fd0c9b77d630193","target":"graph","created_at":"2026-07-05T08:21:48Z","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/2401.15335/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In the rapidly evolving field of machine learning, adversarial attacks present a significant challenge to model robustness and security. Decision-based attacks, which only require feedback on the decision of a model rather than detailed probabilities or scores, are particularly insidious and difficult to defend against. This work introduces L-AutoDA (Large Language Model-based Automated Decision-based Adversarial Attacks), a novel approach leveraging the generative capabilities of Large Language Models (LLMs) to automate the design of these attacks. By iteratively interacting with LLMs in an e","authors_text":"Fei Liu, Ping Guo, Qingchuan Zhao, Qingfu Zhang, Xi Lin","cross_cats":["cs.AI","cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-01-27T07:57:20Z","title":"L-AutoDA: Leveraging Large Language Models for Automated Decision-based Adversarial Attacks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.15335","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":"sha256:299a247929f8c28e2d7a78ffdf736f26eb63636d739f371c909e67da39c09fc1","target":"record","created_at":"2026-07-05T08:21:48Z","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":"fb2ef7488b2e7a84c6df4063ef215a228f51ae7da219e3eeac75a484383c743f","cross_cats_sorted":["cs.AI","cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-01-27T07:57:20Z","title_canon_sha256":"15c3257401f54c73c0f65ce8c14fb0db7eab2f5bfd7f04afd0f7a49f19cc3b82"},"schema_version":"1.0","source":{"id":"2401.15335","kind":"arxiv","version":2}},"canonical_sha256":"56b693b3394d1a619744c878c5fefe457fd66bcfe2a02f08679190c769d678f9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"56b693b3394d1a619744c878c5fefe457fd66bcfe2a02f08679190c769d678f9","first_computed_at":"2026-07-05T08:21:48.671818Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:21:48.671818Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"deDe0kI8XXegrueM4TOuYuGyxPLh6PVxznbFGLm/Jc24V7qQnLFv3mOv/r1FVSnutQHqjbgYA4OdHCxsWgk3Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T08:21:48.672311Z","signed_message":"canonical_sha256_bytes"},"source_id":"2401.15335","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:299a247929f8c28e2d7a78ffdf736f26eb63636d739f371c909e67da39c09fc1","sha256:2f1f8d704b7cbbc42feda5a50251b4bd8d0d451b3a0e9aeb0fd0c9b77d630193"],"state_sha256":"bd136c9027ca9cea0a1bb5826fd60d927b90d403f56b5182429152713a283969"}