{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:B5TXBHC7YFLW2WE6MOJYNYBTJX","short_pith_number":"pith:B5TXBHC7","schema_version":"1.0","canonical_sha256":"0f67709c5fc1576d589e639386e0334df7d2113b5990ae2e2156ce328e6598d4","source":{"kind":"arxiv","id":"2605.19446","version":1},"attestation_state":"computed","paper":{"title":"Targeted Downstream-Agnostic Attack","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Yi Zhang, Zhuxin Lei, Ziyuan Yang","submitted_at":"2026-05-19T07:00:36Z","abstract_excerpt":"Recently, pre-trained encoders have gained widespread use due to their strong capability in representation extraction. However, they are vulnerable to downstream-agnostic attacks (DAAs). Existing DAA methods operate under a permissive threat model, where an attack is successful if the generated downstream-agnostic adversarial examples (DAEs) change the original prediction, without requiring a specific target. In this paper, we propose a Targeted DAA (TDAA) method under a stricter threat model requiring the attack to be both targeted and downstream-agnostic. Since the downstream task is unknown"},"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":"2605.19446","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-05-19T07:00:36Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"262ec4050dc6ace70830716862fa36efa88997848717706ca58203ad6218df1c","abstract_canon_sha256":"34d3b1b9d1f08450eb25b23c212e0b95c0b2cb302e123a9860a6a7648dd98e93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-20T01:05:46.270023Z","signature_b64":"A47Ga4Pmb58AV+LgM7zYIJ+iZp/YeYjG7yAY+UxmefpZDKXZ8oezUO+QGJGgH4CRV1jZHBW3w47+XOpTI8zwDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0f67709c5fc1576d589e639386e0334df7d2113b5990ae2e2156ce328e6598d4","last_reissued_at":"2026-05-20T01:05:46.269187Z","signature_status":"signed_v1","first_computed_at":"2026-05-20T01:05:46.269187Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Targeted Downstream-Agnostic Attack","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Yi Zhang, Zhuxin Lei, Ziyuan Yang","submitted_at":"2026-05-19T07:00:36Z","abstract_excerpt":"Recently, pre-trained encoders have gained widespread use due to their strong capability in representation extraction. However, they are vulnerable to downstream-agnostic attacks (DAAs). Existing DAA methods operate under a permissive threat model, where an attack is successful if the generated downstream-agnostic adversarial examples (DAEs) change the original prediction, without requiring a specific target. In this paper, we propose a Targeted DAA (TDAA) method under a stricter threat model requiring the attack to be both targeted and downstream-agnostic. Since the downstream task is unknown"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.19446","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/2605.19446/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":"2605.19446","created_at":"2026-05-20T01:05:46.269345+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.19446v1","created_at":"2026-05-20T01:05:46.269345+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.19446","created_at":"2026-05-20T01:05:46.269345+00:00"},{"alias_kind":"pith_short_12","alias_value":"B5TXBHC7YFLW","created_at":"2026-05-20T01:05:46.269345+00:00"},{"alias_kind":"pith_short_16","alias_value":"B5TXBHC7YFLW2WE6","created_at":"2026-05-20T01:05:46.269345+00:00"},{"alias_kind":"pith_short_8","alias_value":"B5TXBHC7","created_at":"2026-05-20T01:05:46.269345+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/B5TXBHC7YFLW2WE6MOJYNYBTJX","json":"https://pith.science/pith/B5TXBHC7YFLW2WE6MOJYNYBTJX.json","graph_json":"https://pith.science/api/pith-number/B5TXBHC7YFLW2WE6MOJYNYBTJX/graph.json","events_json":"https://pith.science/api/pith-number/B5TXBHC7YFLW2WE6MOJYNYBTJX/events.json","paper":"https://pith.science/paper/B5TXBHC7"},"agent_actions":{"view_html":"https://pith.science/pith/B5TXBHC7YFLW2WE6MOJYNYBTJX","download_json":"https://pith.science/pith/B5TXBHC7YFLW2WE6MOJYNYBTJX.json","view_paper":"https://pith.science/paper/B5TXBHC7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.19446&json=true","fetch_graph":"https://pith.science/api/pith-number/B5TXBHC7YFLW2WE6MOJYNYBTJX/graph.json","fetch_events":"https://pith.science/api/pith-number/B5TXBHC7YFLW2WE6MOJYNYBTJX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/B5TXBHC7YFLW2WE6MOJYNYBTJX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/B5TXBHC7YFLW2WE6MOJYNYBTJX/action/storage_attestation","attest_author":"https://pith.science/pith/B5TXBHC7YFLW2WE6MOJYNYBTJX/action/author_attestation","sign_citation":"https://pith.science/pith/B5TXBHC7YFLW2WE6MOJYNYBTJX/action/citation_signature","submit_replication":"https://pith.science/pith/B5TXBHC7YFLW2WE6MOJYNYBTJX/action/replication_record"}},"created_at":"2026-05-20T01:05:46.269345+00:00","updated_at":"2026-05-20T01:05:46.269345+00:00"}