{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:IY4Q2ABTVE5VCMUAWZVWE2VFIE","short_pith_number":"pith:IY4Q2ABT","schema_version":"1.0","canonical_sha256":"46390d0033a93b513280b66b626aa5413d202d3f135b9b5b3d1564d23fae2dd7","source":{"kind":"arxiv","id":"2409.01952","version":2},"attestation_state":"computed","paper":{"title":"Exploiting the Vulnerability of Large Language Models via Defense-Aware Architectural Backdoor","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.AR"],"primary_cat":"cs.CR","authors_text":"Abdullah Arafat Miah, Yu Bi","submitted_at":"2024-09-03T14:54:16Z","abstract_excerpt":"Deep neural networks (DNNs) have long been recognized as vulnerable to backdoor attacks. By providing poisoned training data in the fine-tuning process, the attacker can implant a backdoor into the victim model. This enables input samples meeting specific textual trigger patterns to be classified as target labels of the attacker's choice. While such black-box attacks have been well explored in both computer vision and natural language processing (NLP), backdoor attacks relying on white-box attack philosophy have hardly been thoroughly investigated. In this paper, we take the first step to intr"},"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":"2409.01952","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2024-09-03T14:54:16Z","cross_cats_sorted":["cs.AI","cs.AR"],"title_canon_sha256":"c9e5f2c5b94a7c182a70c3ed1fe478246d06c697abefc853e8e5fbebc759dd61","abstract_canon_sha256":"50765d1a60f3e3b309c0c0c29c088f97a976e0daf30d3902b0dc504b7bbecf24"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:04:40.355304Z","signature_b64":"pJbyGd243ZmGzN+yAyTqZRKp7q2nctds4aFw4/tX2x21C+zr46R7t1kSPnJeqa/qOURDi4dKSsM2jNufk6lqBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46390d0033a93b513280b66b626aa5413d202d3f135b9b5b3d1564d23fae2dd7","last_reissued_at":"2026-07-05T09:04:40.354739Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:04:40.354739Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Exploiting the Vulnerability of Large Language Models via Defense-Aware Architectural Backdoor","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.AR"],"primary_cat":"cs.CR","authors_text":"Abdullah Arafat Miah, Yu Bi","submitted_at":"2024-09-03T14:54:16Z","abstract_excerpt":"Deep neural networks (DNNs) have long been recognized as vulnerable to backdoor attacks. By providing poisoned training data in the fine-tuning process, the attacker can implant a backdoor into the victim model. This enables input samples meeting specific textual trigger patterns to be classified as target labels of the attacker's choice. While such black-box attacks have been well explored in both computer vision and natural language processing (NLP), backdoor attacks relying on white-box attack philosophy have hardly been thoroughly investigated. In this paper, we take the first step to intr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.01952","kind":"arxiv","version":2},"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/2409.01952/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":"2409.01952","created_at":"2026-07-05T09:04:40.354805+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.01952v2","created_at":"2026-07-05T09:04:40.354805+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.01952","created_at":"2026-07-05T09:04:40.354805+00:00"},{"alias_kind":"pith_short_12","alias_value":"IY4Q2ABTVE5V","created_at":"2026-07-05T09:04:40.354805+00:00"},{"alias_kind":"pith_short_16","alias_value":"IY4Q2ABTVE5VCMUA","created_at":"2026-07-05T09:04:40.354805+00:00"},{"alias_kind":"pith_short_8","alias_value":"IY4Q2ABT","created_at":"2026-07-05T09:04:40.354805+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2602.07200","citing_title":"BadSNN: Backdoor Attacks on Spiking Neural Networks via Adversarial Spiking Neuron","ref_index":38,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IY4Q2ABTVE5VCMUAWZVWE2VFIE","json":"https://pith.science/pith/IY4Q2ABTVE5VCMUAWZVWE2VFIE.json","graph_json":"https://pith.science/api/pith-number/IY4Q2ABTVE5VCMUAWZVWE2VFIE/graph.json","events_json":"https://pith.science/api/pith-number/IY4Q2ABTVE5VCMUAWZVWE2VFIE/events.json","paper":"https://pith.science/paper/IY4Q2ABT"},"agent_actions":{"view_html":"https://pith.science/pith/IY4Q2ABTVE5VCMUAWZVWE2VFIE","download_json":"https://pith.science/pith/IY4Q2ABTVE5VCMUAWZVWE2VFIE.json","view_paper":"https://pith.science/paper/IY4Q2ABT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.01952&json=true","fetch_graph":"https://pith.science/api/pith-number/IY4Q2ABTVE5VCMUAWZVWE2VFIE/graph.json","fetch_events":"https://pith.science/api/pith-number/IY4Q2ABTVE5VCMUAWZVWE2VFIE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IY4Q2ABTVE5VCMUAWZVWE2VFIE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IY4Q2ABTVE5VCMUAWZVWE2VFIE/action/storage_attestation","attest_author":"https://pith.science/pith/IY4Q2ABTVE5VCMUAWZVWE2VFIE/action/author_attestation","sign_citation":"https://pith.science/pith/IY4Q2ABTVE5VCMUAWZVWE2VFIE/action/citation_signature","submit_replication":"https://pith.science/pith/IY4Q2ABTVE5VCMUAWZVWE2VFIE/action/replication_record"}},"created_at":"2026-07-05T09:04:40.354805+00:00","updated_at":"2026-07-05T09:04:40.354805+00:00"}