{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:HUOZO3LDOIJUMCJFXQQGPTNF7T","short_pith_number":"pith:HUOZO3LD","schema_version":"1.0","canonical_sha256":"3d1d976d637213460925bc2067cda5fce69b790c12c3efce53949687a5ebfb3f","source":{"kind":"arxiv","id":"2312.10467","version":3},"attestation_state":"computed","paper":{"title":"TrojFSP: Trojan Insertion in Few-shot Prompt Tuning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiaqi Xue, Lei Jiang, Mengxin Zheng, Qian Lou, Xun Chen, Yanshan Wang","submitted_at":"2023-12-16T14:49:36Z","abstract_excerpt":"Prompt tuning is one of the most effective solutions to adapting a fixed pre-trained language model (PLM) for various downstream tasks, especially with only a few input samples. However, the security issues, e.g., Trojan attacks, of prompt tuning on a few data samples are not well-studied. Transferring established data poisoning attacks directly to few-shot prompt tuning presents multiple challenges. One significant issue is the \\textit{poisoned imbalance issue}, where non-target class samples are added to the target class, resulting in a greater number of target-class samples compared to non-"},"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":"2312.10467","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2023-12-16T14:49:36Z","cross_cats_sorted":[],"title_canon_sha256":"14b5e8b66e229d3da0e24ea73049a47b5489bfc96d598f70fbf6d574d0855a4f","abstract_canon_sha256":"c222b454a92a3aecec8e532b95656a976de9cc154aa2a0693cab4ec5ce04c68b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:57:47.914512Z","signature_b64":"i6TyY6JN71/skaDu+djba/q7TIrvwf7IBIilK0twYrPoZvkHaPAQJ1yptWxKpeSwEsXgMd3iVCt8RNPYLxALDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"3d1d976d637213460925bc2067cda5fce69b790c12c3efce53949687a5ebfb3f","last_reissued_at":"2026-07-05T07:57:47.913958Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:57:47.913958Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TrojFSP: Trojan Insertion in Few-shot Prompt Tuning","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Jiaqi Xue, Lei Jiang, Mengxin Zheng, Qian Lou, Xun Chen, Yanshan Wang","submitted_at":"2023-12-16T14:49:36Z","abstract_excerpt":"Prompt tuning is one of the most effective solutions to adapting a fixed pre-trained language model (PLM) for various downstream tasks, especially with only a few input samples. However, the security issues, e.g., Trojan attacks, of prompt tuning on a few data samples are not well-studied. Transferring established data poisoning attacks directly to few-shot prompt tuning presents multiple challenges. One significant issue is the \\textit{poisoned imbalance issue}, where non-target class samples are added to the target class, resulting in a greater number of target-class samples compared to non-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.10467","kind":"arxiv","version":3},"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/2312.10467/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":"2312.10467","created_at":"2026-07-05T07:57:47.914029+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.10467v3","created_at":"2026-07-05T07:57:47.914029+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.10467","created_at":"2026-07-05T07:57:47.914029+00:00"},{"alias_kind":"pith_short_12","alias_value":"HUOZO3LDOIJU","created_at":"2026-07-05T07:57:47.914029+00:00"},{"alias_kind":"pith_short_16","alias_value":"HUOZO3LDOIJUMCJF","created_at":"2026-07-05T07:57:47.914029+00:00"},{"alias_kind":"pith_short_8","alias_value":"HUOZO3LD","created_at":"2026-07-05T07:57:47.914029+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.06518","citing_title":"A Systematic Review of Poisoning Attacks Against Large Language Models","ref_index":52,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/HUOZO3LDOIJUMCJFXQQGPTNF7T","json":"https://pith.science/pith/HUOZO3LDOIJUMCJFXQQGPTNF7T.json","graph_json":"https://pith.science/api/pith-number/HUOZO3LDOIJUMCJFXQQGPTNF7T/graph.json","events_json":"https://pith.science/api/pith-number/HUOZO3LDOIJUMCJFXQQGPTNF7T/events.json","paper":"https://pith.science/paper/HUOZO3LD"},"agent_actions":{"view_html":"https://pith.science/pith/HUOZO3LDOIJUMCJFXQQGPTNF7T","download_json":"https://pith.science/pith/HUOZO3LDOIJUMCJFXQQGPTNF7T.json","view_paper":"https://pith.science/paper/HUOZO3LD","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.10467&json=true","fetch_graph":"https://pith.science/api/pith-number/HUOZO3LDOIJUMCJFXQQGPTNF7T/graph.json","fetch_events":"https://pith.science/api/pith-number/HUOZO3LDOIJUMCJFXQQGPTNF7T/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/HUOZO3LDOIJUMCJFXQQGPTNF7T/action/timestamp_anchor","attest_storage":"https://pith.science/pith/HUOZO3LDOIJUMCJFXQQGPTNF7T/action/storage_attestation","attest_author":"https://pith.science/pith/HUOZO3LDOIJUMCJFXQQGPTNF7T/action/author_attestation","sign_citation":"https://pith.science/pith/HUOZO3LDOIJUMCJFXQQGPTNF7T/action/citation_signature","submit_replication":"https://pith.science/pith/HUOZO3LDOIJUMCJFXQQGPTNF7T/action/replication_record"}},"created_at":"2026-07-05T07:57:47.914029+00:00","updated_at":"2026-07-05T07:57:47.914029+00:00"}