{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:JTHRBTWYUIE6AVSOFPOBMW67H4","short_pith_number":"pith:JTHRBTWY","schema_version":"1.0","canonical_sha256":"4ccf10ced8a209e0564e2bdc165bdf3f3bdbffc1b6968feba01c5a2888e09b6d","source":{"kind":"arxiv","id":"2410.02841","version":1},"attestation_state":"computed","paper":{"title":"Demonstration Attack against In-Context Learning for Code Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.CR","authors_text":"Chunrong Fang, Weisong Sun, Xiaofang Zhang, Yang Liu, Yifei Ge, Yihang Lou, Yiming Li, Yiran Zhang, Zhenyu Chen, Zhihong Zhao","submitted_at":"2024-10-03T12:59:29Z","abstract_excerpt":"Recent advancements in large language models (LLMs) have revolutionized code intelligence by improving programming productivity and alleviating challenges faced by software developers. To further improve the performance of LLMs on specific code intelligence tasks and reduce training costs, researchers reveal a new capability of LLMs: in-context learning (ICL). ICL allows LLMs to learn from a few demonstrations within a specific context, achieving impressive results without parameter updating. However, the rise of ICL introduces new security vulnerabilities in the code intelligence field. In th"},"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":"2410.02841","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2024-10-03T12:59:29Z","cross_cats_sorted":["cs.SE"],"title_canon_sha256":"8a8ca2e9002bafe272e78fe6ed7329eed68aba7f0cb3551c6829f214559f18ca","abstract_canon_sha256":"2b7289f3bddd71ab3d63686198635515c078a6658f0a320fd89433e6d4bde1e9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:15:40.657567Z","signature_b64":"auPRxoONAXbwJkFUvr01Nroy8y6IJztA9J7mxM4yxY+DeCgRtnH0qnDlbOITO0FgnMi+xrTYwU04V1gl+pz6Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4ccf10ced8a209e0564e2bdc165bdf3f3bdbffc1b6968feba01c5a2888e09b6d","last_reissued_at":"2026-07-05T09:15:40.656952Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:15:40.656952Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Demonstration Attack against In-Context Learning for Code Intelligence","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.SE"],"primary_cat":"cs.CR","authors_text":"Chunrong Fang, Weisong Sun, Xiaofang Zhang, Yang Liu, Yifei Ge, Yihang Lou, Yiming Li, Yiran Zhang, Zhenyu Chen, Zhihong Zhao","submitted_at":"2024-10-03T12:59:29Z","abstract_excerpt":"Recent advancements in large language models (LLMs) have revolutionized code intelligence by improving programming productivity and alleviating challenges faced by software developers. To further improve the performance of LLMs on specific code intelligence tasks and reduce training costs, researchers reveal a new capability of LLMs: in-context learning (ICL). ICL allows LLMs to learn from a few demonstrations within a specific context, achieving impressive results without parameter updating. However, the rise of ICL introduces new security vulnerabilities in the code intelligence field. In th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2410.02841","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/2410.02841/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":"2410.02841","created_at":"2026-07-05T09:15:40.657016+00:00"},{"alias_kind":"arxiv_version","alias_value":"2410.02841v1","created_at":"2026-07-05T09:15:40.657016+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2410.02841","created_at":"2026-07-05T09:15:40.657016+00:00"},{"alias_kind":"pith_short_12","alias_value":"JTHRBTWYUIE6","created_at":"2026-07-05T09:15:40.657016+00:00"},{"alias_kind":"pith_short_16","alias_value":"JTHRBTWYUIE6AVSO","created_at":"2026-07-05T09:15:40.657016+00:00"},{"alias_kind":"pith_short_8","alias_value":"JTHRBTWY","created_at":"2026-07-05T09:15:40.657016+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.22776","citing_title":"Smaller = Weaker? Benchmarking Robustness of Quantized LLMs in Code Generation","ref_index":36,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/JTHRBTWYUIE6AVSOFPOBMW67H4","json":"https://pith.science/pith/JTHRBTWYUIE6AVSOFPOBMW67H4.json","graph_json":"https://pith.science/api/pith-number/JTHRBTWYUIE6AVSOFPOBMW67H4/graph.json","events_json":"https://pith.science/api/pith-number/JTHRBTWYUIE6AVSOFPOBMW67H4/events.json","paper":"https://pith.science/paper/JTHRBTWY"},"agent_actions":{"view_html":"https://pith.science/pith/JTHRBTWYUIE6AVSOFPOBMW67H4","download_json":"https://pith.science/pith/JTHRBTWYUIE6AVSOFPOBMW67H4.json","view_paper":"https://pith.science/paper/JTHRBTWY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2410.02841&json=true","fetch_graph":"https://pith.science/api/pith-number/JTHRBTWYUIE6AVSOFPOBMW67H4/graph.json","fetch_events":"https://pith.science/api/pith-number/JTHRBTWYUIE6AVSOFPOBMW67H4/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JTHRBTWYUIE6AVSOFPOBMW67H4/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JTHRBTWYUIE6AVSOFPOBMW67H4/action/storage_attestation","attest_author":"https://pith.science/pith/JTHRBTWYUIE6AVSOFPOBMW67H4/action/author_attestation","sign_citation":"https://pith.science/pith/JTHRBTWYUIE6AVSOFPOBMW67H4/action/citation_signature","submit_replication":"https://pith.science/pith/JTHRBTWYUIE6AVSOFPOBMW67H4/action/replication_record"}},"created_at":"2026-07-05T09:15:40.657016+00:00","updated_at":"2026-07-05T09:15:40.657016+00:00"}