{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:UYKXSAGAUV76CR4R3TEOV5LKM5","short_pith_number":"pith:UYKXSAGA","schema_version":"1.0","canonical_sha256":"a6157900c0a57fe14791dcc8eaf56a676518531d10e96744366f123f7a3b1df3","source":{"kind":"arxiv","id":"2309.09826","version":2},"attestation_state":"computed","paper":{"title":"Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CR","authors_text":"Andr\\'e Storhaug, Jingyue Li, Tianyuan Hu","submitted_at":"2023-09-18T14:47:34Z","abstract_excerpt":"Auto-completing code enables developers to speed up coding significantly. Recent advances in transformer-based large language model (LLM) technologies have been applied to code synthesis. However, studies show that many of such synthesized codes contain vulnerabilities. We propose a novel vulnerability-constrained decoding approach to reduce the amount of vulnerable code generated by such models. Using a small dataset of labeled vulnerable lines of code, we fine-tune an LLM to include vulnerability labels when generating code, acting as an embedded classifier. Then, during decoding, we deny 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":"2309.09826","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2023-09-18T14:47:34Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"495df5b286b290047e26011bac2dc07882666516207da9a8c8bd34fd9d18afc3","abstract_canon_sha256":"7e745d01be880547390955634dac5250ff79f99ee578c1e57935001bb207c673"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:57:55.640664Z","signature_b64":"GGj8cSjH4vc3BT9m7NRayJRfHS9JU1wNNAirBsEHp4UksyAV/wCOrRfTWUSPDTl9Fd20eXESu38GJDr2CR34Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6157900c0a57fe14791dcc8eaf56a676518531d10e96744366f123f7a3b1df3","last_reissued_at":"2026-07-05T06:57:55.640083Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:57:55.640083Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Efficient Avoidance of Vulnerabilities in Auto-completed Smart Contract Code Using Vulnerability-constrained Decoding","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.CR","authors_text":"Andr\\'e Storhaug, Jingyue Li, Tianyuan Hu","submitted_at":"2023-09-18T14:47:34Z","abstract_excerpt":"Auto-completing code enables developers to speed up coding significantly. Recent advances in transformer-based large language model (LLM) technologies have been applied to code synthesis. However, studies show that many of such synthesized codes contain vulnerabilities. We propose a novel vulnerability-constrained decoding approach to reduce the amount of vulnerable code generated by such models. Using a small dataset of labeled vulnerable lines of code, we fine-tune an LLM to include vulnerability labels when generating code, acting as an embedded classifier. Then, during decoding, we deny th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.09826","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/2309.09826/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":"2309.09826","created_at":"2026-07-05T06:57:55.640157+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.09826v2","created_at":"2026-07-05T06:57:55.640157+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.09826","created_at":"2026-07-05T06:57:55.640157+00:00"},{"alias_kind":"pith_short_12","alias_value":"UYKXSAGAUV76","created_at":"2026-07-05T06:57:55.640157+00:00"},{"alias_kind":"pith_short_16","alias_value":"UYKXSAGAUV76CR4R","created_at":"2026-07-05T06:57:55.640157+00:00"},{"alias_kind":"pith_short_8","alias_value":"UYKXSAGA","created_at":"2026-07-05T06:57:55.640157+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/UYKXSAGAUV76CR4R3TEOV5LKM5","json":"https://pith.science/pith/UYKXSAGAUV76CR4R3TEOV5LKM5.json","graph_json":"https://pith.science/api/pith-number/UYKXSAGAUV76CR4R3TEOV5LKM5/graph.json","events_json":"https://pith.science/api/pith-number/UYKXSAGAUV76CR4R3TEOV5LKM5/events.json","paper":"https://pith.science/paper/UYKXSAGA"},"agent_actions":{"view_html":"https://pith.science/pith/UYKXSAGAUV76CR4R3TEOV5LKM5","download_json":"https://pith.science/pith/UYKXSAGAUV76CR4R3TEOV5LKM5.json","view_paper":"https://pith.science/paper/UYKXSAGA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.09826&json=true","fetch_graph":"https://pith.science/api/pith-number/UYKXSAGAUV76CR4R3TEOV5LKM5/graph.json","fetch_events":"https://pith.science/api/pith-number/UYKXSAGAUV76CR4R3TEOV5LKM5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UYKXSAGAUV76CR4R3TEOV5LKM5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UYKXSAGAUV76CR4R3TEOV5LKM5/action/storage_attestation","attest_author":"https://pith.science/pith/UYKXSAGAUV76CR4R3TEOV5LKM5/action/author_attestation","sign_citation":"https://pith.science/pith/UYKXSAGAUV76CR4R3TEOV5LKM5/action/citation_signature","submit_replication":"https://pith.science/pith/UYKXSAGAUV76CR4R3TEOV5LKM5/action/replication_record"}},"created_at":"2026-07-05T06:57:55.640157+00:00","updated_at":"2026-07-05T06:57:55.640157+00:00"}