{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:KADMEEVLG4GZHP57ASX36OCNUJ","short_pith_number":"pith:KADMEEVL","schema_version":"1.0","canonical_sha256":"5006c212ab370d93bfbf04afbf384da247bc001d0c80174e6e49c4fb44fba888","source":{"kind":"arxiv","id":"2307.12469","version":5},"attestation_state":"computed","paper":{"title":"How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Cen Zhang, Limin Sun, Mingqiang Bai, Wei Ma, Xiaofei Xie, Yang Liu, Yaowen Zheng, Yeting Li, Yuekang Li","submitted_at":"2023-07-24T01:49:05Z","abstract_excerpt":"LLM-based (Large Language Model) fuzz driver generation is a promising research area. Unlike traditional program analysis-based method, this text-based approach is more general and capable of harnessing a variety of API usage information, resulting in code that is friendly for human readers. However, there is still a lack of understanding regarding the fundamental issues on this direction, such as its effectiveness and potential challenges. To bridge this gap, we conducted the first in-depth study targeting the important issues of using LLMs to generate effective fuzz drivers. Our study featur"},"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":"2307.12469","kind":"arxiv","version":5},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2023-07-24T01:49:05Z","cross_cats_sorted":[],"title_canon_sha256":"1f504a4214fcb21280aa65720712ee393a8678e1ecbf2ecc1dd38280ca65bf70","abstract_canon_sha256":"a8a5fb0936fa0e37047f7e0dcf015dcfe4f8c6b9d6ecb193b44cd351e44d158b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:49:25.193501Z","signature_b64":"M39AW4XmrufRvPTY1U+4lFng7PakFxGmwIXA4PGW22A7FU0TlVEWyjEcmToMVAA6WJsPR2pcWmSHwFgcW+iaAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5006c212ab370d93bfbf04afbf384da247bc001d0c80174e6e49c4fb44fba888","last_reissued_at":"2026-07-05T08:49:25.193111Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:49:25.193111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"How Effective Are They? Exploring Large Language Model Based Fuzz Driver Generation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Cen Zhang, Limin Sun, Mingqiang Bai, Wei Ma, Xiaofei Xie, Yang Liu, Yaowen Zheng, Yeting Li, Yuekang Li","submitted_at":"2023-07-24T01:49:05Z","abstract_excerpt":"LLM-based (Large Language Model) fuzz driver generation is a promising research area. Unlike traditional program analysis-based method, this text-based approach is more general and capable of harnessing a variety of API usage information, resulting in code that is friendly for human readers. However, there is still a lack of understanding regarding the fundamental issues on this direction, such as its effectiveness and potential challenges. To bridge this gap, we conducted the first in-depth study targeting the important issues of using LLMs to generate effective fuzz drivers. Our study featur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2307.12469","kind":"arxiv","version":5},"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/2307.12469/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":"2307.12469","created_at":"2026-07-05T08:49:25.193166+00:00"},{"alias_kind":"arxiv_version","alias_value":"2307.12469v5","created_at":"2026-07-05T08:49:25.193166+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2307.12469","created_at":"2026-07-05T08:49:25.193166+00:00"},{"alias_kind":"pith_short_12","alias_value":"KADMEEVLG4GZ","created_at":"2026-07-05T08:49:25.193166+00:00"},{"alias_kind":"pith_short_16","alias_value":"KADMEEVLG4GZHP57","created_at":"2026-07-05T08:49:25.193166+00:00"},{"alias_kind":"pith_short_8","alias_value":"KADMEEVL","created_at":"2026-07-05T08:49:25.193166+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2412.01317","citing_title":"The Seeds of the FUTURE Sprout from History: Fuzzing for Unveiling Vulnerabilities in Prospective Deep-Learning Libraries","ref_index":54,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/KADMEEVLG4GZHP57ASX36OCNUJ","json":"https://pith.science/pith/KADMEEVLG4GZHP57ASX36OCNUJ.json","graph_json":"https://pith.science/api/pith-number/KADMEEVLG4GZHP57ASX36OCNUJ/graph.json","events_json":"https://pith.science/api/pith-number/KADMEEVLG4GZHP57ASX36OCNUJ/events.json","paper":"https://pith.science/paper/KADMEEVL"},"agent_actions":{"view_html":"https://pith.science/pith/KADMEEVLG4GZHP57ASX36OCNUJ","download_json":"https://pith.science/pith/KADMEEVLG4GZHP57ASX36OCNUJ.json","view_paper":"https://pith.science/paper/KADMEEVL","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2307.12469&json=true","fetch_graph":"https://pith.science/api/pith-number/KADMEEVLG4GZHP57ASX36OCNUJ/graph.json","fetch_events":"https://pith.science/api/pith-number/KADMEEVLG4GZHP57ASX36OCNUJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KADMEEVLG4GZHP57ASX36OCNUJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KADMEEVLG4GZHP57ASX36OCNUJ/action/storage_attestation","attest_author":"https://pith.science/pith/KADMEEVLG4GZHP57ASX36OCNUJ/action/author_attestation","sign_citation":"https://pith.science/pith/KADMEEVLG4GZHP57ASX36OCNUJ/action/citation_signature","submit_replication":"https://pith.science/pith/KADMEEVLG4GZHP57ASX36OCNUJ/action/replication_record"}},"created_at":"2026-07-05T08:49:25.193166+00:00","updated_at":"2026-07-05T08:49:25.193166+00:00"}