{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:S37U3EQXECW2I4FMHZFDQ6DUND","short_pith_number":"pith:S37U3EQX","schema_version":"1.0","canonical_sha256":"96ff4d921720ada470ac3e4a38787468d2bfea80b3da02ba402e620667a09410","source":{"kind":"arxiv","id":"2309.14677","version":1},"attestation_state":"computed","paper":{"title":"XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Chau Thuan Phat, Kiet Van Nguyen, Phan The Duy, Van-Hau Pham, Vu Le Anh Quan","submitted_at":"2023-09-26T05:05:34Z","abstract_excerpt":"With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advanta"},"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":"2309.14677","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CR","submitted_at":"2023-09-26T05:05:34Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"66b11ea8eed52e2eeee05f337f77a88ce20ede2a521647429ebbf26ec19d791c","abstract_canon_sha256":"e9250fa195b4c1ef154c86352c42fec0fbd4ec1f83ecc273dde661e72d896a11"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:54:28.319113Z","signature_b64":"ZNwR0kNWHVI8nSKO3WIzbOaQGYq8oQqYbgBd1CcTLIlFmIiciv6nouzQwL9a8hJ2opcwrfltDB5EBsQiyLMUBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"96ff4d921720ada470ac3e4a38787468d2bfea80b3da02ba402e620667a09410","last_reissued_at":"2026-07-05T06:54:28.318684Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:54:28.318684Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"XGV-BERT: Leveraging Contextualized Language Model and Graph Neural Network for Efficient Software Vulnerability Detection","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CR","authors_text":"Chau Thuan Phat, Kiet Van Nguyen, Phan The Duy, Van-Hau Pham, Vu Le Anh Quan","submitted_at":"2023-09-26T05:05:34Z","abstract_excerpt":"With the advancement of deep learning (DL) in various fields, there are many attempts to reveal software vulnerabilities by data-driven approach. Nonetheless, such existing works lack the effective representation that can retain the non-sequential semantic characteristics and contextual relationship of source code attributes. Hence, in this work, we propose XGV-BERT, a framework that combines the pre-trained CodeBERT model and Graph Neural Network (GCN) to detect software vulnerabilities. By jointly training the CodeBERT and GCN modules within XGV-BERT, the proposed model leverages the advanta"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.14677","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/2309.14677/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":"2309.14677","created_at":"2026-07-05T06:54:28.318747+00:00"},{"alias_kind":"arxiv_version","alias_value":"2309.14677v1","created_at":"2026-07-05T06:54:28.318747+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.14677","created_at":"2026-07-05T06:54:28.318747+00:00"},{"alias_kind":"pith_short_12","alias_value":"S37U3EQXECW2","created_at":"2026-07-05T06:54:28.318747+00:00"},{"alias_kind":"pith_short_16","alias_value":"S37U3EQXECW2I4FM","created_at":"2026-07-05T06:54:28.318747+00:00"},{"alias_kind":"pith_short_8","alias_value":"S37U3EQX","created_at":"2026-07-05T06:54:28.318747+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.10280","citing_title":"AI-Based Software Vulnerability Detection: A Systematic Literature Review","ref_index":108,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/S37U3EQXECW2I4FMHZFDQ6DUND","json":"https://pith.science/pith/S37U3EQXECW2I4FMHZFDQ6DUND.json","graph_json":"https://pith.science/api/pith-number/S37U3EQXECW2I4FMHZFDQ6DUND/graph.json","events_json":"https://pith.science/api/pith-number/S37U3EQXECW2I4FMHZFDQ6DUND/events.json","paper":"https://pith.science/paper/S37U3EQX"},"agent_actions":{"view_html":"https://pith.science/pith/S37U3EQXECW2I4FMHZFDQ6DUND","download_json":"https://pith.science/pith/S37U3EQXECW2I4FMHZFDQ6DUND.json","view_paper":"https://pith.science/paper/S37U3EQX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2309.14677&json=true","fetch_graph":"https://pith.science/api/pith-number/S37U3EQXECW2I4FMHZFDQ6DUND/graph.json","fetch_events":"https://pith.science/api/pith-number/S37U3EQXECW2I4FMHZFDQ6DUND/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S37U3EQXECW2I4FMHZFDQ6DUND/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S37U3EQXECW2I4FMHZFDQ6DUND/action/storage_attestation","attest_author":"https://pith.science/pith/S37U3EQXECW2I4FMHZFDQ6DUND/action/author_attestation","sign_citation":"https://pith.science/pith/S37U3EQXECW2I4FMHZFDQ6DUND/action/citation_signature","submit_replication":"https://pith.science/pith/S37U3EQXECW2I4FMHZFDQ6DUND/action/replication_record"}},"created_at":"2026-07-05T06:54:28.318747+00:00","updated_at":"2026-07-05T06:54:28.318747+00:00"}