{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:S2NAK5KK2VPGFHFTWINITRZSWJ","short_pith_number":"pith:S2NAK5KK","schema_version":"1.0","canonical_sha256":"969a05754ad55e629cb3b21a89c732b27643764d18a24940f38486f58d239df7","source":{"kind":"arxiv","id":"2606.21059","version":1},"attestation_state":"computed","paper":{"title":"DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Ahmad Mohsin, Ahmed Ibrahim, Diksha Goel, Gang Li, Guangsheng Yu, Helge Janicke, Kristen Moore, Minjune Kim, Qin Wang, Zhen Wang","submitted_at":"2026-06-19T03:10:01Z","abstract_excerpt":"Large Language Models (LLMs) show promise for supporting decision-making in cybersecurity, but their reliability in high-stakes, time-evolving environments remains limited due to hallucinations, poor temporal reasoning, and shallow grounding in system context. We introduce DEFENGRAPH, an LLM-driven assistant designed to support human defenders during cybersecurity incidents. DEFENGRAPH improves contextual reasoning by integrating a dual-layer Static-Dynamic Knowledge Graph (KG) with graph-based path retrieval, LLM-driven contextual filtering, and reasoning-based re-ranking. The framework groun"},"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":"2606.21059","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2026-06-19T03:10:01Z","cross_cats_sorted":[],"title_canon_sha256":"e39e59466434ebc2c1a6c69142248a5675d351e4c6bae179231d343350b44aec","abstract_canon_sha256":"4ee28115285a32df070b799dab16f5027e4e7606de28e85d7e7b72a519b8fadf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T01:12:28.561382Z","signature_b64":"cQUu9qlHoL5Q3q+oKRGz/LAGkswi+TQD8GnwjJ0KcVs7jeyRE7YqrN8LAaspMyXtj8SvhYf7Hm5WIx9b1Q6aCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"969a05754ad55e629cb3b21a89c732b27643764d18a24940f38486f58d239df7","last_reissued_at":"2026-06-23T01:12:28.560926Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T01:12:28.560926Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DEFENGRAPH: Knowledge Graph-Enhanced LLMs for Blue Team Cyber Defense","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CR","authors_text":"Ahmad Mohsin, Ahmed Ibrahim, Diksha Goel, Gang Li, Guangsheng Yu, Helge Janicke, Kristen Moore, Minjune Kim, Qin Wang, Zhen Wang","submitted_at":"2026-06-19T03:10:01Z","abstract_excerpt":"Large Language Models (LLMs) show promise for supporting decision-making in cybersecurity, but their reliability in high-stakes, time-evolving environments remains limited due to hallucinations, poor temporal reasoning, and shallow grounding in system context. We introduce DEFENGRAPH, an LLM-driven assistant designed to support human defenders during cybersecurity incidents. DEFENGRAPH improves contextual reasoning by integrating a dual-layer Static-Dynamic Knowledge Graph (KG) with graph-based path retrieval, LLM-driven contextual filtering, and reasoning-based re-ranking. The framework groun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.21059","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/2606.21059/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":"2606.21059","created_at":"2026-06-23T01:12:28.560991+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.21059v1","created_at":"2026-06-23T01:12:28.560991+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.21059","created_at":"2026-06-23T01:12:28.560991+00:00"},{"alias_kind":"pith_short_12","alias_value":"S2NAK5KK2VPG","created_at":"2026-06-23T01:12:28.560991+00:00"},{"alias_kind":"pith_short_16","alias_value":"S2NAK5KK2VPGFHFT","created_at":"2026-06-23T01:12:28.560991+00:00"},{"alias_kind":"pith_short_8","alias_value":"S2NAK5KK","created_at":"2026-06-23T01:12:28.560991+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/S2NAK5KK2VPGFHFTWINITRZSWJ","json":"https://pith.science/pith/S2NAK5KK2VPGFHFTWINITRZSWJ.json","graph_json":"https://pith.science/api/pith-number/S2NAK5KK2VPGFHFTWINITRZSWJ/graph.json","events_json":"https://pith.science/api/pith-number/S2NAK5KK2VPGFHFTWINITRZSWJ/events.json","paper":"https://pith.science/paper/S2NAK5KK"},"agent_actions":{"view_html":"https://pith.science/pith/S2NAK5KK2VPGFHFTWINITRZSWJ","download_json":"https://pith.science/pith/S2NAK5KK2VPGFHFTWINITRZSWJ.json","view_paper":"https://pith.science/paper/S2NAK5KK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.21059&json=true","fetch_graph":"https://pith.science/api/pith-number/S2NAK5KK2VPGFHFTWINITRZSWJ/graph.json","fetch_events":"https://pith.science/api/pith-number/S2NAK5KK2VPGFHFTWINITRZSWJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/S2NAK5KK2VPGFHFTWINITRZSWJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/S2NAK5KK2VPGFHFTWINITRZSWJ/action/storage_attestation","attest_author":"https://pith.science/pith/S2NAK5KK2VPGFHFTWINITRZSWJ/action/author_attestation","sign_citation":"https://pith.science/pith/S2NAK5KK2VPGFHFTWINITRZSWJ/action/citation_signature","submit_replication":"https://pith.science/pith/S2NAK5KK2VPGFHFTWINITRZSWJ/action/replication_record"}},"created_at":"2026-06-23T01:12:28.560991+00:00","updated_at":"2026-06-23T01:12:28.560991+00:00"}