{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:FX3DX4K4DHOWKUWBJH27QEA3NY","short_pith_number":"pith:FX3DX4K4","schema_version":"1.0","canonical_sha256":"2df63bf15c19dd6552c149f5f8101b6e22a9d58211175a56c87489aa2da2795a","source":{"kind":"arxiv","id":"2605.10807","version":3},"attestation_state":"computed","paper":{"title":"LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Large language models can generate hardware designs and detect vulnerabilities but also introduce severe security risks in semiconductors.","cross_cats":["cs.AR","cs.LG"],"primary_cat":"cs.CR","authors_text":"Johann Knechtel, Ozgur Sinanoglu, Ramesh Karri","submitted_at":"2026-05-11T16:31:14Z","abstract_excerpt":"The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry. While LLMs offer unprecedented capabilities in generating Register Transfer Level (RTL) code, automating testbenches, and bridging the semantic gap between high-level specifications and silicon, they simultaneously introduce severe vulnerabilities. This comprehensive review provides an in-depth analysis of the state-of-the-art in LLM-driven hardware design, organized around key advancements in EDA synthesis, hardware trust, design for se"},"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":"2605.10807","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2026-05-11T16:31:14Z","cross_cats_sorted":["cs.AR","cs.LG"],"title_canon_sha256":"22d9bf8c05d7ffef2304aa6bc978a6837ea44b6cd9f92cfe907ad92a2a9b5396","abstract_canon_sha256":"63568d83543f57b9f1a0b623583e5656f57e8a0d8510a49abf142f24954af596"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:04:27.574216Z","signature_b64":"wSPvbCXnzdWoF7fIGqoRnduej8lzlwAdBXb3xvl5C4mVD0T+HCto700bm7Jk6jL0mNRT4ZcfcHqk+vRXG3w0CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2df63bf15c19dd6552c149f5f8101b6e22a9d58211175a56c87489aa2da2795a","last_reissued_at":"2026-05-21T01:04:27.573355Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:04:27.573355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"LLMs for Secure Hardware Design and Related Problems: Opportunities and Challenges","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"Large language models can generate hardware designs and detect vulnerabilities but also introduce severe security risks in semiconductors.","cross_cats":["cs.AR","cs.LG"],"primary_cat":"cs.CR","authors_text":"Johann Knechtel, Ozgur Sinanoglu, Ramesh Karri","submitted_at":"2026-05-11T16:31:14Z","abstract_excerpt":"The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry. While LLMs offer unprecedented capabilities in generating Register Transfer Level (RTL) code, automating testbenches, and bridging the semantic gap between high-level specifications and silicon, they simultaneously introduce severe vulnerabilities. This comprehensive review provides an in-depth analysis of the state-of-the-art in LLM-driven hardware design, organized around key advancements in EDA synthesis, hardware trust, design for se"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry while simultaneously introducing severe vulnerabilities.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That the reviewed methodologies from recent breakthroughs accurately capture the current state of LLM-driven hardware design and that the suggested countermeasures such as dynamic benchmarking will prove effective in practice.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"LLMs enable RTL code generation and vulnerability analysis in hardware design but introduce data contamination and adversarial risks that require red-teaming and dynamic benchmarking.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"Large language models can generate hardware designs and detect vulnerabilities but also introduce severe security risks in semiconductors.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"3879c7e01e892f81dc5e3cebb92946b9bced4e66876399f8cde146318731de1c"},"source":{"id":"2605.10807","kind":"arxiv","version":3},"verdict":{"id":"ae51e310-1c62-4a4a-b2da-04d01fb563a5","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-14T21:09:31.956072Z","strongest_claim":"The integration of Large Language Models (LLMs) into Electronic Design Automation (EDA) and hardware security is rapidly reshaping the semiconductor industry while simultaneously introducing severe vulnerabilities.","one_line_summary":"LLMs enable RTL code generation and vulnerability analysis in hardware design but introduce data contamination and adversarial risks that require red-teaming and dynamic benchmarking.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That the reviewed methodologies from recent breakthroughs accurately capture the current state of LLM-driven hardware design and that the suggested countermeasures such as dynamic benchmarking will prove effective in practice.","pith_extraction_headline":"Large language models can generate hardware designs and detect vulnerabilities but also introduce severe security risks in semiconductors."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.10807/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"claim_evidence","ran_at":"2026-05-20T05:22:00.366962Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T14:35:23.824044Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_title_agreement","ran_at":"2026-05-19T10:31:17.709508Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-19T08:58:40.851967Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"bc554d925bdd4d9329d69d560f6a75c81352a7e7e7279bbaa662c1be53747ba8"},"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":"2605.10807","created_at":"2026-05-21T01:04:27.573439+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.10807v3","created_at":"2026-05-21T01:04:27.573439+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.10807","created_at":"2026-05-21T01:04:27.573439+00:00"},{"alias_kind":"pith_short_12","alias_value":"FX3DX4K4DHOW","created_at":"2026-05-21T01:04:27.573439+00:00"},{"alias_kind":"pith_short_16","alias_value":"FX3DX4K4DHOWKUWB","created_at":"2026-05-21T01:04:27.573439+00:00"},{"alias_kind":"pith_short_8","alias_value":"FX3DX4K4","created_at":"2026-05-21T01:04:27.573439+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/FX3DX4K4DHOWKUWBJH27QEA3NY","json":"https://pith.science/pith/FX3DX4K4DHOWKUWBJH27QEA3NY.json","graph_json":"https://pith.science/api/pith-number/FX3DX4K4DHOWKUWBJH27QEA3NY/graph.json","events_json":"https://pith.science/api/pith-number/FX3DX4K4DHOWKUWBJH27QEA3NY/events.json","paper":"https://pith.science/paper/FX3DX4K4"},"agent_actions":{"view_html":"https://pith.science/pith/FX3DX4K4DHOWKUWBJH27QEA3NY","download_json":"https://pith.science/pith/FX3DX4K4DHOWKUWBJH27QEA3NY.json","view_paper":"https://pith.science/paper/FX3DX4K4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.10807&json=true","fetch_graph":"https://pith.science/api/pith-number/FX3DX4K4DHOWKUWBJH27QEA3NY/graph.json","fetch_events":"https://pith.science/api/pith-number/FX3DX4K4DHOWKUWBJH27QEA3NY/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FX3DX4K4DHOWKUWBJH27QEA3NY/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FX3DX4K4DHOWKUWBJH27QEA3NY/action/storage_attestation","attest_author":"https://pith.science/pith/FX3DX4K4DHOWKUWBJH27QEA3NY/action/author_attestation","sign_citation":"https://pith.science/pith/FX3DX4K4DHOWKUWBJH27QEA3NY/action/citation_signature","submit_replication":"https://pith.science/pith/FX3DX4K4DHOWKUWBJH27QEA3NY/action/replication_record"}},"created_at":"2026-05-21T01:04:27.573439+00:00","updated_at":"2026-05-21T01:04:27.573439+00:00"}