{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:BO5YPNNZ6PLGCOVNXJUA63BZBF","short_pith_number":"pith:BO5YPNNZ","schema_version":"1.0","canonical_sha256":"0bbb87b5b9f3d6613aadba680f6c390947dc031a91e20b89b0f7c16acd4b2490","source":{"kind":"arxiv","id":"2203.10597","version":1},"attestation_state":"computed","paper":{"title":"The Dark Side: Security Concerns in Machine Learning for EDA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.CR","authors_text":"Chen-Chia Chang, Jingyu Pan, Yiran Chen, Zhiyao Xie","submitted_at":"2022-03-20T16:44:25Z","abstract_excerpt":"The growing IC complexity has led to a compelling need for design efficiency improvement through new electronic design automation (EDA) methodologies. In recent years, many unprecedented efficient EDA methods have been enabled by machine learning (ML) techniques. While ML demonstrates its great potential in circuit design, however, the dark side about security problems, is seldomly discussed. This paper gives a comprehensive and impartial summary of all security concerns we have observed in ML for EDA. Many of them are hidden or neglected by practitioners in this field. In this paper, we first"},"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":"2203.10597","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CR","submitted_at":"2022-03-20T16:44:25Z","cross_cats_sorted":["cs.LG","cs.SY","eess.SY"],"title_canon_sha256":"f0ecb20eab3da3ccea285a18cdcd2b2eadcf1988807a2cfb0165b0589e5fbe43","abstract_canon_sha256":"3764ccf1b48c09bb49be9dbedc30c1298b064dd0560f74848d53e0c266f878cd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:06:54.902376Z","signature_b64":"oOLizxFnFat5ioyzdSwrUNVHUtKk/m2bqLfNNqmVddt90CQUwI5RIQlkJGy6JIypyJ5IYqadebpTIrG7kdF1DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0bbb87b5b9f3d6613aadba680f6c390947dc031a91e20b89b0f7c16acd4b2490","last_reissued_at":"2026-07-05T04:06:54.901989Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:06:54.901989Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Dark Side: Security Concerns in Machine Learning for EDA","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","cs.SY","eess.SY"],"primary_cat":"cs.CR","authors_text":"Chen-Chia Chang, Jingyu Pan, Yiran Chen, Zhiyao Xie","submitted_at":"2022-03-20T16:44:25Z","abstract_excerpt":"The growing IC complexity has led to a compelling need for design efficiency improvement through new electronic design automation (EDA) methodologies. In recent years, many unprecedented efficient EDA methods have been enabled by machine learning (ML) techniques. While ML demonstrates its great potential in circuit design, however, the dark side about security problems, is seldomly discussed. This paper gives a comprehensive and impartial summary of all security concerns we have observed in ML for EDA. Many of them are hidden or neglected by practitioners in this field. In this paper, we first"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.10597","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/2203.10597/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":"2203.10597","created_at":"2026-07-05T04:06:54.902045+00:00"},{"alias_kind":"arxiv_version","alias_value":"2203.10597v1","created_at":"2026-07-05T04:06:54.902045+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.10597","created_at":"2026-07-05T04:06:54.902045+00:00"},{"alias_kind":"pith_short_12","alias_value":"BO5YPNNZ6PLG","created_at":"2026-07-05T04:06:54.902045+00:00"},{"alias_kind":"pith_short_16","alias_value":"BO5YPNNZ6PLGCOVN","created_at":"2026-07-05T04:06:54.902045+00:00"},{"alias_kind":"pith_short_8","alias_value":"BO5YPNNZ","created_at":"2026-07-05T04:06:54.902045+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/BO5YPNNZ6PLGCOVNXJUA63BZBF","json":"https://pith.science/pith/BO5YPNNZ6PLGCOVNXJUA63BZBF.json","graph_json":"https://pith.science/api/pith-number/BO5YPNNZ6PLGCOVNXJUA63BZBF/graph.json","events_json":"https://pith.science/api/pith-number/BO5YPNNZ6PLGCOVNXJUA63BZBF/events.json","paper":"https://pith.science/paper/BO5YPNNZ"},"agent_actions":{"view_html":"https://pith.science/pith/BO5YPNNZ6PLGCOVNXJUA63BZBF","download_json":"https://pith.science/pith/BO5YPNNZ6PLGCOVNXJUA63BZBF.json","view_paper":"https://pith.science/paper/BO5YPNNZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2203.10597&json=true","fetch_graph":"https://pith.science/api/pith-number/BO5YPNNZ6PLGCOVNXJUA63BZBF/graph.json","fetch_events":"https://pith.science/api/pith-number/BO5YPNNZ6PLGCOVNXJUA63BZBF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BO5YPNNZ6PLGCOVNXJUA63BZBF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BO5YPNNZ6PLGCOVNXJUA63BZBF/action/storage_attestation","attest_author":"https://pith.science/pith/BO5YPNNZ6PLGCOVNXJUA63BZBF/action/author_attestation","sign_citation":"https://pith.science/pith/BO5YPNNZ6PLGCOVNXJUA63BZBF/action/citation_signature","submit_replication":"https://pith.science/pith/BO5YPNNZ6PLGCOVNXJUA63BZBF/action/replication_record"}},"created_at":"2026-07-05T04:06:54.902045+00:00","updated_at":"2026-07-05T04:06:54.902045+00:00"}