{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YCIFSFPZMLWWVNR6VNB5ICH7BX","short_pith_number":"pith:YCIFSFPZ","schema_version":"1.0","canonical_sha256":"c0905915f962ed6ab63eab43d408ff0de81006c573a4d1f3ef29b5d9cbdc8425","source":{"kind":"arxiv","id":"2606.23197","version":1},"attestation_state":"computed","paper":{"title":"The EVerest Dataset for Secure Software Engineering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.SE","authors_text":"Anne Koziolek, Debora Grupp, Dominik Fuch{\\ss}, Frederik Reiche, Jan Keim, Sophie Corallo, Tobias Hey","submitted_at":"2026-06-22T11:44:58Z","abstract_excerpt":"End-to-end security verification, from requirements through architecture to code, requires datasets that span all three artifact types with fine-grained security labels. No existing dataset provides this combination. We present the EVerest dataset, a multi-artifact resource based on EVerest, an industry-driven open-source software stack for electric vehicle charging stations. The dataset includes 84 manually elicited security requirements annotated with security objectives, 1,445 fine-grained security elements (components, entities, data, data flows, states, etc.), acceptance windows, corefere"},"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":"2606.23197","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.SE","submitted_at":"2026-06-22T11:44:58Z","cross_cats_sorted":["cs.CR"],"title_canon_sha256":"920e4fbcc2f33890b81c3b85f2e24916d511beefdfc4d7412115fcd0b1f3e73a","abstract_canon_sha256":"66973416a56fbe0356792267ab5db2e154165f4ef5fa4f39141c70a076043e6e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T03:14:12.736158Z","signature_b64":"m9jrebINEys/9Yy6fF35CroHpZ5EnkL/P7B9eiznLKMbCIlTYeKchVwF9jECZ/KwWp6ICK4Ful/S0LhNR7RqBg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c0905915f962ed6ab63eab43d408ff0de81006c573a4d1f3ef29b5d9cbdc8425","last_reissued_at":"2026-06-23T03:14:12.735750Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T03:14:12.735750Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The EVerest Dataset for Secure Software Engineering","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CR"],"primary_cat":"cs.SE","authors_text":"Anne Koziolek, Debora Grupp, Dominik Fuch{\\ss}, Frederik Reiche, Jan Keim, Sophie Corallo, Tobias Hey","submitted_at":"2026-06-22T11:44:58Z","abstract_excerpt":"End-to-end security verification, from requirements through architecture to code, requires datasets that span all three artifact types with fine-grained security labels. No existing dataset provides this combination. We present the EVerest dataset, a multi-artifact resource based on EVerest, an industry-driven open-source software stack for electric vehicle charging stations. The dataset includes 84 manually elicited security requirements annotated with security objectives, 1,445 fine-grained security elements (components, entities, data, data flows, states, etc.), acceptance windows, corefere"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.23197","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/2606.23197/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":"2606.23197","created_at":"2026-06-23T03:14:12.735819+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.23197v1","created_at":"2026-06-23T03:14:12.735819+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.23197","created_at":"2026-06-23T03:14:12.735819+00:00"},{"alias_kind":"pith_short_12","alias_value":"YCIFSFPZMLWW","created_at":"2026-06-23T03:14:12.735819+00:00"},{"alias_kind":"pith_short_16","alias_value":"YCIFSFPZMLWWVNR6","created_at":"2026-06-23T03:14:12.735819+00:00"},{"alias_kind":"pith_short_8","alias_value":"YCIFSFPZ","created_at":"2026-06-23T03:14:12.735819+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.05913","citing_title":"xDECAF: An Extensible Data Flow Diagram Analysis Framework for Information Security","ref_index":8,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YCIFSFPZMLWWVNR6VNB5ICH7BX","json":"https://pith.science/pith/YCIFSFPZMLWWVNR6VNB5ICH7BX.json","graph_json":"https://pith.science/api/pith-number/YCIFSFPZMLWWVNR6VNB5ICH7BX/graph.json","events_json":"https://pith.science/api/pith-number/YCIFSFPZMLWWVNR6VNB5ICH7BX/events.json","paper":"https://pith.science/paper/YCIFSFPZ"},"agent_actions":{"view_html":"https://pith.science/pith/YCIFSFPZMLWWVNR6VNB5ICH7BX","download_json":"https://pith.science/pith/YCIFSFPZMLWWVNR6VNB5ICH7BX.json","view_paper":"https://pith.science/paper/YCIFSFPZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.23197&json=true","fetch_graph":"https://pith.science/api/pith-number/YCIFSFPZMLWWVNR6VNB5ICH7BX/graph.json","fetch_events":"https://pith.science/api/pith-number/YCIFSFPZMLWWVNR6VNB5ICH7BX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YCIFSFPZMLWWVNR6VNB5ICH7BX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YCIFSFPZMLWWVNR6VNB5ICH7BX/action/storage_attestation","attest_author":"https://pith.science/pith/YCIFSFPZMLWWVNR6VNB5ICH7BX/action/author_attestation","sign_citation":"https://pith.science/pith/YCIFSFPZMLWWVNR6VNB5ICH7BX/action/citation_signature","submit_replication":"https://pith.science/pith/YCIFSFPZMLWWVNR6VNB5ICH7BX/action/replication_record"}},"created_at":"2026-06-23T03:14:12.735819+00:00","updated_at":"2026-06-23T03:14:12.735819+00:00"}