{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:BB7QLCCG7HU4UQJPEISJY2XIZ2","short_pith_number":"pith:BB7QLCCG","schema_version":"1.0","canonical_sha256":"087f058846f9e9ca412f22249c6ae8cea06c6cb0739e86e5a11097256be718ad","source":{"kind":"arxiv","id":"2606.30479","version":1},"attestation_state":"computed","paper":{"title":"COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.MA"],"primary_cat":"cs.NI","authors_text":"Abed Showgan, Andres Murillo, Asaf Shabtai, Aviram Zilberman, Chen Frydman, Rami Puzis, Rubin Krief, Sekiya Motoyoshi, Yuval Elovici","submitted_at":"2026-06-29T15:39:05Z","abstract_excerpt":"Mitigating an observed adversary in an enterprise network typically takes weeks of expert work: an analyst derives a mitigation tailored to that adversary, validates it without breaking production, and verifies it disrupts the specific attack. The procedure relies on expert judgment and cannot safely be exercised against the production network. COHORT is the first end-to-end framework to automate this procedure for deployable mitigations. A role-decomposed multi-agent LLM workflow proposes candidates, implements them as real device commands, and refines them through a critique loop, all on a h"},"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":"2606.30479","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.NI","submitted_at":"2026-06-29T15:39:05Z","cross_cats_sorted":["cs.AI","cs.CR","cs.MA"],"title_canon_sha256":"87a9a2071ac64df5f0749056ee80c95cc07eb5c7584c9cc2f1835036f6206c9b","abstract_canon_sha256":"c2b4462185a03ebf1c6b70bf357d32307cdb0406123a1be73f9aaac976adf5e7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T02:18:17.089176Z","signature_b64":"2fP2IFd3UhfBE2A9acPNb7iXlBppqClfmwj9CwVpq9jf8TAuG5krR/evGXB/h/K68jIuT3JSKwXWH31mffocDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"087f058846f9e9ca412f22249c6ae8cea06c6cb0739e86e5a11097256be718ad","last_reissued_at":"2026-06-30T02:18:17.088554Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T02:18:17.088554Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"COHORT: Collaborative Orchestration for Hardening via Offensive Replay on Emulated Topologies","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR","cs.MA"],"primary_cat":"cs.NI","authors_text":"Abed Showgan, Andres Murillo, Asaf Shabtai, Aviram Zilberman, Chen Frydman, Rami Puzis, Rubin Krief, Sekiya Motoyoshi, Yuval Elovici","submitted_at":"2026-06-29T15:39:05Z","abstract_excerpt":"Mitigating an observed adversary in an enterprise network typically takes weeks of expert work: an analyst derives a mitigation tailored to that adversary, validates it without breaking production, and verifies it disrupts the specific attack. The procedure relies on expert judgment and cannot safely be exercised against the production network. COHORT is the first end-to-end framework to automate this procedure for deployable mitigations. A role-decomposed multi-agent LLM workflow proposes candidates, implements them as real device commands, and refines them through a critique loop, all on a h"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.30479","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/2606.30479/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":"2606.30479","created_at":"2026-06-30T02:18:17.088642+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.30479v1","created_at":"2026-06-30T02:18:17.088642+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.30479","created_at":"2026-06-30T02:18:17.088642+00:00"},{"alias_kind":"pith_short_12","alias_value":"BB7QLCCG7HU4","created_at":"2026-06-30T02:18:17.088642+00:00"},{"alias_kind":"pith_short_16","alias_value":"BB7QLCCG7HU4UQJP","created_at":"2026-06-30T02:18:17.088642+00:00"},{"alias_kind":"pith_short_8","alias_value":"BB7QLCCG","created_at":"2026-06-30T02:18:17.088642+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/BB7QLCCG7HU4UQJPEISJY2XIZ2","json":"https://pith.science/pith/BB7QLCCG7HU4UQJPEISJY2XIZ2.json","graph_json":"https://pith.science/api/pith-number/BB7QLCCG7HU4UQJPEISJY2XIZ2/graph.json","events_json":"https://pith.science/api/pith-number/BB7QLCCG7HU4UQJPEISJY2XIZ2/events.json","paper":"https://pith.science/paper/BB7QLCCG"},"agent_actions":{"view_html":"https://pith.science/pith/BB7QLCCG7HU4UQJPEISJY2XIZ2","download_json":"https://pith.science/pith/BB7QLCCG7HU4UQJPEISJY2XIZ2.json","view_paper":"https://pith.science/paper/BB7QLCCG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.30479&json=true","fetch_graph":"https://pith.science/api/pith-number/BB7QLCCG7HU4UQJPEISJY2XIZ2/graph.json","fetch_events":"https://pith.science/api/pith-number/BB7QLCCG7HU4UQJPEISJY2XIZ2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BB7QLCCG7HU4UQJPEISJY2XIZ2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BB7QLCCG7HU4UQJPEISJY2XIZ2/action/storage_attestation","attest_author":"https://pith.science/pith/BB7QLCCG7HU4UQJPEISJY2XIZ2/action/author_attestation","sign_citation":"https://pith.science/pith/BB7QLCCG7HU4UQJPEISJY2XIZ2/action/citation_signature","submit_replication":"https://pith.science/pith/BB7QLCCG7HU4UQJPEISJY2XIZ2/action/replication_record"}},"created_at":"2026-06-30T02:18:17.088642+00:00","updated_at":"2026-06-30T02:18:17.088642+00:00"}