{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:VR5AL5EXK3X2YFWB4UKXDAPACX","short_pith_number":"pith:VR5AL5EX","schema_version":"1.0","canonical_sha256":"ac7a05f49756efac16c1e5157181e015ea0319715a28233f4ada1052ca8b1cf3","source":{"kind":"arxiv","id":"2608.08100","version":1},"attestation_state":"computed","paper":{"title":"Defending Retrieval-Augmented Intrusion Detection Against Knowledge Poisoning and Prompt Injection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Atsuo Inomata, Kaysarul Anas Apurba, Mahedee Zaman Moon, Md. Hasibul Hasan, Sk. Md. Mizanur Rahman","submitted_at":"2026-08-08T12:29:47Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) enables large language models to classify network flows and generate human-readable incident reports by retrieving semantically similar historical traffic from a vector knowledge base. However, the retrieval layer introduces vulnerabilities to knowledge poisoning and prompt-injection attacks. We present RAG-IDS, a three-tier multi-agent intrusion detection framework with a retrieval-boundary defense combining soft trust scoring, label-embedding consistency checking (LECC), and prompt sanitization, designed to recover classification quality under retrieval-l"},"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":"2608.08100","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CR","submitted_at":"2026-08-08T12:29:47Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"0c1206bd4498a70f6bfbabebaab6c41157ffac13c27334ab467c3e6fc8d3465f","abstract_canon_sha256":"ed75cb25ee87dc71bb47c7e097a5de4ef4d777df6ad6249ae95b05ba5db8d98b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-11T01:21:41.732758Z","signature_b64":"Iwo7K8UnjoTlZApE3xpG+VWkaUv7IDz+8ywuhnoAm8fD7jA7lLANELzcd2EaRDZ4gyddh3t4YZs+7brkaxlLAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac7a05f49756efac16c1e5157181e015ea0319715a28233f4ada1052ca8b1cf3","last_reissued_at":"2026-08-11T01:21:41.730429Z","signature_status":"signed_v1","first_computed_at":"2026-08-11T01:21:41.730429Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Defending Retrieval-Augmented Intrusion Detection Against Knowledge Poisoning and Prompt Injection","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.CR","authors_text":"Atsuo Inomata, Kaysarul Anas Apurba, Mahedee Zaman Moon, Md. Hasibul Hasan, Sk. Md. Mizanur Rahman","submitted_at":"2026-08-08T12:29:47Z","abstract_excerpt":"Retrieval-Augmented Generation (RAG) enables large language models to classify network flows and generate human-readable incident reports by retrieving semantically similar historical traffic from a vector knowledge base. However, the retrieval layer introduces vulnerabilities to knowledge poisoning and prompt-injection attacks. We present RAG-IDS, a three-tier multi-agent intrusion detection framework with a retrieval-boundary defense combining soft trust scoring, label-embedding consistency checking (LECC), and prompt sanitization, designed to recover classification quality under retrieval-l"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.08100","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/2608.08100/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":"2608.08100","created_at":"2026-08-11T01:21:41.731164+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.08100v1","created_at":"2026-08-11T01:21:41.731164+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.08100","created_at":"2026-08-11T01:21:41.731164+00:00"},{"alias_kind":"pith_short_12","alias_value":"VR5AL5EXK3X2","created_at":"2026-08-11T01:21:41.731164+00:00"},{"alias_kind":"pith_short_16","alias_value":"VR5AL5EXK3X2YFWB","created_at":"2026-08-11T01:21:41.731164+00:00"},{"alias_kind":"pith_short_8","alias_value":"VR5AL5EX","created_at":"2026-08-11T01:21:41.731164+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/VR5AL5EXK3X2YFWB4UKXDAPACX","json":"https://pith.science/pith/VR5AL5EXK3X2YFWB4UKXDAPACX.json","graph_json":"https://pith.science/api/pith-number/VR5AL5EXK3X2YFWB4UKXDAPACX/graph.json","events_json":"https://pith.science/api/pith-number/VR5AL5EXK3X2YFWB4UKXDAPACX/events.json","paper":"https://pith.science/paper/VR5AL5EX"},"agent_actions":{"view_html":"https://pith.science/pith/VR5AL5EXK3X2YFWB4UKXDAPACX","download_json":"https://pith.science/pith/VR5AL5EXK3X2YFWB4UKXDAPACX.json","view_paper":"https://pith.science/paper/VR5AL5EX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.08100&json=true","fetch_graph":"https://pith.science/api/pith-number/VR5AL5EXK3X2YFWB4UKXDAPACX/graph.json","fetch_events":"https://pith.science/api/pith-number/VR5AL5EXK3X2YFWB4UKXDAPACX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VR5AL5EXK3X2YFWB4UKXDAPACX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VR5AL5EXK3X2YFWB4UKXDAPACX/action/storage_attestation","attest_author":"https://pith.science/pith/VR5AL5EXK3X2YFWB4UKXDAPACX/action/author_attestation","sign_citation":"https://pith.science/pith/VR5AL5EXK3X2YFWB4UKXDAPACX/action/citation_signature","submit_replication":"https://pith.science/pith/VR5AL5EXK3X2YFWB4UKXDAPACX/action/replication_record"}},"created_at":"2026-08-11T01:21:41.731164+00:00","updated_at":"2026-08-11T01:21:41.731164+00:00"}