{"data":{"id":"dd2b40ea-bceb-43f8-9d94-5cd435b560df","title":"CAT-RAG: Improving Secure Code Generation with Category-Aware Agentic Retrieval","summary":"CAT-RAG is an agentic retrieval-augmented code generation framework that uses an LLM categorizer agent to select compact, language-specific security categories in place of CWE-level retrieval. It works standalone or as a drop-in add-on to RACG pipelines without modifying the underlying model. On CyberSecEval it improves secure code generation by 23.57% versus 12.11% for the prior state of the art, while cutting token overhead by 41.71%.","solution":"N/A -- no mitigation discussed in source.","labels":["security","research"],"sourceUrl":"https://arxiv.org/abs/2610.24907v1","publishedAt":"2026-10-17T23:09:33.000Z","cveId":null,"cweIds":null,"cvssScore":null,"cvssSeverity":null,"severity":"info","attackType":[],"issueType":"research","affectedPackages":null,"affectedPackageNames":null,"affectedPackageRefs":null,"affectedVendors":[],"affectedVendorsRaw":[],"classifierModel":"claude-haiku-5-5","classifierPromptVersion":"v4","summaryPromptVersion":"v2","headline":null,"headlinePromptVersion":null,"cvssVector":null,"attackVector":null,"attackComplexity":null,"privilegesRequired":null,"userInteraction":null,"exploitMaturity":null,"epssScore":null,"epssCheckedAt":null,"kevDateAdded":null,"advisoryAliases":null,"affectedPackagesSource":null,"affectedPackagesCheckedAt":null,"patchAvailable":null,"disclosureDate":"2026-10-17T23:09:33.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":["integrity"],"aiComponentTargeted":"framework","llmSpecific":true,"classifierConfidence":0.93,"researchCategory":"preprint","atlasIds":null}}