{"data":{"id":"b2210100-f71b-46a6-a784-9728779da4ae","title":"Enhancing LLM-Based Vulnerability Detection via Representation-Aware Logical Ensemble","summary":"This research studies LLM-based vulnerability detection in C source code across three code representations: raw source, Abstract Syntax Trees (AST), and Control Flow Graphs (CFG). The authors propose a Representation-Aware Logical Ensemble (RALE) that combines LLMs trained on different representations using explicit logical rules and majority voting. RALE reaches a 78% F1-score, compared with up to 73% for single-representation models.","solution":"N/A -- no mitigation discussed in source.","labels":["research","security"],"sourceUrl":"https://arxiv.org/abs/2610.33756v1","publishedAt":"2026-10-24T15:55:52.000Z","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":"2026-10-24T15:55:52.000Z","capecIds":null,"crossRefCount":0,"attackSophistication":"moderate","impactType":null,"aiComponentTargeted":null,"llmSpecific":true,"classifierConfidence":0.85,"researchCategory":"preprint","atlasIds":null}}