Confidence-Aware LLM Routing for Automotive CVE-to-ATM Mapping
A new framework reformulates the task of mapping automotive CVEs to the Auto-ISAC Automotive Threat Matrix (ATM) as a selective automation problem. Using hierarchical in-context learning and a calibrated meta-model, the system assigns mappings to AUTO, REVIEW, or HOLD tiers based on confidence. In evaluation, the AUTO tier achieved a precision of 0.878 in high-confidence mode, substantially outperforming a zero-shot GPT-5.2 baseline.
Why it matters: This approach advances automated vulnerability mapping in automotive cybersecurity by enabling selective automation and reducing the risk of misclassification in safety-critical contexts.
Full story at: arXiv Cryptography and Security ↗