When to Trust the Map: Confidence-Aware LLM Routing for Automotive CVE-To-ATM Mapping

Public CVE descriptions report the technical conditions and impact of vulnerabilities, whereas the Auto-ISAC Automotive Threat Matrix ATM expresses an adversary's tactics and techniques. Because the two representations are not directly aligned, incorrect automated mappings in safety-critical...

Evaluating Large Language Models for Phishing Detection, Self-Consistency, Faithfulness, and Explainability

Phishing attacks remain one of the most prevalent and persistent cybersecurity threat with attackers continuously evolving and intensifying tactics to evade the general detection system. Despite significant advances in artificial intelligence and machine learning, faithfully reproducing the...

Towards Effective Complementary Security Analysis Using Large Language Models

A key challenge in security analysis is the manual evaluation of potential security weaknesses generated by static application security testing SAST tools. Numerous false positives FPs in these reports reduce the effectiveness of security analysis. We propose using Large Language Models LLMs to...