A new arXiv paper introduces TyPatch, a method that converts historical Linux kernel patches into typestate rules usable by static analysis. Building on prior work where large language models generate checkers from past patches, the approach aims to extend the reach of defect knowledge beyond the sites where bugs were originally fixed. The technique targets automated kernel bug detection.
A new arXiv paper investigates whether large language models can help identify security flaws in JavaScript, which underpins the vast majority of websites. The authors note that conventional static analysis tools frequently miss real-world vulnerabilities, motivating a learning-based approach. The work is a research preprint and has not yet been peer reviewed.
A new arXiv paper examines cases where Python code passes static security checks but fails when actually executed, a discrepancy the authors call the static-pass dynamic-fail gap. The work focuses on security-sensitive code as well as code produced by large language models, arguing that static analysis alone is insufficient as a security gate. The authors frame the measurement as a step beyond reliance on static guarantees for evaluating generated software.