The field of software security is witnessing significant advancements with the integration of Large Language Models (LLMs). Recent developments indicate a shift towards leveraging LLMs to improve the precision and reliability of static analysis tools, enhancing vulnerability detection, and promoting more secure software development practices. Notably, researchers are exploring the potential of LLMs in identifying complex vulnerabilities, logical flaws, and reducing false positives in static application security testing. Furthermore, studies are investigating the limitations and biases of LLMs in conservation and species evaluation, highlighting the need for human oversight in judgment-based decisions. The use of LLMs in software security is becoming increasingly prominent, with applications in vulnerability detection, code analysis, and security risk assessment.

Noteworthy papers include: ZeroFalse, which presents a framework that integrates static analysis with LLMs to reduce false positives while preserving coverage. Real-VulLLM, which explores the capability of LLMs for vulnerability detection in real-world scenarios. FineSec, which harnesses LLMs through knowledge distillation to enable efficient and precise vulnerability identification in C/C++ codebases.