Using LLMs to Find and Prioritize Vulnerabilities Is No Easy Task
Summary
Large language models (LLMs) are proving to be challenging to use effectively for finding and prioritizing software vulnerabilities. Despite advancements, LLMs currently exhibit high false-positive rates and struggle to incorporate the contextual information necessary for accurate vulnerability assessment, increasing the workload for application security professionals.
IFF Assessment
The article highlights limitations and inefficiencies when using LLMs for vulnerability management, which can hinder defender efforts.
Defender Context
While the promise of AI in cybersecurity is high, current LLM applications for vulnerability discovery show significant limitations. Defenders should be aware that relying solely on LLMs for vulnerability identification may lead to wasted effort due to high false-positive rates, and human expertise remains critical for accurate prioritization and remediation.