Why LLMs Fall Short in Vulnerability Management

Explore the critical limitations of Large Language Models in cybersecurity through this 39-minute conference talk that challenges the widespread hype surrounding LLMs as transformative tools for vulnerability management. Examine why LLMs, despite their impressive language understanding and contextual reasoning capabilities, often fail to meet the demands of modern security operations across key areas including open source dependencies, contextual prioritization, supply-chain attacks, and operational remediation. Delve into existing research and tools to understand the specific limitations and risks of LLMs in vulnerability management, covering issues such as false positives in vulnerability prioritization, challenges in updating vulnerable open-source dependencies, difficulties in detecting supply chain attacks, and limitations in enhancing vulnerability fuzzers through dynamic testing. Analyze quantitative research findings from real-world scenarios that demonstrate the accuracy challenges LLMs face when identifying software vulnerabilities and verifying vulnerability fixes. Discover what technological advancements are required to make LLMs more reliable for vulnerability management tasks, while learning proven tips and practical strategies that work effectively today. Gain insights into an open-source tool designed to flag suspicious code commits in open-source components, presented by cybersecurity expert Itamar Sher, who brings over 15 years of experience from elite intelligence units and cybersecurity research roles.