Enhancing Security Alert Triage with LLMs
Triaging security alerts often faces challenges with false positives due to complex patterns that traditional tools struggle to decode. At GitHub Security Lab, we’re leveraging large language models (LLMs) within our GitHub Security Lab Taskflow Agent AI framework to streamline this process effectively. Our recent blog post explores the creation of tailored triage taskflows, showcasing significant outcomes, including identifying approximately 30 real-world vulnerabilities since August.
These taskflows, written in YAML, break down repetitive tasks into smaller, manageable steps, ensuring clarity and efficiency in alert handling. They incorporate specific criteria to accurately distinguish valid alerts from false positives, significantly enhancing the auditing process. Additionally, our open-source repositories allow others to develop similar LLM taskflows tailored to their needs.
By automating triage workflows, we’ve decreased false positive rates while uncovering genuine security flaws in code scanning alerts. Explore the full guide on setting up the GitHub Security Lab Taskflow Agent for optimal results.
