GitHub Security Lab Leverages AI for Automated Vulnerability Triage

GitHub Security Lab Introduces AI-Powered Vulnerability Triage

The GitHub Security Lab has developed an AI-driven framework to automate the triage of security alerts, significantly reducing false positives and improving efficiency. Using the GitHub Security Lab Taskflow Agent, the team has successfully identified approximately 30 real-world vulnerabilities since August by leveraging large language models (LLMs) to analyze CodeQL-generated alerts.

How AI Enhances Vulnerability Triage

Traditional vulnerability triage is often repetitive, with false positives arising from patterns that are easily recognizable to human auditors but difficult to encode in static analysis tools. LLMs excel at identifying these "fuzzy" patterns, making them ideal for automating triage workflows.

The Taskflow Agent framework uses YAML-based taskflows to break down complex triage processes into smaller, manageable tasks. Each task is designed to:

• Fetch and analyze code scanning results
• Audit alerts for common false-positive patterns
• Generate detailed bug reports with precise code references
• Validate findings before creating GitHub Issues

By structuring the process this way, the system minimizes hallucinations (incorrect conclusions) and ensures consistency in results.

Technical Implementation

Taskflow Architecture

Taskflows are divided into stages:

1. Information Collection – Gathers relevant data (e.g., workflow triggers, permissions, sanitizers) and stores it in structured notes.
2. Audit Stage – Applies LLM-driven checks to filter out false positives (e.g., disabled workflows, permission restrictions).
3. Report Generation – Compiles findings into a standardized bug report with code snippets and line references.
4. Validation & Issue Creation – Ensures reports are complete before opening GitHub Issues for further review.

Key Features

• Modular Design – Tasks are independent, reducing context window limitations and improving debuggability.
• Asynchronous Processing – Supports batch operations for large-scale alert triage.
• Integration with MCP Servers – Offloads deterministic tasks (e.g., API calls) to traditional programming, improving accuracy.
• Reusable Components – Prompts and tasks can be shared across different triage workflows.

Results and Impact

Since deployment, the GitHub Security Lab has used these taskflows to triage alerts for:

• GitHub Actions (e.g., untrusted code checkout, code injection)
• JavaScript/TypeScript (e.g., client-side cross-site scripting via js/xss)

The system has proven effective in:

• Reducing false positives by identifying common dismissal patterns (e.g., disabled workflows, permission checks).
• Improving efficiency by automating repetitive checks (e.g., trigger events, sanitization).
• Enhancing accuracy through structured prompts and validation steps.

Recommendations for Security Teams

1. Adopt Modular AI Workflows – Break down triage into discrete, reusable tasks to improve scalability.
2. Combine LLMs with Traditional Tools – Use LLMs for pattern recognition while offloading deterministic tasks to static analysis or APIs.
3. Leverage Open-Source Frameworks – The seclab-taskflow-agent and seclab-taskflows repositories are publicly available for customization.
4. Validate AI Outputs – Always review LLM-generated reports before acting on vulnerabilities.
5. Monitor Resource Usage – Running taskflows can consume significant LLM quota; plan accordingly.

Future Directions

The GitHub Security Lab continues to refine AI-assisted triage, with ongoing experiments in code auditing and vulnerability hunting. Security teams are encouraged to explore the open-source framework and adapt it to their workflows.

For more details, visit the GitHub Security Lab Taskflow Agent repository.