SecurityAgent is an experimental project that explores the potential of creating an autonomous security analysis system that can understand, analyze, and reason about code security without relying solely on pattern matching or traditional SAST tools.
- Create an intelligent security agent that can understand codebases holistically
- Move beyond pattern matching to true security reasoning
- Provide actionable security insights with context
- Reduce false positives through intelligent analysis
- Generate exploitability proofs for vulnerabilities
The system builds a comprehensive understanding of the codebase through multiple analysis layers:
- Call Graph Analysis: Maps function relationships and data flow paths
- Trust Boundary Detection: Identifies interfaces between trusted and untrusted contexts
- Data Flow Tracking: Follows sensitive data through the application
- Input Validation Analysis: Verifies proper handling of external inputs
The system leverages Large Language Models for intelligent security analysis:
- Vector-Based Code Search: Semantic search through codebases using embeddings
- Contextual Understanding: Deep comprehension of code patterns and security implications
- Interactive Analysis: Multi-step reasoning about potential vulnerabilities
- Automated Mitigation: AI-generated security recommendations
- Code Parsing & Embedding: Convert source code into embeddings for semantic search
- Multi-layer Analysis:
- Traditional static analysis
- LLM-based vulnerability detection
- Context-aware security reasoning
- Interactive Analysis:
- Code pattern search
- Vulnerability assessment
- Mitigation suggestion
- Report Generation: Detailed security insights with context