Web Application AI Security Agent

Project Idea

LLM-powered code vulnerability triage

Why

Today's security testing tools overwhelm developers with non-contextual alerts and false positives, making vulnerability management inefficient. As MITRE notes, developers face thousands of bug reports without clear prioritization guidance. OWASP confirms this limitation - static analysis tools can't understand application context, requiring significant manual effort to validate findings. We're solving this by combining traditional security tools with LLM agents that can understand context, prioritize real threats, and provide actionable security insights via a centralized dashboard.

About 50-80% of SAST findings are false positives. Our tool would reduce false positives by 40-60%, which translates to 15-20 hours per week for a security team of 5 people.

What

A security dashboard powered by AI agents that:

• Prioritizes real vulnerabilities over false positives
• Provides context-aware security analysis
• Maps findings to CVE and OWASP data
• Uses vector databases to enable AI comprehension of your codebase
• Combines SAST and SCA with intelligent triage

When

Throughout software development and QA

Where

Flexible deployment options:

• Local environment via docker-compose
• CI/CD pipeline integration
• hosted service offering

Who

Software developers, Product security teams, open source maintainers

How

Implementation approach:

• Integrates open source security tools (CodeQL, semgrep, OWASP Dependency Check, npm audit, PyPi Safety)
• Vector database integration for codebase analysis
• AI agents process tool outputs for intelligent vulnerability triage

Workflow

graph TD
    Start[Start Analysis] --> Discovery[Discovery Agent]
    
    subgraph "Discovery Phase"
        Discovery --> TechStack[Analyze Tech Stack]
        Discovery --> Dependencies[Analyze Dependencies]
        Discovery --> Structure[Analyze Project Structure]
        Discovery --> TestCov[Analyze Test Coverage]
        
        TechStack --> Context[Project Context]
        Dependencies --> Context
        Structure --> Context
        TestCov --> Context
        
        Context --> ThreatModel[Generate Threat Model]
        Context --> TestPlan[Generate Test Plan]
    end
    
    subgraph "Vulnerability Analysis Phase"
        TestPlan --> ToolSelect[Select Security Tools]
        ToolSelect --> ParallelAnalysis[Run Parallel Analysis]
        
        ParallelAnalysis --> CodeQL[CodeQL Analysis]
        ParallelAnalysis --> Semgrep[Semgrep Analysis]
        ParallelAnalysis --> DepCheck[Dependency Check]
        ParallelAnalysis --> CustomTools[Other Tools...]
        
        CodeQL --> Findings[Raw Findings]
        Semgrep --> Findings
        DepCheck --> Findings
        CustomTools --> Findings
    end
    
    subgraph "Triage Phase"
        Findings --> Triage[Triage Agent]
        Context --> Triage
        
        Triage --> DataFlow[Data Flow Analysis]
        Triage --> PatternMatch[Pattern Matching]
        Triage --> Sanitizers[Sanitizer Analysis]
        
        DataFlow --> Scoring[Confidence Scoring]
        PatternMatch --> Scoring
        Sanitizers --> Scoring
        
        Scoring --> FinalReport[Security Report]
    end
    
    FinalReport --> End[End Analysis]

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