AI-Powered Security Auditing for Your Codebase
Structured six-phase audit process from reconnaissance to independent verification, ensuring comprehensive security analysis of your codebase.
Intelligent gap detection using coverage critics to identify vulnerabilities that might be missed by traditional scanning methods.
Adversarial validation where the agent that checks a finding is never the agent that found it, ensuring unbiased results.
Machine-readable findings with distinct verdicts: confirmed, needs_validation, and rejected, complete with source traces and bounded results.
Multiple runs are additive, targeting gaps and revalidating changed source while carrying forward current-source evidence.
Comprehensive reports derived from verified records, including detailed findings and validation requirements.
Maps architecture, trust boundaries, input surfaces, prior evidence, and deterministic coverage in structured documentation.
Assigns isolated hunters from ledger units, records checks, and uses coverage critics to identify security gaps.
Gives every unique candidate to a fresh verifier that attempts to disprove it through rigorous testing.
Writes confirmed, needs_validation, and rejected records to findings.json with schema validation.
Fresh agents verify final source claims with material replacements receiving additional verification.
Generates comprehensive reports from verified records and coverage ledger for actionable insights.
Comprehensive auditing for large codebases with complex architectures
Systematic vulnerability discovery with structured methodology
Integrate security audits into CI/CD pipelines for continuous monitoring
Web, mobile, and desktop application vulnerability assessment
Infrastructure, container, and serverless security auditing
Dependency, CI, and release pipeline vulnerability scanning
Agent with tool use and parallel sub-agent capabilities
For zero-dependency findings and coverage validation
Enforced isolation with resource limits and network controls