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Validate and audit LLM-generated code securely in Docker sandboxes.
Copy the install command and let the AI configure it · recommended for beginners
No copy-paste install info for "aletheia-mcp" yet — see the docs or source repo.
Run and validate this LLM-generated Python script in a Docker sandbox. Check for dangerous system calls, unauthorized file access, risky network requests, and runtime errors, then provide an audit verdict and remediation suggestions: <paste code>
Returns sandbox execution results, a list of security risks, error logs, and fix recommendations.
Perform multi-agent runtime validation on this AI-generated web service code: build the container, start the service, run basic endpoint tests, monitor abnormal behavior, and summarize whether it is ready for a staging environment: <paste project code or repository contents>
Outputs build and startup status, endpoint test results, suspicious behavior records, and deployment readiness advice.
Act as a real-time supervisory auditor for code changes produced by multiple AI agents. Identify high-risk modifications, conflicting decisions, and unvalidated commits, then generate an audit report: <paste task records, code diffs, or logs>
Generates an audit report with risk levels, issue locations, evidence summaries, and recommended actions.
Run agent-native end-to-end tests securely through MCP with audited policy gates.
Detect prompt injections and jailbreaks to secure LLM applications and workflows.
Scans AI-generated code for vulnerabilities, secrets, and secure coding issues.
Run Python code securely with session state and file access in containers.
Evaluate code in a sandbox with automated execution and LLM-based quality scoring.
Validate AI-generated code with browser tests, evidence capture, and smart diagnostics.