Stop AI agents from rm -rf'ing your production servers
AI agents are rewriting infrastructure commands with hallucinated flags and dangerous operations. CLI Agent Safety Validator intercepts every LLM-generated shell command, validates it against your safety policies using pattern matching and sandbox simulation, and blocks destructive operations before they touch your systems. Built on FastAPI with Redis-backed policy caching, it integrates with any agent framework via REST API and logs every blocked command to PostgreSQL for compliance auditing.
Key Benefits:
- Real-time command interception with <50ms latency using Redis policy cache prevents hallucinated destructive operations (rm -rf, DROP TABLE, kubectl delete) before execution
- Configurable safety policies with blacklist/whitelist rules, regex patterns, and sandbox validation catch dangerous flag combinations and privilege escalations that LLMs frequently hallucinate
- Complete audit trail in PostgreSQL with OpenTelemetry tracing shows exactly which agent attempted what command, when it was blocked, and why—critical for SOC2 and compliance requirements
MVP Scope: Build a command interception service that parses LLM-generated shell commands, validates them against safety policies (blacklist/whitelist), and provides audit logging. Includes REST API for agent integration, basic policy engine with configurable rules, and dashboard for viewing blocked commands and execution logs.
Tech Stack: Python, FastAPI, PostgreSQL, Redis, Docker, OpenTelemetry, React, TypeScript
Components:
- Command Interception Layer
- Safety Policy Engine
- Execution Sandbox
- Audit & Logging Dashboard
- Agent Integration API
Quality assessment: Addresses a genuine pain point in AI agent deployment with a concrete technical solution and clear architecture, but lacks originality (command validation is established practice) and the pitch/MVP description is incomplete, preventing assessment of full market differentiation.
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