verify_policy: Z3 formal verification in one call
simulate_policy: test any JSON input, get ALLOWED/BLOCKED
explain_policy: human-readable breakdown of any policy
scaffold_policy: describe what you want in English, get a CSL template
This means you can do this from Claude Desktop or Cursor:
"Write me a policy that blocks transfers over $5000 for non-admin users"
→ scaffold generates a CSL template
→ verify proves it has no contradictions
→ simulate tests your edge cases
→ all without leaving your editor
The full loop: From English description to mathematically verified, runtime-enforced policy; happens inside your AI assistant.
Why not just prompt the LLM to enforce rules? We benchmarked GPT-4o, Claude Sonnet 4, and Gemini 2.0 Flash as guardrails with a hardened system prompt. Every model was bypassed by at least one attack (context spoofing, multi-turn role escalation, unicode homoglyphs). CSL-Core blocked all of them; because the LLM never touches the enforcement layer.
Setup:
pip install "csl-core[mcp]"
Claude Desktop config:
{
"mcpServers": {
"csl-core": {
"command": "csl-core-mcp"
}
}
}
Or with Docker:
docker build -t csl-core-mcp .
docker run -i csl-core-mcp
GitHub: https://github.com/Chimera-Protocol/csl-core
Listed on awesome-mcp-servers:
https://github.com/punkpeye/awesome-mcp-servers
Previous Show HN discussion: https://news.ycombinator.com/item?id=46963250
Would love feedback, especially from anyone building agentic systems where safety guarantees matter.