# CSL-Core
[](https://pypi.org/project/csl-core/)
[](https://pepy.tech/projects/csl-core)
[](https://pypi.org/project/csl-core/)
[](LICENSE)
[](https://github.com/Z3Prover/z3)
**CSL-Core** (Chimera Specification Language) is a deterministic safety layer for AI agents. Write rules in `.csl` files, verify them mathematically with Z3, enforce them at runtime — outside the model. The LLM never sees the rules. It simply cannot violate them.
```bash
pip install csl-core
```
Originally built for [**Project Chimera**](https://github.com/Chimera-Protocol/Project-Chimera), now open-source for any AI system.
---
## Why?
```python
prompt = """You are a helpful assistant. IMPORTANT RULES:
- Never transfer more than $1000 for junior users
- Never send PII to external emails
- Never query the secrets table"""
```
This doesn't work. LLMs can be prompt-injected, rules are probabilistic (99% ≠ 100%), and there's no audit trail when something goes wrong.
**CSL-Core flips this**: rules live outside the model in compiled, Z3-verified policy files. Enforcement is deterministic — not a suggestion.
---
## Quick Start (60 Seconds)
### 1. Write a Policy
Create `my_policy.csl`:
```js
CONFIG {
ENFORCEMENT_MODE: BLOCK
CHECK_LOGICAL_CONSISTENCY: TRUE
}
DOMAIN MyGuard {
VARIABLES {
action: {"READ", "WRITE", "DELETE"}
user_level: 0..5
}
STATE_CONSTRAINT strict_delete {
WHEN action == "DELETE"
THEN user_level >= 4
}
}
```
### 2. Verify & Test (CLI)
```bash
# Compile + Z3 formal verification
cslcore verify my_policy.csl
# Test a scenario
cslcore simulate my_policy.csl --input '{"action": "DELETE", "user_level": 2}'
# → BLOCKED: Constraint 'strict_delete' violated.
# Interactive REPL
cslcore repl my_policy.csl
```
### 3. Use in Python
```python
from chimera_core import load_guard
guard = load_guard("my_policy.csl")
result = guard.verify({"action": "READ", "user_level": 1})
print(result.allowed) # True
result = guard.verify({"action": "DELETE", "user_level": 2})
print(result.allowed) # False
```
---
## Benchmark: Adversarial Attack Resistance
We tested prompt-based safety rules vs CSL-Core enforcement across 4 frontier LLMs with 22 adversarial attacks and 15 legitimate operations:
| Approach | Attacks Blocked | Bypass Rate | Legit Ops Passed | Latency |
|----------|----------------|-------------|------------------|---------|
| GPT-4.1 (prompt rules) | 10/22 (45%) | 55% | 15/15 (100%) | ~850ms |
| GPT-4o (prompt rules) | 15/22 (68%) | 32% | 15/15 (100%) | ~620ms |
| Claude Sonnet 4 (prompt rules) | 19/22 (86%) | 14% | 15/15 (100%) | ~480ms |
| Gemini 2.0 Flash (prompt rules) | 11/22 (50%) | 50% | 15/15 (100%) | ~410ms |
| **CSL-Core (deterministic)** | **22/22 (100%)** | **0%** | **15/15 (100%)** | **~0.84ms** |
**Why 100%?** Enforcement happens outside the model. Prompt injection is irrelevant because there's nothing to inject against. Attack categories: direct instruction override, role-play jailbreaks, encoding tricks, multi-turn escalation, tool-name spoofing, and more.
> Full methodology: [`benchmarks/`](benchmarks/)
---
## LangChain Integration
Protect any LangChain agent with 3 lines — no prompt changes, no fine-tuning:
```python
from chimera_core import load_guard
from chimera_core.plugins.langchain import guard_tools
from langchain_classic.agents import AgentExecutor, create_tool_calling_agent
guard = load_guard("agent_policy.csl")
# Wrap tools — enforcement is automatic
safe_tools = guard_tools(
tools=[search_tool, transfer_tool, delete_tool],
guard=guard,
inject={"user_role": "JUNIOR", "environment": "prod"}, # LLM can't override these
tool_field="tool" # Auto-inject tool name
)
agent = create_tool_calling_agent(llm, safe_tools, prompt)
executor = AgentExecutor(agent=agent, tools=safe_tools)
```
Every tool call is intercepted before execution. If the policy says no, the tool doesn't run. Period.
### Context Injection
Pass runtime context that the LLM **cannot override** — user roles, environment, rate limits:
```python
safe_tools = guard_tools(
tools=tools,
guard=guard,
inject={
"user_role": current_user.role, # From your auth system
"environment": os.getenv("ENV"), # prod/dev/staging
"rate_limit_remaining": quota.remaining # Dynamic limits
}
)
```
### LCEL Chain Protection
```python
from chimera_core.plugins.langchain import gate
chain = (
{"query": RunnablePassthrough()}
| gate(guard, inject={"user_role": "USER"}) # Policy checkpoint
| prompt | llm | StrOutputParser()
)
```
---
## CLI Tools
The CLI is a complete development environment for policies — test, debug, and deploy without writing Python.
### `verify` — Compile + Z3 Proof
```bash
cslcore verify my_policy.csl
# ⚙️ Compiling Domain: MyGuard
# • Validating Syntax... ✅ OK
# ├── Verifying Logic Model (Z3 Engine)... ✅ Mathematically Consistent
# • Generating IR... ✅ OK
```
### `simulate` — Test Scenarios
```bash
# Single input
cslcore simulate policy.csl --input '{"action": "DELETE", "user_level": 2}'
# Batch testing from file
cslcore simulate policy.csl --input-file test_cases.json --dashboard
# CI/CD: JSON output
cslcore simulate policy.csl --input-file tests.json --json --quiet
```
### `repl` — Interactive Development
```bash
cslcore repl my_policy.csl --dashboard
cslcore> {"action": "DELETE", "user_level": 2}
🛡️ BLOCKED: Constraint 'strict_delete' violated.
cslcore> {"action": "DELETE", "user_level": 5}
✅ ALLOWED
```
### CI/CD Pipeline
```yaml
# GitHub Actions
- name: Verify policies
run: |
for policy in policies/*.csl; do
cslcore verify "$policy" || exit 1
done
```
---
## MCP Server (Claude Desktop / Cursor / VS Code)
Write, verify, and enforce safety policies directly from your AI assistant — no code required.
```bash
pip install "csl-core[mcp]"
```
Add to Claude Desktop config (`~/Library/Application Support/Claude/claude_desktop_config.json`):
```json
{
"mcpServers": {
"csl-core": {
"command": "uv",
"args": ["run", "--with", "csl-core[mcp]", "csl-core-mcp"]
}
}
}
```
| Tool | What It Does |
|---|---|
| `verify_policy` | Z3 formal verification — catches contradictions at compile time |
| `simulate_policy` | Test policies against JSON inputs — ALLOWED/BLOCKED |
| `explain_policy` | Human-readable summary of any CSL policy |
| `scaffold_policy` | Generate a CSL template from plain-English description |
> **You:** "Write me a safety policy that prevents transfers over $5000 without admin approval"
>
> **Claude:** *scaffold_policy → you edit → verify_policy catches a contradiction → you fix → simulate_policy confirms it works*
---
## Architecture
```
┌──────────────────────────────────────────────────────────┐
│ 1. COMPILER .csl → AST → IR → Compiled Artifact │
│ Syntax validation, semantic checks, functor gen │
├──────────────────────────────────────────────────────────┤
│ 2. VERIFIER Z3 Theorem Prover — Static Analysis │
│ Contradiction detection, reachability, rule shadowing │
│ ⚠️ If verification fails → policy will NOT compile │
├──────────────────────────────────────────────────────────┤
│ 3. RUNTIME Deterministic Policy Enforcement │
│ Fail-closed, zero dependencies, <1ms latency │
└──────────────────────────────────────────────────────────┘
```
Heavy computation happens once at compile-time. Runtime is pure evaluation.
---
## Used in Production
<table>
<tr>
<td width="80" align="center">
<a href="https://github.com/Chimera-Protocol/Project-Chimera">🏛️</a>
</td>
<td>
<a href="https://github.com/Chimera-Protocol/Project-Chimera"><b>Project Chimera</b></a> — Neuro-Symbolic AI Agent<br/>
CSL-Core powers all safety policies across e-commerce and quantitative trading domains. Both are Z3-verified at startup.
</td>
</tr>
</table>
*Using CSL-Core? [Let us know](https://github.com/Chimera-Protocol/csl-core/discussions) and we'll add you here.*
---
## Example Policies
| Example | Domain | Key Features |
|---------|--------|--------------|
| [`agent_tool_guard.csl`](examples/agent_tool_guard.csl) | AI Safety | RBAC, PII protection, tool permissions |
| [`chimera_banking_case_study.csl`](examples/chimera_banking_case_study.csl) | Finance | Risk scoring, VIP tiers, sanctions |
| [`dao_treasury_guard.csl`](examples/dao_treasury_guard.csl) | Web3 | Multi-sig, timelocks, emergency bypass |
```bash
python examples/run_examples.py # Run all with test suites
python examples/run_examples.py banking # Run specific example
```
---
## API Reference
```python
from chimera_core import load_guard, RuntimeConfig
# Load + compile + verify
guard = load_guard("policy.csl")
# With custom config
guard = load_guard("policy.csl", config=RuntimeConfig(
raise_on_block=False, # Return result instead of raising
collect_all_violations=True, # Report all violations, not just first
missing_key_behavior="block" # "block", "warn", or "ignore"
))
# Verify
result = guard.verify({"action": "DELETE", "user_level": 2})
print(result.allowed) # False
print(result.violations) # ['strict_delete']
```
Full docs: [**Getting Started**](docs/getting-started.md) · [**Syntax Spec**](docs/syntax-spec.md) · [**CLI Reference**](docs/cli-reference.md) · [**Philosophy**](docs/philosophy.md)
---
## Roadmap
**✅ Done:** Core language & parser · Z3 verification · Fail-closed runtime · LangChain integration · CLI (verify, simulate, repl) · MCP Server · Production deployment in Chimera v1.7.0
**🚧 In Progress:** Policy versioning · LangGraph integration
**🔮 Planned:** LlamaIndex & AutoGen · Multi-policy composition · Hot-reload · Policy marketplace · Cloud templates
**🔒 Enterprise (Research):** TLA+ temporal logic · Causal inference · Multi-tenancy
---
## Contributing
We welcome contributions! Start with [`good first issue`](https://github.com/Chimera-Protocol/csl-core/issues?q=is%3Aissue+is%3Aopen+label%3A%22good+first+issue%22) or check [`CONTRIBUTING.md`](CONTRIBUTING.md).
**High-impact areas:** Real-world example policies · Framework integrations · Web-based policy editor · Test coverage
---
## License
**Apache 2.0** (open-core model). The complete language, compiler, Z3 verifier, runtime, CLI, MCP server, and all examples are open-source. See [LICENSE](LICENSE).
---
**Built with ❤️ by [Chimera Protocol](https://github.com/Chimera-Protocol)** · [Issues](https://github.com/Chimera-Protocol/csl-core/issues) · [Discussions](https://github.com/Chimera-Protocol/csl-core/discussions) · [Email](mailto:akarlaraytu@gmail.com)
