CSL-Core

❤️ Our Contributors!

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.

pip install csl-core

Originally built for Project Chimera, now open-source for any AI system.

---

Why?

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:

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)

# 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

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/

---

LangChain Integration

Protect any LangChain agent with 3 lines — no prompt changes, no fine-tuning:

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:

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

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

cslcore verify my_policy.csl

# ⚙️  Compiling Domain: MyGuard
#    • Validating Syntax... ✅ OK
#    ├── Verifying Logic Model (Z3 Engine)... ✅ Mathematically Consistent
#    • Generating IR... ✅ OK

simulate — Test Scenarios

# 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

cslcore repl my_policy.csl --dashboard

cslcore> {"action": "DELETE", "user_level": 2}
🛡️ BLOCKED: Constraint 'strict_delete' violated.

cslcore> {"action": "DELETE", "user_level": 5}
✅ ALLOWED

formal — TLA⁺ Model Checking

cslcore formal my_policy.csl

Runs the official TLC model checker (java -jar tla2tools.jar) against your policy. TLC exhaustively explores every reachable state in the abstract state space and proves each temporal property holds — or returns a concrete counterexample trace with the exact state that breaks your invariant.

╔══════════════════════════════════════════════════════════════════════════════╗
║                       TLA⁺ FORMAL VERIFICATION ENGINE                        ║
║          Chimera Specification Language · Temporal Logic of Actions          ║
║                                                                              ║
║    ⚡  REAL TLC  ·  java -jar tla2tools.jar  ·  Exhaustive Model Checking    ║
║       TLC2 Version 2026.03.31.154134 (rev: becec35)  ·  pid 48146  ·  1      ║
║                                  worker(s)                                   ║
╚══════════════════════════════════════════════════════════════════════════════╝

  Variable      Domain                         Cardinality
 ━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━
  agent_tier    {"STANDARD", "PREMIUM"}                |2|
  task_type     {"READ", "WRITE", "ANALYZE"}           |3|
  risk_score    0..5                                   |6|

  ├─ □(no_destructive_ops)      ✅  HOLDS  [288 states  349ms]
  ├─ □(no_production_access)    ✅  HOLDS  [288 states  349ms]
  ├─ □(bounded_risk)            ✅  HOLDS  [288 states  349ms]

  └─ Proof hash: 17dd1564897d242fc045a3a884a52bbb… ✅

╔══════════════ TLA⁺ VERIFICATION COMPLETE — ALL PROPERTIES HOLD ══════════════╗
║  ✅  Domain: AIAgentSafetyDemo  ·  ⬡ 144 states  ·  ⏱ 1047ms               ║
╚══════════════════════════════════════════════════════════════════════════════╝

Enable in any policy by adding one line to CONFIG:

CONFIG {
  ENFORCEMENT_MODE: BLOCK
  ENABLE_FORMAL_VERIFICATION: TRUE   // ← triggers cslcore formal automatically
}

Or run standalone:

cslcore formal policy.csl              # real TLC (Java required, JAR auto-downloaded)
cslcore formal policy.csl --mock       # Python BFS fallback (no Java needed)
cslcore formal policy.csl --timeout 120
cslcore formal policy.csl --export-tla ./specs/   # save .tla + .cfg for TLA+ Toolbox

No Java? CSL-Core falls back to a Python BFS model checker automatically. The banner clearly labels which engine ran. JAR is auto-downloaded on first use (~4MB from the official TLA+ GitHub release).

CI/CD Pipeline

# 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.

pip install "csl-core[mcp]"

Add to Claude Desktop config (~/Library/Application Support/Claude/claude_desktop_config.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. Z3 VERIFIER    Theorem Prover — Static Analysis       │
│     Contradiction detection, reachability, rule shadowing │
│     ⚠️ If verification fails → policy will NOT compile    │
├──────────────────────────────────────────────────────────┤
│  3. TLA⁺ VERIFIER  Model Checker — Temporal Safety        │
│     Exhaustive state-space exploration via TLC            │
│     Predicate abstraction for large numeric domains       │
│     Counterexample traces + automated fix suggestions     │
│     (opt-in: ENABLE_FORMAL_VERIFICATION: TRUE)            │
├──────────────────────────────────────────────────────────┤
│  4. RUNTIME     Deterministic Policy Enforcement          │
│     Fail-closed, zero dependencies, <1ms latency          │
└──────────────────────────────────────────────────────────┘

Heavy computation happens once at compile-time. Runtime is pure evaluation.

---

Used in Production

🏛️	Project Chimera — Neuro-Symbolic AI Agent CSL-Core powers all safety policies across e-commerce and quantitative trading domains. Both are Z3-verified at startup.

Using CSL-Core? Let us know and we'll add you here.

---

Example Policies

Example	Domain	Key Features
agent_tool_guard.csl	AI Safety	RBAC, PII protection, tool permissions
chimera_banking_case_study.csl	Finance	Risk scoring, VIP tiers, sanctions
dao_treasury_guard.csl	Web3	Multi-sig, timelocks, emergency bypass
tla_demo.csl	Formal Methods	TLA⁺ model checking — all properties hold
tla_demo_violation.csl	Formal Methods	TLA⁺ counterexample trace + fix suggestions

python examples/run_examples.py          # Run all with test suites
python examples/run_examples.py banking  # Run specific example

---

API Reference

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 · Syntax Spec · CLI Reference · Philosophy

---

Roadmap

✅ Done: Core language & parser · Z3 verification · Fail-closed runtime · LangChain integration · CLI (verify, simulate, repl, formal) · MCP Server · TLA⁺ model checking with real TLC · Predicate abstraction · Counterexample analysis · 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): Causal inference · Multi-tenancy

---

Contributing

We welcome contributions! Start with good first issue or check 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.

---

Built with ❤️ by Chimera Protocol · Issues · Discussions · Email