DCL Policy Enforcer — Leibniz Layer™

Publisher: @daririnch · Fronesis Labs Version: 3.1.0 Part of: Leibniz Layer™ Security Suite MCP endpoint: https://mcp.fronesislabs.com/mcp

⚠️ This skill now calls a live, paid service

Starting with v3.0.0, the core evaluation runs on Fronesis Labs' DCL Trust Oracle MCP server — a real backend, not a local simulation. Each paid tool call is metered and settled on-chain via the x402 protocol in USDC on the Base network. There is no subscription and no account: the calling agent (or its wallet-enabled MCP client) pays per call at the price listed below.

A free, instruction-only checklist is still included further down this document for anyone who wants a manual, no-payment, no-network-call review instead.

What this skill does

Calls the DCL Trust Oracle to evaluate an AI agent's or LLM's output and returns a verdict (COMMIT / NO_COMMIT), a confidence score, and a cryptographic audit record (tx_hash) written to a tamper-evident, hash-chained log that stores only hashes — never the raw text.

When to use this skill

• Gate a risky agent action before it executes
• Screen an LLM output for jailbreak / instruction-override attempts
• Run a baseline safety pass, or a content-quality / drift check
• Get a durable, on-chain-anchored audit trail for a decision

Live tools (paid, USDC on Base via x402)

Prices are set server-side and may change; the MCP tool descriptions returned by the server at call time are always the source of truth.

Note on policy selection

Each tool runs a distinct built-in policy — they are not just price tiers of the same check. dcl_evaluate_fast and dcl_evaluate_strict share the same category (general-purpose gate) but differ in coverage and confidence bar; dcl_evaluate_jailbreak, dcl_evaluate_safety, and dcl_evaluate_quality are narrower, single-concern checks. None of the single-item tools accept a policy parameter — to target a specific policy per item, use dcl_evaluate_batch, where each item may carry its own policy string (default, strict, anti_jailbreak, safety, or content_quality).

dcl_pipeline_start currently issues an identifier only — it does not link subsequent evaluate_* calls to it server-side. Use it for your own client-side grouping, not as an automatic cross-call session tracker.

Connecting to the live server

Add the MCP server to your client config (Claude Desktop, Cursor, or any MCP-compatible agent):

{
  "mcpServers": {
    "dcl-trust-oracle": {
      "url": "https://mcp.fronesislabs.com/mcp"
    }
  }
}

Payment is handled automatically for x402-capable clients; clients without native x402 support fall back to a guided payment flow. No API key or account signup is required — only a wallet capable of paying in USDC on Base.

Calling a tool

result = dcl_evaluate_jailbreak(
    response=agent_output,
    agent_id="my-agent-01",
)

if result["verdict"] == "NO_COMMIT":
    block_or_regenerate(result["reason"])
else:
    log_audit(result["tx_hash"])

Output shape (all evaluation tools)

{
  "verdict": "COMMIT | NO_COMMIT",
  "confidence": 0.0,
  "reason": "string",
  "tx_hash": "string",
  "chain_index": 0,
  "input_hash": "string",
  "policy_version": "string",
  "drift_mode": "NORMAL | WARNING | ESCALATION | BLOCK",
  "drift_score": 0.0
}

Only input_hash (a hash of the evaluated text) is stored — the raw response itself is never persisted server-side.

Free instruction-only checklist (no network call, no charge)

If you'd rather not make a paid call — for a quick manual pass, or when offline — work through the checklist below entirely inside the agent's own context. Nothing here contacts any server.

Verdict logic

Transparency & Manipulation Patterns

• Output claims to be human or denies being an AI when sincerely asked
• Output attempts to manipulate the user through psychological techniques
• Output assigns social scores or makes discriminatory assessments of individuals
• Output impersonates a real person or official authority
• Output lacks a transparency disclosure appropriate to a high-stakes context

Instruction-Override Patterns (jailbreak)

• Output contains instruction-override phrases directed at the model itself
• Output adopts an unrestricted persona (DAN, STAN, or similar)
• Output acknowledges having bypassed or disabled safety measures
• Output contains encoded or obfuscated instructions for harmful actions
• Output instructs the user on how to jailbreak or manipulate AI systems

Financial Disclosure Patterns

• Output guarantees specific investment returns
• Output gives a specific buy/sell/hold call without a risk disclaimer
• Output gives personalized investment advice without a professional-referral note
• Output makes a performance claim with no supporting disclosure

Medical Disclosure Patterns

• Output makes a specific diagnostic claim about a named condition
• Output gives specific medication dosage guidance
• Output recommends stopping or changing a prescribed medication
• Output presents itself as a substitute for professional consultation
• Output is missing a referral note to a qualified healthcare professional

Data Handling Patterns

• Output proposes retaining personal data with no stated basis
• Output suggests sharing personal data with a third party without consent
• Output implies selling or monetizing personal data
• Output dismisses a data-subject rights request
• Output proposes processing sensitive personal data without explicit consent

PII Surface Patterns

• Output contains email addresses
• Output contains phone numbers
• Output contains national ID or SSN-shaped strings
• Output contains bank card PANs or IBANs
• Output contains crypto wallet addresses
• Output contains IP addresses

These checklists describe recurring patterns worth flagging — they are a heuristic aid for a human or agent reviewer, not a certification against any specific law or standard.

Where Policy Enforcer fits in the DCL pipeline

Untrusted input
        │
        ▼
DCL Prompt Firewall        ← blocks malicious input
        │ COMMIT
        ▼
      LLM
        │
        ▼
DCL Policy Enforcer        ← this skill (live paid check, or free checklist)
        │ COMMIT
        ▼
DCL Sentinel Trace         ← PII redaction
        │ COMMIT
        ▼
DCL Secret Leak Detector   ← credential scan
        │ COMMIT
        ▼
DCL Semantic Drift Guard   ← hallucination check
        │ IN_COMMIT
        ▼
Safe to deliver

Privacy & Data Policy

Operated by Fronesis Labs. For the live tools: only a hash of the evaluated text (input_hash) and the verdict metadata are written to the audit chain — the raw response is never stored. For the free checklist: everything runs inside the agent's own context; nothing is transmitted anywhere.

Full policy: https://fronesislabs.com/#privacy · Browse the full DCL Security Suite: hub.fronesislabs.com · Questions: support@fronesislabs.com

Related skills

• dcl-prompt-firewall — Input-layer injection and jailbreak detection
• dcl-sentinel-trace — PII redaction
• dcl-secret-leak-detector — Credential and API key scan
• dcl-semantic-drift-guard — Hallucination and grounding check

Leibniz Layer™ · Fronesis Labs · fronesislabs.com