🛡️ CloakLLM

Cloak your prompts. Prove your compliance.

Every prompt you send to an LLM provider is visible in plaintext — names, emails, SSNs, API keys, medical records. CloakLLM intercepts, cloaks, and audits every call.

┌──────────────┐    ┌─────────────────────┐     ┌──────────────┐
│   Your App   │───▶│     CLOAKLLM        │───▶│  Claude/GPT  │
│              │    │                     │     │  /Gemini     │
│  "Email      │    │  "Email [PERSON_0]  │     │              │
│   john@..."  │    │   [EMAIL_0]..."     │     │  Never sees  │
│              │◀───│                     │◀───│  real data   │
└──────────────┘    └─────────────────────┘     └──────────────┘
                          │
                    ┌─────────────┐
                    │  Hash-Chain  │
                    │  Audit Log   │
                    │  (EU AI Act) │
                    └─────────────┘
> **Also available for JavaScript/TypeScript:** `npm install cloakllm` — zero dependencies, OpenAI SDK integration. See [CloakLLM JS](https://github.com/cloakllm/CloakLLM-JS). | [Project Hub](https://github.com/cloakllm/CloakLLM)

⏰ Why Now?

EU AI Act enforcement begins August 2, 2026. Article 12 requires tamper-evident audit logs that regulators can mathematically verify. Non-compliance: up to 7% of global annual revenue.

Your current logging (logger.info()) won't survive an audit. CloakLLM provides:

• 🔒 PII Detection — Names, emails, SSNs, API keys, IPs, credit cards, IBANs via NER + regex
• 🎭 Context-Preserving Cloaking — John Smith → [PERSON_0] (the LLM still understands the prompt)
• ⛓️ Tamper-Evident Audit Chain — Every event hash-linked. Any tampering breaks the chain.
• ⚡ One-Line Middleware — Drop-in protection for OpenAI SDK and LiteLLM (100+ providers)

🚀 Quick Start

Install

pip install cloakllm                  # standalone usage
pip install cloakllm[litellm]         # with LiteLLM integration
python -m spacy download en_core_web_sm

Option A: With OpenAI SDK (one line)

from cloakllm import enable_openai
from openai import OpenAI

client = OpenAI()
enable_openai(client)  # Done. All calls are now cloaked.

response = client.chat.completions.create(
    model="gpt-4o-mini",
    messages=[{"role": "user", "content": "Email john@acme.com about Project X"}]
)
# Provider never sees "john@acme.com" — only "[EMAIL_0]"
# Response is automatically uncloaked before you see it

Option B: With LiteLLM (one line)

import cloakllm
cloakllm.enable()  # Done. All LiteLLM calls are now cloaked.

import litellm
response = litellm.completion(
    model="anthropic/claude-sonnet-4-20250514",
    messages=[{"role": "user", "content": "Email john@acme.com about Project X"}]
)
# Provider never sees "john@acme.com" — only "[EMAIL_0]"
# Response is automatically uncloaked before you see it

Option C: Standalone

from cloakllm import Shield

shield = Shield()

# Cloak
cloaked, token_map = shield.sanitize(
    "Send report to john@acme.com, SSN 123-45-6789"
)
# cloaked: "Send report to [EMAIL_0], SSN [SSN_0]"

# ... send cloaked prompt to any LLM ...

# Uncloak response
clean = shield.desanitize(llm_response, token_map)

Redaction Mode (irreversible)

from cloakllm import Shield, ShieldConfig

shield = Shield(ShieldConfig(mode="redact"))
redacted, _ = shield.sanitize("Email john@acme.com about Sarah Johnson")
# redacted: "Email [EMAIL_REDACTED] about [PERSON_REDACTED]"
# No token map stored — cannot be reversed

Entity Details (compliance metadata)

from cloakllm import Shield

shield = Shield()
sanitized, token_map = shield.sanitize("Email john@acme.com, SSN 123-45-6789")

# Per-entity metadata (no original text — PII-safe)
token_map.entity_details
# [
#   {"category": "EMAIL", "start": 6, "end": 19, "length": 13, "confidence": 0.95, "source": "regex", "token": "[EMAIL_0]"},
#   {"category": "SSN", "start": 25, "end": 36, "length": 11, "confidence": 0.95, "source": "regex", "token": "[SSN_0]"}
# ]

# Full report for dashboards
token_map.to_report()
# {"entity_count": 2, "categories": {...}, "tokens": [...], "mode": "tokenize", "entity_details": [...]}

Option D: CLI

# Scan text for sensitive data
python -m cloakllm scan "Email john@acme.com, SSN 123-45-6789"

# Verify audit chain integrity
python -m cloakllm verify ./cloakllm_audit/

# View audit statistics
python -m cloakllm stats ./cloakllm_audit/

⛓️ Tamper-Evident Audit Chain

Every cloaking event is recorded in a hash-chained append-only log:

{
  "seq": 42,
  "event_id": "a1b2c3d4-...",
  "timestamp": "2026-02-27T14:30:00+00:00",
  "event_type": "sanitize",
  "model": "claude-sonnet-4-20250514",
  "entity_count": 3,
  "categories": {"PERSON": 1, "EMAIL": 1, "SSN": 1},
  "tokens_used": ["[PERSON_0]", "[EMAIL_0]", "[SSN_0]"],
  "prompt_hash": "sha256:9f86d0...",
  "sanitized_hash": "sha256:a3f2b1...",
  "entity_details": [
    {"category": "PERSON", "start": 0, "end": 10, "length": 10, "confidence": 0.85, "source": "spacy", "token": "[PERSON_0]"},
    {"category": "EMAIL", "start": 12, "end": 25, "length": 13, "confidence": 0.95, "source": "regex", "token": "[EMAIL_0]"},
    {"category": "SSN", "start": 27, "end": 38, "length": 11, "confidence": 0.95, "source": "regex", "token": "[SSN_0]"}
  ],
  "latency_ms": 4.2,
  "prev_hash": "sha256:7c4d2e...",
  "entry_hash": "sha256:b5e8f3..."
}

Chain verification:

python -m cloakllm verify ./cloakllm_audit/
# ✅ Audit chain integrity verified — no tampering detected.

If anyone modifies a single entry, every subsequent hash breaks:

Entry #40 ✅ → #41 ✅ → #42 ❌ TAMPERED → #43 ❌ BROKEN → ...

This is what EU AI Act Article 12 requires.

⚙️ Configuration

from cloakllm import Shield, ShieldConfig

shield = Shield(config=ShieldConfig(
    # Detection
    spacy_model="en_core_web_lg",       # Larger model = better accuracy
    detect_emails=True,
    detect_phones=True,
    detect_api_keys=True,
    custom_patterns=[                    # Your own regex patterns
        ("PROJECT_CODE", r"PRJ-\d{4}-\w+"),
        ("INTERNAL_ID", r"EMP-\d{6}"),
    ],

    # Audit
    log_dir="./compliance_audit",
    log_original_values=False,           # Never log original PII

    # Middleware
    skip_models=["ollama/", "local/"],   # Don't cloak local model calls
))

LLM Detection (opt-in) — uses a local Ollama instance to catch semantic PII (addresses, medical info, etc.):

shield = Shield(config=ShieldConfig(
    llm_detection=True,                  # Enable LLM-based detection
    llm_model="llama3.2",               # Ollama model to use
    llm_ollama_url="http://localhost:11434",  # Ollama endpoint
    llm_timeout=10.0,                   # Timeout in seconds
    llm_confidence=0.85,                # Confidence score for LLM detections
))

Environment variables:

CLOAKLLM_LOG_DIR=./audit
CLOAKLLM_SPACY_MODEL=en_core_web_sm
CLOAKLLM_OTEL_ENABLED=true
CLOAKLLM_LLM_DETECTION=true
CLOAKLLM_LLM_MODEL=llama3.2
CLOAKLLM_OLLAMA_URL=http://localhost:11434

🔍 What Gets Detected

🗺️ Roadmap

• PII detection (NER + regex)
• Deterministic tokenization
• Hash-chain audit logging
• LiteLLM middleware integration
• OpenAI SDK middleware integration
• CLI tool
• Redaction / scrubbing mode
• Field-level PII metadata (entity_details)
• OpenTelemetry span emission (with auto-redaction)
• RFC 3161 trusted timestamping
• Signed audit snapshots
• MCP security gateway (tool validation, permission enforcement)
• Local LLM detection (opt-in, via Ollama)
• Sensitivity-based routing (PII → local model, general → cloud)
• Admin dashboard
• EU AI Act conformity report generator

📜 License

MIT

🤝 Contributing

PRs welcome. Highest-impact areas:

1. Non-English NER — Hebrew, Arabic, Chinese PII detection
2. De-tokenization accuracy — handling LLM paraphrasing
3. OpenTelemetry integration — GenAI semantic conventions
4. MCP security — tool validation middleware

Built for the EU AI Act deadline. Ships before the auditors do.