Agent Output Security & Hallucination Gate (x402) 🛡️⚡

A deterministic, ultra-low latency (<10ms) security and hallucination inspection micro-oracle for autonomous AI agents.

• Service Name: agent-security-gate-x402
• Settlement Rail: HTTP 402 + x402 Protocol on Base network ($0.002 USDC per request)
• Supported Standards: Google AP2 (/.well-known/ap2), FastMCP, OpenAPI (/docs)
• Compliance & Legal: Zero-Data-Retention Policy (/privacy), Terms of Service & AS-IS Disclaimer (/terms), OFAC Sanctions Screening.
• Deployment: Google Cloud Platform (GCP Cloud Run / Cloud Build)

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🚀 Key Features

1. Prompt Injection & Role Hijacking Guard
   • High-speed heuristic detection of instruction override directives, DAN modes, synthetic system tags, and zero-width character evasion.
2. Secret & Key Leak Scanner
   • Instant scanning for EVM 32-byte private keys, OpenAI / Anthropic API keys, GitHub PATs, AWS access keys, and asymmetric private key blocks.
3. AST Dangerous Code Execution Guard
   • Python AST analyzer blocking prohibited module imports (os, subprocess, sys, socket, shutil, pty, ctypes) and dangerous builtins (eval, exec, __import__).
4. Numerical & Entity Hallucination Validator (Lightweight NLI)
   • Cross-checks numerical claims and named entities in agent outputs against ground truth contexts, pinpointing fabricated numbers and ungrounded entities without heavy external LLM latency.
5. OFAC & Mixer Sanctions Screening
   • Automatically blocks requests from OFAC-sanctioned mixer contracts (e.g. Tornado Cash) and malicious addresses (403 Forbidden).
6. Cryptographic Proof-of-Safety Attestation (EIP-191)
   • Generates tamper-proof audit certificates signed by the gate issuer. Downstream orchestrators and smart contracts can verify proof of inspection before releasing task bounties or executing transactions.
7. Autonomous Agent Self-Discovery (llms.txt & Google AP2)
   • Exposes machine-readable discovery interfaces (llms.txt, /.well-known/ap2.json, mcp_tool_spec.json) allowing autonomous AI crawlers to discover, bind tools, and settle autonomously without human sign-up.
8. Zero-Retention & Legal Disclaimers (/terms, /privacy)
   • Formal in-memory processing policy (no customer data storage) and limitation of liability ($0.002 fee cap).
9. One-Click Python SDK & @gate_inspect Decorator
   • Seamless integration with built-in verify_attestation() for LangChain, CrewAI, AutoGen, or custom agent pipelines.

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📁 Project Structure

agent-security-gate-x402/
├── app/
│   ├── __init__.py
│   ├── main.py              # FastAPI server, /terms, /privacy, & x402 payment enforcement
│   ├── security_engine.py   # Injection, key leak, AST & NLI verification logic
│   ├── x402_verifier.py     # x402 facilitator signature & OFAC verification
│   └── schemas.py           # Pydantic request/response schemas
├── sdk/
│   ├── __init__.py
│   └── agent_gate_sdk.py    # Python SDK client & @gate_inspect decorator
├── tests/
│   ├── __init__.py
│   └── test_client.py       # End-to-end payment, security, & SDK test suite
├── .well-known/
│   └── ap2.json             # Google AP2 manifest
├── mcp_tool_spec.json       # MCP tool definition for Claude/Cursor/LLMs
├── cloudbuild.yaml          # GCP Cloud Build automated pipeline
├── deploy-gcp.sh            # GCP Cloud Run deployment script (Bash)
├── deploy-gcp.ps1           # GCP Cloud Run deployment script (PowerShell)
├── Dockerfile               # Ultra-lightweight container
├── requirements.txt         # Dependencies
├── .env.example             # Environment template
└── README.md

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🐍 Python SDK & Decorator Usage

Install the client SDK in your agent project and wrap your LLM calls:

from sdk.agent_gate_sdk import SecurityGateClient, gate_inspect

client = SecurityGateClient(
    gate_url="https://agent-security-gate-x402-7qxtp3324q-du.a.run.app",
    private_key="0xYourAgentEVMKey..."
)

# 1. Direct Inspection
result = client.inspect(
    agent_output="The total quarterly net revenue was $1.2M.",
    context_ground_truth="Quarterly revenue: $1.2M."
)
print(result["audit"]["verdict"])  # "PASSED"

# 2. Function Decorator Middleware
@gate_inspect(client=client, strict=True)
def run_agent_reasoning(task_prompt: str) -> str:
    # Your LLM call (OpenAI, Anthropic, LangChain, etc.)
    return llm.invoke(task_prompt)

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☁️ Google Cloud Platform (GCP Cloud Run) Deployment

Prerequisites

1. Install Google Cloud SDK (gcloud).
2. Authenticate: gcloud auth login and gcloud config set project <YOUR_GCP_PROJECT_ID>.

One-Click Deployment

Linux / macOS

chmod +x deploy-gcp.sh
./deploy-gcp.sh

Windows (PowerShell)

.\deploy-gcp.ps1

Once deployed, your Cloud Run service URL will be printed (e.g. https://agent-security-gate-x402-xxx.a.run.app).

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🛠️ Local Development & Testing

# 1. Start local server
uvicorn app.main:app --host 0.0.0.0 --port 8080 --reload

# 2. Run test suite
python tests/test_client.py
# or
pytest tests/test_client.py -v