NIST TEVV-Athlon AI Agent Security & Verification MCP Server

Securing the Autonomous Frontier with NIST TEVV-Athlon

With the rapid deployment of autonomous agents capable of code execution and infrastructure management, robust security verification is non-negotiable. The NIST TEVV-Athlon (Testing, Evaluation, Verification, and Validation) framework provides a rigorous methodology for AI system auditing. By exposing TEVV-Athlon primitives via a FastMCP Python server, we enable agents in Claude Desktop and Cursor to proactively self-audit, scan for adversarial injections, and validate safety boundaries in real-time.

Building the FastMCP Python Server

We will construct a Python-based FastMCP server that provides three critical security tools: adversarial prompt scanning, authorization boundary verification, and output safety validation. This ensures agents can verify their actions before committing them to production workflows.

1. Project Setup

Initialize a Python environment and install the required dependencies:

python -m venv venv
source venv/bin/activate
pip install fastmcp pydantic nist-tevv-core

2. The Python Implementation

The following Python script defines our FastMCP server, integrating Pydantic for strict input validation and the hypothetical nist_tevv_core library for security evaluations.

import os
from fastmcp import FastMCP
from pydantic import BaseModel, Field
from nist_tevv_core import TEVVScanner, AuthVerifier, SafetyValidator

# Initialize NIST TEVV Components
scanner = TEVVScanner(model_tier="enterprise")
verifier = AuthVerifier(policy_path="/etc/agent-policies/rbac.json")
validator = SafetyValidator(strict_mode=True)

# Initialize FastMCP Server
mcp = FastMCP(
    name="nist-tevv-athlon-security",
    version="1.0.0",
    description="NIST TEVV-Athlon Security & Verification primitives for AI Agents."
)

class AdversarialScanInput(BaseModel):
    prompt_text: str = Field(..., description="The input prompt or context to scan for adversarial injections or jailbreaks.")
    sensitivity_level: float = Field(0.8, description="Sensitivity threshold (0.0 to 1.0) for the scan.")

@mcp.tool(name="scan_adversarial_injection", description="Scan inputs for adversarial prompt injections, jailbreak attempts, or malicious payloads.")
def scan_adversarial_injection(args: AdversarialScanInput) -> str:
    try:
        result = scanner.analyze(args.prompt_text, threshold=args.sensitivity_level)
        if result.is_malicious:
            return f"[SECURITY ALERT] Adversarial injection detected! Confidence: {result.confidence}. Threat Vectors: {result.threat_vectors}"
        return "[SAFE] No adversarial injections detected."
    except Exception as e:
        return f"Error during scan: {str(e)}"

class AuthVerificationInput(BaseModel):
    tool_name: str = Field(..., description="The name of the tool the agent intends to call.")
    agent_role: str = Field(..., description="The assigned role or identity of the agent (e.g., 'readonly_researcher', 'system_admin').")
    target_resource: str = Field(..., description="The resource URI or identifier the tool will affect.")

@mcp.tool(name="verify_tool_authorization", description="Verify if the current agent role has the cryptographic authorization to execute a specific tool against a resource.")
def verify_tool_authorization(args: AuthVerificationInput) -> str:
    try:
        is_authorized = verifier.check_access(
            role=args.agent_role,
            action=args.tool_name,
            resource=args.target_resource
        )
        if is_authorized:
            return f"[AUTHORIZED] Agent role '{args.agent_role}' is permitted to execute '{args.tool_name}' on '{args.target_resource}'."
        return f"[DENIED] Agent role '{args.agent_role}' lacks permissions for '{args.tool_name}' on '{args.target_resource}'. Execution blocked."
    except Exception as e:
        return f"Authorization verification failed: {str(e)}"

class OutputSafetyInput(BaseModel):
    generated_code: str = Field(..., description="The code or script generated by the agent to be validated before execution.")
    language: str = Field(..., description="The programming language of the generated code.")

@mcp.tool(name="validate_output_safety", description="Statically analyze generated code for destructive commands, infinite loops, or unauthorized network calls.")
def validate_output_safety(args: OutputSafetyInput) -> str:
    try:
        safety_report = validator.analyze_code(args.generated_code, lang=args.language)
        if safety_report.has_critical_violations:
            return f"[BLOCKED] Destructive or unsafe patterns found: {safety_report.violations_list}"
        return "[PASSED] Code passed static safety validation."
    except Exception as e:
        return f"Safety validation error: {str(e)}"

if __name__ == "__main__":
    print("Starting NIST TEVV-Athlon Security MCP Server...")
    mcp.run_stdio()

Pydantic Input Schemas

We use Pydantic Models (AdversarialScanInput, AuthVerificationInput, OutputSafetyInput) to define the inputSchema for each tool. FastMCP automatically translates these Pydantic models into the JSON Schema format required by the Model Context Protocol, ensuring agents only provide well-typed, validated arguments.

mcpServers Configuration

To deploy this security server, update your client configurations as follows:

Claude Desktop Configuration

{
  "mcpServers": {
    "nist-tevv-security": {
      "command": "/absolute/path/to/venv/bin/python",
      "args": ["/absolute/path/to/nist-tevv-server/main.py"]
    }
  }
}

Cursor IDE Configuration

Within Cursor, add a FastMCP server pointing to the absolute path of your Python executable (within the virtual environment) and the main.py script. This ensures that any code written or executed by the Cursor agent can be pre-validated by the TEVV tools.

OAuth 2.0 & API Key Security Guide

When running a security verification server, the integrity of the server itself is critical.

• Policy File Protection: The rbac.json policy file used by the AuthVerifier must be read-only and secured via strict file system permissions. Agents must never have write access to their own policy definitions.
• Zero-Trust Auditing: Incorporate OAuth 2.0 service accounts if the TEVV verification offloads complex scanning to a centralized security cluster. Use mutual TLS (mTLS) to authenticate the MCP server against the enterprise security backend.
• Audit Logging: Every tool execution, especially denials (e.g., [DENIED]), should be shipped to an external SIEM for AI News monitoring and incident response.

By Deepak Bagada, CEO at SaaSNext & Principal AI Architect.