Introduction to Offensive AI

// claude_desktop_config.json — MCP server configuration
// Location: ~/Library/Application Support/Claude/claude_desktop_config.json (macOS)
//           %APPDATA%\Claude\claude_desktop_config.json (Windows)
{
  "mcpServers": {
    "security-toolkit": {
      // Point this at a real security MCP server you run locally —
      // e.g. HexStrike AI (see /offensive-ai/02-hexstrike/) or secops-mcp
      "command": "python3",
      "args": ["/opt/mcp-servers/security_server.py"],
      "env": {
        "NMAP_PATH": "/usr/bin/nmap",
        "NUCLEI_PATH": "/usr/bin/nuclei"
      }
    },
    "ollama-bridge": {
      "command": "python3",
      "args": ["/opt/mcp-servers/ollama_bridge.py"],
      "env": {
        "OLLAMA_HOST": "http://localhost:11434",
        "MODEL": "llama3.3:70b"
      }
    }
  }
}

# MITRE ATLAS Navigator — list adversarial ML techniques
# Prerequisites: pip install requests pyyaml
# Docs: https://atlas.mitre.org/
import requests, yaml

# Fetch the ATLAS matrix (adversarial ML tactics and techniques)
ATLAS_URL = "https://raw.githubusercontent.com/mitre-atlas/atlas-data/main/dist/ATLAS.yaml"
data = yaml.safe_load(requests.get(ATLAS_URL).text)

# Techniques live under the ATLAS matrix
techniques = data["matrices"][0]["techniques"]
for t in techniques[:10]:
    print(f"  {t['id']}: {t['name']}")

# --- Selected LLM/GenAI techniques (verify current IDs at atlas.mitre.org) ---
# AML.T0051: LLM Prompt Injection
# AML.T0054: LLM Jailbreak
# AML.T0043: Craft Adversarial Data
# AML.T0020: Poison Training Data
# AML.T0015: Evade AI Model
# AML.T0024: Exfiltration via AI Inference API
# AML.T0057: LLM Data Leakage
# AML.T0044: Full AI Model Access

# Prerequisites: curl, Docker (optional)
# Install Ollama (macOS/Linux)
curl -fsSL https://ollama.com/install.sh | sh

# Pull a security-focused model
ollama pull llama3.3:70b
ollama pull deepseek-r1:32b

# Verify the model is running
ollama list
# NAME                  ID            SIZE    MODIFIED
# llama3.3:70b          a]6b4c8df...  39 GB   2 minutes ago
# deepseek-r1:32b       f1e2d3c4...   18 GB   5 minutes ago

# Run an interactive security query
ollama run llama3.3:70b "Explain the OWASP Top 10 2025 changes"

# Start the Ollama API server (default: http://localhost:11434)
ollama serve

# Test API connectivity
curl http://localhost:11434/api/tags | jq '.models[].name'

# Prerequisites: pip install langchain openai mcp
# Example: minimal MCP-aware agent using LangChain + MCP
import asyncio
from mcp import ClientSession, StdioServerParameters
from mcp.client.stdio import stdio_client

async def run_security_agent():
    """Connect to an MCP security server and invoke a scan tool."""
    server_params = StdioServerParameters(
        command="python3",
        args=["/opt/mcp-servers/security_server.py"]
    )

    async with stdio_client(server_params) as (read, write):
        async with ClientSession(read, write) as session:
            await session.initialize()

            # List available security tools
            tools = await session.list_tools()
            for tool in tools.tools:
                print(f"  Tool: {tool.name} — {tool.description}")

            # Call a reconnaissance tool
            result = await session.call_tool(
                "nmap_scan",
                arguments={"target": "192.168.1.0/24", "flags": "-sV --top-ports 100"}
            )
            print(result.content[0].text)

asyncio.run(run_security_agent())