ai-pentesting

Phase 1: RECONNAISSANCE
├── Subdomain enumeration (subfinder)
├── Technology fingerprinting (whatweb, wappalyzer)
├── Port scanning (nmap)
├── API schema discovery (crawling, OpenAPI/GraphQL introspection)
└── Source code analysis (if white-box)
    AI decides: which tools to run, in what order, based on findings

Phase 2: VULNERABILITY ANALYSIS
├── Known CVE scanning (nuclei)
├── Web vulnerability scanning (OWASP ZAP, nikto)
├── API fuzzing (schemathesis)
├── Code-level vulnerability hunting (semgrep, CodeQL)
└── Data flow analysis (input → dangerous function)
    AI decides: which findings are likely exploitable

Phase 3: EXPLOITATION
├── SQL injection (sqlmap, manual payloads)
├── XSS (reflected, stored, DOM)
├── SSRF (internal access, cloud metadata)
├── Authentication bypass (broken auth, privilege escalation)
├── Business logic flaws (price manipulation, race conditions)
└── Browser-based exploitation (Playwright/Puppeteer)
    AI decides: exploitation order, payload selection, chaining

Phase 4: REPORTING
├── Proof-of-concept for each finding
├── Reproducible steps (curl commands, screenshots)
├── Severity rating (CVSS score)
├── Remediation guidance
└── Executive summary
    AI generates: structured, evidence-based report

# Clone and set up Shannon
git clone https://github.com/KeygraphHQ/shannon.git
cd shannon

# Configure credentials
export ANTHROPIC_API_KEY="your-api-key"
export CLAUDE_CODE_MAX_OUTPUT_TOKENS=64000

# Run a pentest against your application
# Requires: Docker, target URL, source code repo
./shannon start URL=https://your-app.com REPO=./your-repo

# Monitor progress
./shannon logs

# View results in Temporal UI
open http://localhost:8233

# ai_pentester.py
# Custom AI pentesting pipeline using LLM + security tools

import subprocess
import json
from openai import OpenAI

client = OpenAI()

class AIPentester:
    """Autonomous AI penetration tester.
    
    Orchestrates security tools using LLM reasoning
    to find and prove vulnerabilities.
    """
    
    def __init__(self, target_url: str, scope: list[str] = None):
        self.target = target_url
        self.scope = scope or [target_url]
        self.findings = []
        self.recon_data = {}
    
    async def run_pentest(self) -> dict:
        """Execute full penetration test lifecycle.
        
        Returns:
            Dict with findings, evidence, and recommendations
        """
        # Phase 1: Recon
        self.recon_data = await self._recon()
        
        # Phase 2: AI-guided vulnerability analysis
        targets = await self._analyze_attack_surface(self.recon_data)
        
        # Phase 3: AI-guided exploitation
        for target in targets:
            finding = await self._exploit(target)
            if finding:
                self.findings.append(finding)
        
        # Phase 4: Generate report
        report = await self._generate_report()
        return report
    
    async def _recon(self) -> dict:
        """Run reconnaissance tools and aggregate results."""
        recon = {}
        
        # Subdomain enumeration
        result = subprocess.run(
            ['subfinder', '-d', self._get_domain(), '-silent'],
            capture_output=True, text=True, timeout=120
        )
        recon['subdomains'] = result.stdout.strip().split('\n')
        
        # Technology fingerprinting
        result = subprocess.run(
            ['whatweb', self.target, '--log-json=/dev/stdout', '-a', '3'],
            capture_output=True, text=True, timeout=60
        )
        recon['technologies'] = json.loads(result.stdout) if result.stdout else {}
        
        # Port scanning
        result = subprocess.run(
            ['nmap', '-sV', '--top-ports', '1000', '-oJ', '-', self._get_domain()],
            capture_output=True, text=True, timeout=300
        )
        recon['ports'] = result.stdout
        
        # Nuclei scan for known CVEs
        result = subprocess.run(
            ['nuclei', '-u', self.target, '-severity', 'critical,high',
             '-json', '-silent'],
            capture_output=True, text=True, timeout=300
        )
        recon['known_vulns'] = [
            json.loads(line) for line in result.stdout.strip().split('\n')
            if line.strip()
        ]
        
        return recon
    
    async def _analyze_attack_surface(self, recon: dict) -> list:
        """Use AI to analyze recon data and prioritize attack targets."""
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content":
                 "You are an expert penetration tester. Analyze the "
                 "reconnaissance data and identify the most promising "
                 "attack vectors. Return JSON array of targets."},
                {"role": "user", "content":
                 f"Recon data:\n{json.dumps(recon, indent=2)}\n\n"
                 "Identify attack targets with: endpoint, vulnerability_type, "
                 "technique, priority (1-5), reasoning."}
            ],
            response_format={"type": "json_object"}
        )
        return json.loads(response.choices[0].message.content).get("targets", [])

    async def _exploit(self, target: dict) -> dict | None:
        """Attempt to exploit an identified vulnerability."""
        vuln_type = target.get('vulnerability_type', '').lower()
        handlers = {
            'injection': self._test_injection,
            'xss': self._test_xss,
            'ssrf': self._test_ssrf,
            'auth': self._test_auth_bypass,
        }
        for key, handler in handlers.items():
            if key in vuln_type:
                return await handler(target)
        return None

    async def _generate_report(self) -> dict:
        """Generate a structured penetration test report."""
        response = client.chat.completions.create(
            model="gpt-4o",
            messages=[
                {"role": "system", "content":
                 "Generate a professional penetration test report with "
                 "executive summary, findings with CVSS scores, PoC steps, "
                 "and remediation recommendations."},
                {"role": "user", "content":
                 f"Target: {self.target}\n"
                 f"Findings: {json.dumps(self.findings, indent=2)}\n"
                 f"Recon data: {json.dumps(self.recon_data, indent=2)}"}
            ]
        )
        return {
            "target": self.target,
            "findings_count": len(self.findings),
            "findings": self.findings,
            "report": response.choices[0].message.content
        }

# .github/workflows/pentest.yml
name: AI Penetration Test
on:
  push:
    branches: [main]
  schedule:
    - cron: '0 2 * * 1'  # Weekly Monday 2 AM

jobs:
  pentest:
    runs-on: ubuntu-latest
    services:
      app:
        image: your-app:${{ github.sha }}
        ports:
          - 8080:8080
    
    steps:
      - uses: actions/checkout@v4
      
      - name: Run Shannon Pentest
        env:
          ANTHROPIC_API_KEY: ${{ secrets.ANTHROPIC_API_KEY }}
        run: |
          git clone https://github.com/KeygraphHQ/shannon.git
          cd shannon
          ./shannon start \
            URL=http://localhost:8080 \
            REPO=../ \
            MAX_CONCURRENT=3
          
          # Wait for completion and extract report
          ./shannon wait
          cp workspace/report.md $GITHUB_WORKSPACE/pentest-report.md
      
      - name: Upload Report
        uses: actions/upload-artifact@v4
        with:
          name: pentest-report
          path: pentest-report.md
      
      - name: Fail on Critical Findings
        run: |
          if grep -q "CRITICAL" pentest-report.md; then
            echo "::error::Critical vulnerabilities found!"
            exit 1
          fi

Set up Shannon to run a full penetration test on our staging environment at https://staging.ourapp.com. The source code is in the current repository. Configure it to test for: SQL injection, XSS, SSRF, and broken authentication. Run with maximum concurrency and generate a report with reproducible proof-of-concept exploits for every finding. Flag any critical vulnerabilities that need immediate attention.

Build a custom AI pentesting pipeline that combines subfinder (subdomain discovery), whatweb (tech fingerprinting), nuclei (CVE scanning), and schemathesis (API fuzzing) orchestrated by an LLM agent. The LLM should analyze results from each tool, decide what to test next, and generate exploitation payloads. Target: our API at api.example.com with the OpenAPI spec at /docs/openapi.json. Produce a structured findings report.

Add automated penetration testing to our GitHub Actions pipeline. It should run on every push to main and weekly on a schedule. The app runs in Docker (docker-compose up), exposed at localhost:8080. Use Shannon for the pentest, upload the report as an artifact, and fail the build if any critical or high severity vulnerabilities are found. Include Slack notification for findings.