AI Pentest Methodology Beginner

A structured methodology ensures comprehensive coverage and repeatable results. This lesson presents a six-phase AI penetration testing methodology that extends traditional pentest frameworks with AI-specific reconnaissance, testing, and reporting techniques.

The Six-Phase AI Pentest Methodology

  1. Phase 1: Scoping and Planning

    Define the engagement scope, rules of engagement, access levels, and success criteria. Identify which AI components are in scope and what testing is permitted.

  2. Phase 2: Reconnaissance

    Gather information about the AI system: model type, framework, API endpoints, input/output formats, training data sources, and deployment architecture.

  3. Phase 3: Threat Enumeration

    Map potential attack vectors using threat models (STRIDE, OWASP ML Top 10) and prioritize based on the specific system under test.

  4. Phase 4: Testing and Exploitation

    Execute attacks against each identified vector: adversarial inputs, model extraction, data leakage probes, API fuzzing, and infrastructure testing.

  5. Phase 5: Analysis and Validation

    Validate findings, assess real-world impact, eliminate false positives, and determine the exploitability of each vulnerability.

  6. Phase 6: Reporting

    Document all findings with evidence, risk ratings, and actionable remediation recommendations. Present results to technical and executive audiences.

AI-Specific Reconnaissance

Beyond standard network and application reconnaissance, AI pentesting requires gathering ML-specific intelligence:

Information Method Value
Model type API probing, documentation review, error messages Determines which attack techniques are applicable
Input format API documentation, schema discovery, trial and error Required for crafting adversarial inputs
Output format Query the API with various inputs Confidence scores enable model extraction and boundary mapping
Framework Error messages, HTTP headers, dependency analysis Framework-specific vulnerabilities (CVEs in TensorFlow, PyTorch)
Rate limits Systematic testing of query rates Determines feasibility of extraction and brute-force attacks
Python 
import requests
import json

def recon_ai_api(base_url, sample_input):
    """Basic reconnaissance of an AI API endpoint."""

    # Test the endpoint
    response = requests.post(
        f"{base_url}/predict",
        json={"input": sample_input}
    )

    recon_data = {
        "status_code": response.status_code,
        "headers": dict(response.headers),
        "response_format": response.json(),
        "has_confidence": "confidence" in response.text,
        "has_probabilities": "probabilities" in response.text,
    }

    # Test error handling (may reveal framework info)
    error_resp = requests.post(
        f"{base_url}/predict",
        json={"input": "invalid_data_type"}
    )
    recon_data["error_info"] = error_resp.text

    return recon_data

Test Planning Matrix

Map each AI component to the relevant test categories:

Test Plan Template 
ENGAGEMENT:  [Client Name] AI Security Assessment
DATES:       [Start] - [End]
TESTER:      [Name]

TEST MATRIX:
  Component          | Black | White | Tests Planned
  -------------------|-------|-------|---------------------
  Classification API | Yes   | No    | Evasion, Extraction
  Training Pipeline  | No    | Yes   | Poisoning, Tampering
  Model Registry     | Yes   | No    | Access Control, Supply Chain
  LLM Chatbot        | Yes   | No    | Prompt Injection, Jailbreak
  Feature Store      | No    | Yes   | Data Integrity, Access

RULES OF ENGAGEMENT:
  - Maximum 10,000 API queries per day
  - No testing against production during business hours
  - Report critical findings immediately
  - No destructive testing on training data
Methodology Tip: Document every step as you go. Detailed notes on inputs, outputs, and observations are essential for report writing and for reproducing findings during validation.

Ready to Test Models?

Now that you have a structured methodology, the next lesson covers hands-on techniques for testing ML models for adversarial vulnerabilities, data leakage, and model extraction.

Next: Model Testing →
← Introduction Model Testing →