AI Pentesting Methodology
A structured and ethical framework for assessing AI system security
1
Planning
2
Reconnaissance
3
Vulnerability Analysis
4
Exploitation
5
Reporting
1
Planning
Define the objectives, scope, legal permissions, and constraints of the AI pentest. This ensures the engagement is safe, authorized, and aligned with organizational goals.
Key Activities:
- Obtain explicit written authorization
- Define system boundaries, goals, and success criteria
- Establish secure data handling and retention policies
- Agree on rules of engagement and escalation procedures
- Identify compliance/regulatory requirements (GDPR, HIPAA, etc.)
2
Reconnaissance
Gather information about the AI system, its architecture, and its surrounding ecosystem to identify possible attack surfaces.
Key Activities:
- Map system components and integrations
- Document exposed API endpoints and interfaces
- Identify model type, training data sources, and pipelines
- Assess authentication, logging, and monitoring controls
- Review documentation, public repos, and related metadata
3
Vulnerability Analysis
Identify weaknesses in the AI model, its deployment environment, and supporting infrastructure. Focus on both technical and AI-specific vulnerabilities.
Key Activities:
- Test for prompt injection and prompt leakage
- Evaluate data poisoning and model evasion risks
- Assess robustness against adversarial examples
- Check for insecure default configurations
- Analyze model outputs for sensitive information leakage
4
Exploitation
Safely test vulnerabilities in a controlled manner to validate findings without causing harm or disruption to the system.
Key Activities:
- Conduct proof-of-concept exploit attempts under agreed safeguards
- Simulate real-world attack scenarios (adversarial prompts, model extraction)
- Document attack vectors and system behavior
- Verify impacts on confidentiality, integrity, and availability
- Maintain monitoring and rollback mechanisms
5
Reporting
Communicate findings clearly and responsibly, with actionable recommendations for remediation and risk mitigation.
Key Activities:
- Prioritize findings by severity, likelihood, and business impact
- Provide clear remediation guidance and secure configuration advice
- Include sanitized test cases and proof-of-concept details
- Recommend monitoring and detection improvements
- Deliver executive summaries and technical appendices
Ethical Considerations
- Always operate under formal authorization and scope
- Do not test production systems without explicit consent
- Respect user privacy and data ownership at all times
- Minimize disruption to business operations
- Follow coordinated vulnerability disclosure practices
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Frequently Asked Questions
What is the AI pentesting methodology?
The AI pentesting methodology is a structured approach to security testing AI and LLM systems. It covers five phases: Planning and Reconnaissance, Vulnerability Analysis, Exploitation and Validation, Post-Exploitation and Data Analysis, and Reporting and Remediation — adapted for the unique attack surface of AI systems including prompt injection, model extraction, and MCP server testing.
What are the phases of an AI security assessment?
An AI security assessment typically includes: 1) Planning — defining scope and rules of engagement, 2) Reconnaissance — mapping the AI system architecture, MCP tools, and data flows, 3) Vulnerability Analysis — testing for prompt injection, data poisoning, and model vulnerabilities, 4) Exploitation — safely demonstrating impact, 5) Reporting — documenting findings with remediation steps.
How do you test MCP servers for security vulnerabilities?
MCP server testing involves checking for authentication bypass, tool parameter injection, resource exhaustion, unauthorized data access through MCP tools, and prompt injection via MCP resources. Testers use specialized MCP security scanners and manual probing to identify vulnerabilities in the Model Context Protocol implementation.
What is the difference between AI pentesting and traditional pentesting?
AI pentesting differs from traditional pentesting in its focus on LLM-specific attack surfaces: prompt injection, model extraction, training data poisoning, and adversarial examples. Unlike traditional web app testing, AI pentesting also evaluates model behavior, output filtering, system prompt security, and MCP tool configurations.