AI Security Checklists
Structured testing protocols for comprehensive assessments
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Checklist Guidance
Use these checklists to ensure thorough and lawful security testing
Pre Engagement
3 itemsComplete before any testing begins
Testing
3 itemsApply during active testing phases
Reporting
3 itemsPrompt Injection
10 itemsMcp Security
10 itemsLLM Pentesting
10 itemsAgentic Ai
10 itemsChecklist Progress
Overall Completion
0%
Custom Checklist Builder
Checklist Tips
- Complete items in order where dependencies exist
- Document exceptions with rationale
- Review with stakeholders periodically
- Update checklists as standards evolve
Align each item with the AI security standards it maps to, so the checklist stays auditable.
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Frequently Asked Questions
What should be included in an AI pentesting checklist?
An AI pentesting checklist should cover pre-engagement planning, reconnaissance of LLM endpoints and MCP servers, prompt injection testing (direct and indirect), model extraction attempts, data poisoning verification, output handling review, API security assessment, and reporting guidelines.
How do you test prompt injection vulnerabilities?
Testing prompt injection involves crafting inputs that attempt to override system prompts, bypass content filters, or extract sensitive instructions. Testers use techniques like delimiter injection, role-playing scenarios, encoding bypasses, multi-language attacks, and indirect injection through documents or web content the LLM processes.
What are the key checks for MCP server security?
Key MCP server security checks include verifying authentication mechanisms, testing tool parameter validation, checking for resource exhaustion vulnerabilities, auditing MCP resource permissions, testing for prompt injection through tool outputs, and validating the MCP server's rate limiting and access controls.
What should an LLM pentesting checklist include?
An LLM pentesting checklist should include mapping the application's attack surface (endpoints, tools, data sources), direct and indirect prompt injection tests, system prompt extraction attempts, output sanitization verification, excessive agency checks, training data extraction probes, model denial of service tests, API authentication/authorization review, and logging/leakage inspection.
How should you document AI security assessment findings?
AI security findings should be documented with: a clear description of the vulnerability, affected AI components (LLM model, MCP tool, API endpoint), proof of concept showing impact, severity rating using CVSS adapted for AI systems, and actionable remediation steps prioritized by risk.