Security Checklist for AI Pentest Tools

AI tools are fast becoming indispensable in pentesting. But before you use one, look past its capabilities to its own security: where it sends data, whether it stays within the agreed scope, and whether the operator keeps control of what it does. This checklist helps you assess those risks.

We prepared it as a companion to the talk by Sentra CEO Maxim Bragin at SOC Forum 2026. It applies to LLM-enabled tools and autonomous pentest agents, covering everything from code and dependency provenance to prompt injection resilience and a working kill switch. A separate section lists mandatory operating controls.

What’s inside

The checklist combines three groups of checks:

• 35 core checks: provenance and supply chain, scope enforcement, network egress and exfiltration, destructive actions and target impact, security of the tool itself, and legal and contractual constraints.
• 17 additional checks for AI agents: LLM provider usage, prompt injection defenses, sandboxed execution, memory storage, logging, and oversight of autonomous actions.
• 12 lab test cases: hands-on scenarios that expose out-of-scope activity, data leaks, access to operator secrets, unsafe actions, and the agent obeying instructions planted in target content.

How to use it

The PDF is fillable: tick the checkboxes and type straight into the fields.

1. Define what you’re assessing. Specify the tool, version, LLM, provider, and run configuration.
2. Work through the checks. Mark each one PASS, FAIL, or N/A. Partially met counts as FAIL.
3. Run the test cases. If a scenario fails, also mark FAIL on the checks listed after its arrow.
4. Make the decision. Check the blocking rules first, then add up the score and record any compensating controls.

Important: the decision applies only to the version, model, and configuration you reviewed. If any of them changes, run a new review.