AI Red Teaming ToolsAI penetration testing vs AI red teaming
Both attack your AI system on purpose. They differ in what counts as success and in how much of the system they look at.
| AI penetration testing | AI red teaming | |
|---|---|---|
| Goal | Find exploitable security vulnerabilities in the AI application | Show whether a realistic adversary can reach a goal: data out, a harmful action, harmful output |
| Scope | Fixed list of components and a time box | The system end to end, including people and processes around it |
| Typical findings | Prompt injection, insecure output handling, broken access control on retrieval, exposed secrets | The same, plus chains of weaknesses and harmful or misleading behaviour |
| Method | Methodical coverage of known vulnerability classes | Adaptive: the tester changes approach based on what works |
| Output | Vulnerability report with severity and fixes | Narrative of attack paths, findings with severity, and fixes |
What both should cover
Whichever you commission, check that the plan covers each OWASP Top 10 for LLM Applications 2025 (OWASP GenAI Security Project) category that applies to your system, and that findings name the MITRE ATLAS (Adversarial Threat Landscape for AI Systems) technique used, so you can compare reports over time.
- LLM01:2025 Prompt Injection: 9 test cases in our catalogue
- LLM02:2025 Sensitive Information Disclosure: 7 test cases in our catalogue
- LLM03:2025 Supply Chain: 3 test cases in our catalogue
- LLM04:2025 Data and Model Poisoning: 3 test cases in our catalogue
- LLM05:2025 Improper Output Handling: 5 test cases in our catalogue
- LLM06:2025 Excessive Agency: 10 test cases in our catalogue
- LLM07:2025 System Prompt Leakage: 4 test cases in our catalogue
- LLM08:2025 Vector and Embedding Weaknesses: 4 test cases in our catalogue
- LLM09:2025 Misinformation: 4 test cases in our catalogue
- LLM10:2025 Unbounded Consumption: 3 test cases in our catalogue
Questions to ask a provider
- Will you test the tools, retrieval sources and memory, or only the chat interface?
- Do you test indirect prompt injection through content the system reads?
- Will every finding include the exact input and output so we can reproduce and retest it?
- How do you avoid harming production data during agent tests?
- Can you map findings to OWASP and MITRE ATLAS IDs?
Build a red-team plan for your own system in two minutes: Open the free plan builder
Questions
Is AI penetration testing the same as AI red teaming?
They overlap. An AI penetration test usually has a fixed scope and time box and aims to find as many exploitable vulnerabilities as possible in the application. AI red teaming is usually goal-driven (can someone get customer data out, or make the agent pay an invoice?) and also covers harmful output, not only security flaws.
Which one do I need?
Before launch, most teams need both a security test of the application and its infrastructure and an adversarial test of the AI behaviour: prompt injection, data leakage and what the agent's tools can be made to do.