AI penetration testing assesses the security of a whole LLM-powered application against a standard — the OWASP Top 10 for LLMs — covering prompt injection, insecure output handling, sensitive-information disclosure, excessive agency, supply chain, and the surrounding app, API and infrastructure. AI red teaming focuses on the model's behaviour and safety, probing at scale for jailbreaks, harmful or biased output, and ways to break its guardrails. One secures the application; the other stress-tests the model's conduct. They overlap on prompt injection. If you've deployed an LLM app, start with an AI penetration test; add red teaming when the model's behaviour itself is the primary risk.
// 01 Two disciplines, two questions
The confusion is understandable — both involve attacking an AI system, and both use adversarial prompts. But they aim at different targets. AI penetration testing treats the LLM as one component of a system and asks whether that whole system — model, prompts, application code, APIs, data stores, infrastructure — is secure. AI red teaming treats the model itself as the subject and asks whether its behaviour can be pushed outside its intended bounds. This mirrors the split in traditional security between a penetration test (secure the system) and a red team (stress the whole under adversarial pressure).
| AI Penetration Test | AI Red Teaming | |
|---|---|---|
| Target | The whole LLM application | The model's behaviour |
| Question | Is the system secure? | Can it be made to misbehave? |
| Framework | OWASP LLM Top 10 | NIST AI RMF, MITRE ATLAS |
| Looks for | Injection, data leakage, insecure output, supply chain | Jailbreaks, harmful / biased output, guardrail bypass |
| Scope | App + API + infra + model | Model conduct at scale |
// 02 AI penetration testing: securing the whole system
An AI penetration test assesses your LLM-powered application the way any application would be tested — but with the AI-specific risks added. It works through the OWASP Top 10 for LLM Applications: prompt injection (direct and indirect), insecure output handling, sensitive information disclosure, improper output feeding downstream systems, excessive agency in agents and tools, supply-chain risk in models and plugins, and the APIs and cloud infrastructure around it. This is the assessment that satisfies customers, frameworks and due diligence, because it covers the security of the system end to end — not just the model's manners.
// 03 AI red teaming: stress-testing model behaviour
AI red teaming zeroes in on the model's conduct. It probes, usually at scale and with automation alongside human creativity, for the ways a model can be made to do what it shouldn't: jailbreaks that bypass safety instructions, prompts that elicit harmful, illegal or biased content, attempts to extract system prompts or training data, and manipulation of a customer-facing assistant into off-brand or dangerous output. Where an AI pentest asks "can an attacker compromise the system?", AI red teaming asks "can a user — malicious or not — get the model to violate its guardrails?" It's essential when the model's behaviour is itself a business, safety or reputational risk.
// 04 Where they overlap: prompt injection
The two meet at prompt injection — the number-one LLM risk, and the technique of making a model follow attacker-supplied instructions instead of its own. An AI penetration test evaluates prompt injection as one of the OWASP LLM Top 10, within the application's security posture: what can an injected instruction reach, and what damage can it do to the system? AI red teaming pushes injection further as a behavioural attack, chaining it with jailbreaks to force policy violations and unsafe output. Same root technique, two lenses — which is why any serious assessment of an LLM system tests it thoroughly.
// 05 Which do you need?
The practical guidance is straightforward. If you've built or deployed an LLM-powered application, start with an AI penetration test — it covers the security of the whole system, which is what regulators, enterprise customers and frameworks want to see. Add AI red teaming when the model's behaviour is your headline risk: a public-facing assistant that could be manipulated into harmful or brand-damaging output, or a high-stakes domain (health, finance, legal) where safety and alignment carry real consequence. Many mature AI deployments end up needing both. Map either against the OWASP LLM Top 10 and, for red teaming, emerging standards like the NIST AI RMF and MITRE ATLAS, so findings are traceable.
// 06 Frequently asked questions
AI red teaming vs AI penetration testing?
AI penetration testing secures the whole LLM application against a standard like the OWASP LLM Top 10 (injection, data leakage, insecure output, supply chain, plus app/API/infra). AI red teaming stress-tests the model's behaviour — jailbreaks, harmful or biased output, guardrail bypass. One secures the app; the other tests the model's conduct.
Which do you need?
If you've deployed an LLM app, start with AI penetration testing — it covers system security, which regulators and customers care about. Add AI red teaming when the model's behaviour is the primary risk (customer-facing assistants, high-stakes domains). Many need both; they overlap on prompt injection.
Is prompt injection part of both?
Yes — it's the #1 LLM risk and sits at the overlap. An AI pentest assesses it as one of the OWASP LLM Top 10 within system security; AI red teaming pushes it as a behavioural attack, chaining injection with jailbreaks to force policy violations.
What standards guide AI security testing?
The OWASP Top 10 for LLM Applications leads for AI penetration testing. AI red teaming draws on the NIST AI Risk Management Framework and MITRE ATLAS. A rigorous assessment maps findings to these for traceability.
// 07 References & related
- OWASP — Top 10 for LLM Applications; NIST — AI Risk Management Framework; MITRE ATLAS.
- Related: OWASP LLM Top 10 explained, prompt injection testing, and our AI penetration testing service.