What is the difference between AI red teaming and penetration testing?

A penetration test targets your infrastructure, applications and networks, and asks whether an attacker can gain unauthorised access. It works against a known catalogue of technical flaws, such as the OWASP Web Security Testing Guide, and a finding is a vulnerability like an injection point or a misconfigured server. AI red teaming targets the behaviour of a model, copilot or agent, and asks whether an attacker can make it act against its own rules through prompt injection, data exfiltration through the model, or tool and agent abuse. It works against AI-specific frameworks such as the OWASP LLM Top 10 and MITRE ATLAS, and a finding is often a manipulated output or an action the system was never meant to take. The two overlap where an AI feature runs on ordinary infrastructure, but neither replaces the other.

Does a penetration test cover our AI or LLM features?

Only the parts of them that behave like normal software. A standard penetration test will check the API around your model, the authentication, the hosting and the data stores, and it should. It will not test whether the model itself can be talked into ignoring its instructions, leaking its system prompt, or misusing a connected tool, because those are not classic technical vulnerabilities. They are failures of the model behaving exactly as designed while an attacker steers it. If your AI feature reads untrusted content and can then take an action, a pen test alone leaves the largest new risk untested.

What frameworks does AI red teaming use?

The three most cited are the OWASP Top 10 for Large Language Model Applications (2025), which names the ten weakness classes from prompt injection to unbounded consumption; MITRE ATLAS, which maps real adversary tactics and techniques against AI systems in the same style as ATT&CK; and the NIST AI Risk Management Framework with its Generative AI Profile. In the United Kingdom the DSIT and NCSC Code of Practice for the Cyber Security of AI sets the baseline expectation, and it explicitly calls for adversarial testing of AI systems before and after deployment.

Do we need both AI red teaming and a penetration test?

If you have deployed an AI feature that matters, yes. The penetration test proves the infrastructure and application around the model hold up. The AI red team proves the model and any agent behaviour hold up. Skipping the pen test leaves the plumbing untested; skipping the AI red team leaves the one part of the system that can be manipulated with plain language untested. The sensible order is to run them against the same system and read the findings together, because the worst attack paths usually cross from one into the other.

Field Notes / AI Security

AI Red Teaming vs Penetration Testing: What the Difference Actually Means for Your AI System.

A penetration test asks whether an attacker can break into your systems. AI red teaming asks whether an attacker can make your model, copilot or agent do something it should never do. They use different playbooks, find different failures, and one does not substitute for the other. Here is where each one applies, mapped to the frameworks your auditors already cite.

Author: Red Team Partners
Read: 11 MIN READ
Filed: 3 Sep 2026

The short answer

A penetration test asks one question: can an attacker get in? It targets your networks, servers and applications, works through a known catalogue of technical flaws, and hands you back the ways in that it found. AI red teaming asks a different question: can an attacker make your model do something it should never do? It targets the behaviour of a model, copilot or agent, and the failures it finds are rarely broken code. They are the system working as designed while an attacker steers it with words.

That is why one does not stand in for the other. A pen test will check the API and the servers around your AI feature, and it should. It will not tell you whether the model can be talked into leaking its system prompt, exfiltrating data through its own output, or misusing a tool it is connected to. Those are the risks that arrived with AI, and they need a playbook built for AI OWASP LLM Top 10, 2025 .

01 What a pen test tests

penetration test is a controlled attack on your infrastructure and applications. A tester takes the position of an adversary and tries to gain unauthorised access, escalate privilege, and reach data that should be out of reach. The discipline is mature. It runs against published methodologies such as the Penetration Testing Execution Standard and the OWASP Web Security Testing Guide, and in the United Kingdom it is accredited through schemes like CREST PTES / OWASP WSTG .

A finding from a pen test is a vulnerability with a name. An SQL injection point. A server missing a patch. An access control that lets a standard user reach an admin function. A password policy that never forced a change. Each one is a defect in how the system was built or configured, and each one has a fix that closes it. You can retest and confirm the hole is gone, because the hole was concrete.

When your AI feature runs on ordinary infrastructure, and it does, a pen test covers that infrastructure well. The hosting, the API gateway, the authentication, the databases behind the model: all of it is fair game and all of it should be tested. The gap opens at the model itself, because the model is not vulnerable in the way a server is. It is suggestible in a way a server is not.

02 What AI red teaming tests

I red teaming is a goal-oriented attack on the behaviour of an AI system. The tester does not look for a broken function. They look for a way to make the model act against its own instructions, and they treat the model, the data it can reach, and the tools it can call as one attack surface. The reference catalogue is the OWASP Top 10 for Large Language Model Applications, refreshed for 2025, and the adversary tactics are mapped in MITRE ATLAS, the AI counterpart to the ATT&CK matrix your SOC already knows MITRE ATLAS .

The 2025 OWASP list names the weakness classes an AI red team works through. They are not bugs in the classic sense. They are the ways a system that reads language and takes action can be turned against its owner.

OWASP LLM Top 10 (2025)	What it means in plain terms
LLM01 Prompt Injection	An attacker plants instructions the model obeys, often hidden in content it reads
LLM02 Sensitive Information Disclosure	The model reveals data, secrets or its own configuration it should not
LLM03 Supply Chain	A compromised model, dataset or plugin brings the risk in with it
LLM04 Data and Model Poisoning	Tampered training or fine-tuning data bends the model's behaviour
LLM05 Improper Output Handling	Downstream systems trust model output and execute what they should not
LLM06 Excessive Agency	An agent holds more permission or tool access than the task needs
LLM07 System Prompt Leakage	The hidden instructions steering the model are extracted by an attacker
LLM08 Vector and Embedding Weaknesses	The retrieval layer behind the model is manipulated or leaks data
LLM09 Misinformation	The model produces confident, wrong output that a user acts on
LLM10 Unbounded Consumption	An attacker drives cost or denial of service through the model itself

A finding here rarely looks like a vulnerability report. It looks like a transcript. The tester shows the exact input that made the copilot ignore its guardrails, or the sequence that made an agent call a tool it should have refused. The fix is rarely a patch. It is a change to the system prompt, the tool permissions, the input handling, or the boundary between what the model reads and what it is allowed to do.

03 Side by side

Read the two disciplines across the dimensions that decide which you need. They share the mindset of the attacker. They diverge on almost everything else.

	Penetration testing	AI red teaming
Core question	Can an attacker get in?	Can an attacker make the AI misbehave?
Target	Networks, servers, applications, APIs	Models, copilots, agents, the data and tools around them
A finding is	A named technical vulnerability	A manipulated output or an unintended action
Frameworks	PTES, OWASP WSTG, CREST	OWASP LLM Top 10, MITRE ATLAS, NIST AI RMF
The fix is usually	A patch, a config change, an access rule	A prompt, permission, input or boundary change
Repeatability	Deterministic; the hole is there or it is not	Probabilistic; the same input can vary by run
Maturity	Decades of accredited practice	An emerging discipline, moving fast

The line that matters most is repeatability. A pen tester confirms a fix by rerunning the attack and watching it fail. An AI red teamer works against a system that can answer the same prompt two ways, so a single clean run proves little. Testing has to probe the behaviour many times, across variations, which is why AI red teaming leans on adversarial creativity rather than a scanner alone.

04 Which one you need

tart with what you have shipped. If you run standard applications and infrastructure with no AI in the path, a penetration test is the right test and AI red teaming does not apply yet. If you have deployed a model, a copilot or an agent that customers or staff actually use, you have opened a surface a pen test was never built to cover, and you need both.

A short way to decide. Ask what the AI can reach and what it can do. A model that only answers questions from a fixed, trusted source and takes no action carries modest risk, and a pen test of the surrounding application may be enough for now. A model that reads content you do not control, holds tools it can call, or acts on behalf of a user needs an AI red team, because every one of the OWASP LLM classes above is now in play.

Regulation is closing the same gap. The UK Code of Practice for the Cyber Security of AI, published by DSIT with the NCSC, sets adversarial testing of AI systems as a baseline expectation across the lifecycle, and the NIST AI Risk Management Framework asks the same in its Generative AI Profile. Boards that treated AI testing as optional in 2024 are being asked to evidence it in 2026 DSIT / NCSC Code of Practice .

05 How they fit together

The honest answer is that they belong on the same engagement. The worst attack paths cross the boundary between them. A prompt injection tricks an agent into calling an internal API, and from there the attack becomes a classic privilege problem a pen tester recognises. A misconfigured database found in a pen test turns out to hold the training data that poisons a model. Read the two sets of findings apart and you miss the chain. Read them together and the real risk stands out.

Key takeaways

A pen test asks whether an attacker can get in. AI red teaming asks whether an attacker can make the model misbehave. Different questions, different playbooks.
A pen test finding is a named vulnerability with a patch. An AI red team finding is often a transcript, and the fix is a prompt, permission or boundary change.
A standard pen test covers the infrastructure around your AI, not the model behaviour. Prompt injection, data exfiltration and tool abuse stay untested.
If your AI reads untrusted content and can then take an action, it needs an AI red team, mapped to the OWASP LLM Top 10 and MITRE ATLAS.
The UK Code of Practice and the NIST AI RMF now expect adversarial testing of AI systems. It is moving from optional to evidenced.
Run both against the same system and read the findings together. The worst attack paths cross from one discipline into the other.

If you have shipped an AI feature and you are not certain which test it needs, that uncertainty is the finding. Start with a free audit. We tell you where your exposure sits across both disciplines, then give you a fixed price to close it.

References

Sources

OWASP. Top 10 for Large Language Model Applications, 2025 (v2.0). genai.owasp.org
MITRE. ATLAS (Adversarial Threat Landscape for Artificial-Intelligence Systems). atlas.mitre.org
NIST. AI Risk Management Framework (AI RMF 1.0) and the Generative AI Profile (NIST AI 600-1). nist.gov
NIST. Adversarial Machine Learning: A Taxonomy and Terminology of Attacks and Mitigations (NIST AI 100-2e2025). csrc.nist.gov
DSIT and NCSC. Code of Practice for the Cyber Security of AI. United Kingdom, 2025. gov.uk
PTES. Penetration Testing Execution Standard, technical guidelines. pentest-standard.org
OWASP. Web Security Testing Guide (WSTG). owasp.org