AI Security · Global
AI penetration testing, against the OWASP LLM Top 10.
You shipped an LLM, an agent, or a copilot. We test it the way an attacker would, working through every class of AI weakness, prove which ones are exploitable, and hand you the fixes. Structured, evidence-led, human-verified.
CREST-certified operators · every finding human-verified · OWASP LLM Top 10 · NIST AI RMF
What is AI penetration testing?
AI penetration testing is a structured security test of an AI application, an LLM, an agent, or a copilot, against a known set of weakness classes. A tester probes the deployed system, proves which weaknesses are actually exploitable, and delivers the fixes. It is how you find the flaws in an AI feature before an attacker does.
The reference list is the OWASP Top 10 for LLM Applications. A competent AI penetration test works through all ten against your real system, then reports coverage so your board can see what was checked and what held.
You will see the same work sold as AI pentesting, LLM penetration testing, or AI security testing. The labels are interchangeable. What separates a real engagement from a scanner run is whether a person attacked your deployed system and proved the finding.
What we test
Every class in the OWASP LLM Top 10
The 2025 industry reference list of critical LLM risks. We attack each one against your deployed system.
| # | Risk | What we test |
|---|---|---|
| LLM01 | Prompt injection | We hide instructions in the content your model reads and turn it against its own rules, direct and indirect. |
| LLM02 | Sensitive information disclosure | We try to make the model reveal training data, secrets, or other users’ information. |
| LLM03 | Supply chain | We check the models, datasets and plugins you depend on for tampering and known weaknesses. |
| LLM04 | Data and model poisoning | We test whether training or fine-tuning data can be corrupted to plant behaviour. |
| LLM05 | Improper output handling | We test what happens downstream when the model’s output is trusted: XSS, SSRF, code execution. |
| LLM06 | Excessive agency | We push an agent to use its tools and permissions beyond what it should, the highest-impact class. |
| LLM07 | System prompt leakage | We extract the hidden system prompt and any secrets or logic it exposes. |
| LLM08 | Vector and embedding weaknesses | We attack the RAG layer: poisoned documents, embedding inversion, retrieval abuse. |
| LLM09 | Misinformation | We probe where the model produces confident, wrong output your business would act on. |
| LLM10 | Unbounded consumption | We test for denial-of-wallet and resource exhaustion through crafted requests. |
The model layer
What is LLM penetration testing?
The same discipline, aimed at the language model itself and everything it is wired into.
LLM penetration testing is an authorised attack on a large language model application: the model, the prompts around it, the documents it retrieves, the tools it can call, and the permissions it holds. A tester proves which of those can be turned against you, then hands back the guardrail that closes each one.
We test the deployed system rather than the model in isolation, because that is where the damage happens. A model answering questions in a sandbox has a small blast radius. The same model wired to your CRM, your mailbox and your payment API has a very different one, and the second setup is what you shipped.
Three findings recur across almost every engagement. Indirect prompt injection through content the model reads without a human ever typing it. System prompt extraction that reveals your logic and sometimes your keys. And tool misuse, where an agent is talked into using a permission it holds legitimately, for a purpose you never intended.
Which test do you need?
AI pen test vs traditional pen test vs AI red team
Every assessment starts where an attacker would: outside, watching, looking for the one door left ajar. We find it, then we show you the walk-through.
| Activity | Approach | Best for |
|---|---|---|
| Traditional pen test | Known weakness classes in infrastructure and code | The servers and APIs around the model, not the model itself |
| AI penetration test | Structured, works through the OWASP LLM Top 10 | Coverage: proving you tested the known AI risk classes |
| AI red team | Adversarial, open-ended, goal-driven | Realism: how a real attacker chains flaws through the whole system |
What you get
Proof, mapped and prioritised
Not a scanner dump. The attacks that worked, ranked, with the fix for each.
- Every finding mapped to its OWASP LLM Top 10 category, so it fits your reporting
- Proof of exploit, not a scanner’s guess: we show the attack that worked
- A prioritised fix list with the specific guardrail for each finding
- Verified by a CREST-certified operator before it reaches you
Before you ask
AI penetration testing, answered
Every assessment starts where an attacker would: outside, watching, looking for the one door left ajar. We find it, then we show you the walk-through.
What is AI penetration testing?
AI penetration testing is a structured security test of an AI application, an LLM, an agent, or a copilot, against a known set of weakness classes, most commonly the OWASP Top 10 for LLM Applications. A tester probes the deployed system for prompt injection, sensitive data disclosure, excessive agency, insecure output handling and the rest, proves which ones are exploitable, and delivers the fixes. It is how you find the flaws in an AI feature before an attacker does.
What is the OWASP LLM Top 10?
The OWASP Top 10 for LLM Applications is the industry reference list of the most critical security risks in systems built on large language models. The 2025 edition covers prompt injection, sensitive information disclosure, supply chain, data and model poisoning, improper output handling, excessive agency, system prompt leakage, vector and embedding weaknesses, misinformation, and unbounded consumption. It is the checklist a competent AI penetration test works through.
How is AI penetration testing different from LLM red teaming?
They overlap and the terms are often used together. AI penetration testing is structured: it works through a known list of weakness classes, typically the OWASP LLM Top 10, and reports coverage against it. AI red teaming is adversarial and open-ended: it attacks the whole system toward a goal, the way a real threat actor would, without a fixed checklist. Most mature programmes use both, the pen test for coverage and the red team for realism.
What is LLM penetration testing?
LLM penetration testing is an authorised attack on an application built on a large language model, covering the model, its system prompt, the documents it retrieves, the tools it can call and the permissions it holds. A tester proves which of those can be turned against the business and hands back the guardrail for each. It is the same discipline as AI penetration testing, and the two terms are used interchangeably. The work is scoped against the OWASP Top 10 for LLM Applications so you can report coverage.
How do you penetration test an LLM application?
We test the deployed system, not the model in isolation. We map what the AI can read, the tools and APIs it can call, and the permissions it holds, then attack each OWASP LLM Top 10 class against it: injecting instructions through the content it ingests, attempting data exfiltration, forcing tool misuse, extracting the system prompt, and poisoning the retrieval layer. Every exploitable finding is proven and handed back with the guardrail to close it.
Find the flaws in your AI before an attacker does.
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