AI features are reaching production faster than they are being secured. A large language model can be persuaded to ignore its instructions, made to hand back data it was told to protect, or given tools it can be tricked into misusing. We test yours against the OWASP Top 10 for LLM Applications, the same evidence-first testing we bring to every other system, pointed at a new one.
Tested against the OWASP Top 10 for LLM Applications, the recognised standard for where these systems fail.
Manual-led testing, tooling used to support the tester rather than replace them.
Every engagement runs against the OWASP Top 10 for LLM Applications. Not every category applies to every system: scoping decides which matter for yours, and every finding is mapped back to the category it came from.
Crafted input that makes the model ignore its own instructions, typed directly or hidden inside content it reads.
The model handing back personal data, secrets or proprietary information it should have kept.
Risk inherited from third-party models, datasets, adapters and libraries.
Malicious data bending behaviour through training, fine-tuning or retrieval sources.
Downstream systems trusting model output without checking it, opening the door to XSS, SQL injection or code execution.
A model given more permission or autonomy than it needs through its tools and plugins.
Secrets or security logic hidden in the system prompt that were never safe to keep there.
Attacks on the retrieval and embedding layer, including poisoning, inversion, and cross-tenant leakage.
Confident, wrong output being relied on where accuracy matters, including hallucination and over-reliance.
Resource exhaustion, runaway cost, and the model itself being copied through unlimited querying.
Findings are rated Critical, High, Medium or Low, the same scale we use on every engagement, so an AI test slots straight into the risk picture you already work from.
Map the system, its data, its tools and its trust boundaries. Decide which categories are in scope.
Work through the OWASP LLM Top 10 manually, chaining weaknesses together the way a real attacker would.
Findings tied to an OWASP category, rated by severity, with a clear reproduction and a fix.
Every fix independently retested and verified before we call it closed.
It is the recognised industry standard for security risks in large language model applications, covering issues such as prompt injection, sensitive information disclosure, supply chain risk, excessive agency and unbounded consumption.
It targets the model and everything feeding it, on top of the usual application layer: how it can be manipulated through prompt injection, what data it can be made to leak, and what actions it can be tricked into taking through its tools. See our general penetration testing service for the application layer itself.
Yes. We test AI and LLM features at any stage, whether newly built, mid-development, or already live and handling real users.
Yes. We assess vector and embedding weaknesses, including retrieval poisoning, embedding inversion, and whether one user's data can be reached through another user's session.
We already run these assessments against the OWASP LLM Top 10 today. Tell us what your AI system does, and we'll scope a test that actually answers it.
Confidential, no obligation, and scoped around your actual system.