LLM Security Assessment
A structured, expert-led evaluation of an LLM-backed application for AI-specific vulnerabilities, using the OWASP LLM Top 10 as the structuring framework for findings.
An LLM security assessment is a structured, expert-led evaluation of an application built on or around a large language model, checking it for AI-specific vulnerabilities that standard testing does not surface. It examines how your model, prompts, integrations, and application logic behave under adversarial conditions.
This is worth clearing up a common misconception. An LLM security assessment is not a rebadged web application penetration test. It reuses solid application security fundamentals, then adds the AI-specific attack surface on top: prompt manipulation, unsafe handling of model output, and data leakage through the model itself.
Web Application Penetration Test
Secures the surrounding application, but stops short of testing how the model itself can be manipulated. SAST, DAST, and SCA examine code, the running app, and dependencies — not model behaviour.
LLM Security Assessment
Adds AI-specific attack surface on top of standard application security, testing the prompt-to-output path and failure modes that only exist because a language model is in the loop.
This service is built for CISOs, security leads, engineering leads, product owners, and founders shipping AI features. If you are shipping an AI feature, adding a chatbot or agent, or exposing an LLM API, this assessment is designed to confirm that surface is safe before or shortly after it goes live. It is delivered as UK and London expert-led consultancy work, with senior testers leading each engagement.
The typical trigger is concrete:
- exposing a model to real users
- adding retrieval over internal documents
- wiring an agent into tools and systems
What the Assessment Tests and What You Receive
Testing is organised around the OWASP LLM Top 10 and translated into what each risk means for your product, not just a vulnerability label. Findings speak the same language your governance and engineering teams already use. The table below maps the core risks we test to their practical business impact.
| OWASP LLM risk | What it means for your product |
|---|---|
| Prompt injection | A user, or hidden text in retrieved content, overrides your instructions and makes the model act against your intent. |
| Sensitive data disclosure | The model reveals training data, connected records, or another user's information. |
| Improper output handling | Model output is trusted downstream and triggers injection, code execution, or broken workflows. |
| System prompt leakage | Your hidden instructions, guardrails, or secrets are exposed, letting an attacker plan around your controls. |
| Unbounded consumption | Uncontrolled requests drive token cost, degrade service, or enable denial-of-wallet abuse. |
Many of these flaws are context-dependent. A business-logic prompt injection that abuses your specific tools, permissions, or workflow will not trigger a generic scanner, because the scanner does not understand what your application is allowed to do. That is why testing is expert-led and human-in-the-loop rather than tool-only, with automated tooling used to widen coverage where it helps.
At the end you receive:
- Findings report with each vulnerability risk-rated by severity and business impact.
- Remediation guidance your engineers can act on.
- Retest to confirm fixes hold.
Scope, Suitability and Safe Testing Checks
Scope depends on how much AI you have built and how far it reaches into your systems. The main drivers are:
- Number of models and distinct AI features in scope.
- RAG or agentic architecture that lets the model retrieve data or take actions.
- Integrations and tools the model can call, and the permissions attached to them.
- Sensitivity of the data the model can reach.
- Access provided to source code, architecture detail, and a staging environment.
This assessment is worth prioritising if you are shipping an AI feature soon or already run one in production, especially where it touches customer or internal data, calls external tools, or acts autonomously. It can reasonably wait where a model is isolated, handles no sensitive data, and has no live users.
It complements rather than replaces your existing testing: SAST reviews source code, DAST probes the running application, and SCA checks dependencies, while an LLM assessment targets model behaviour and the prompt-to-output path that those tools do not evaluate. It also differs from a standard web app pentest, which secures the surrounding application but stops short of testing how the model itself can be manipulated.
Testing is designed to protect your environment. Where prompt manipulation, data exposure, or token consumption could disrupt a live service, work is run against a controlled or staging environment, and consumption-sensitive tests are throttled and scoped so they do not run up uncontrolled cost. Exact scope is confirmed with you at the enquiry and scoping stage.
How the Engagement Works and Why Choose Us
The engagement runs in clear stages so you know what to expect after enquiry:
- Scoping – confirm the AI features, architecture, data sensitivity, and testing environment.
- Testing – expert-led assessment across the application and model interaction layers.
- Reporting – risk-rated findings with business impact and remediation guidance.
- Remediation support and retest – validate that fixes resolve the issues raised.
To scope accurately we need:
- a description of the AI features and how they are used
- architecture detail including any RAG or agentic components and connected tools
- an indication of data sensitivity
- access to a suitable staging or test environment
Where relevant, sharing the system prompts and guardrails already in place helps us test how well they hold. The more of this you can provide early, the tighter and more useful the assessment.
Work is expert-led and aligned to the OWASP LLM verification standard, delivered by a UK-based consultancy familiar with the governance and assurance expectations enterprise and regulated teams work to. To move forward, contact us to arrange a scoping conversation and receive a tailored assessment plan for your AI system.
Frequently Asked Questions
How is an LLM security assessment different from a standard web app penetration test?
A web app pentest secures the application around the model but does not evaluate model behaviour itself. An LLM assessment adds AI-specific attack surface such as prompt injection, system prompt leakage, and unsafe output handling, and it goes beyond SAST, DAST, and SCA, which examine code, the running app, and dependencies rather than the model. Choose it when your product's risk lives in how the model interprets input and generates output.
Will testing my LLM affect production, leak data, or increase costs?
Testing is planned to avoid disruption, and consumption-sensitive or data-exposing tests are throttled and managed to control token cost. Where there is real risk to a live service, work is run against a controlled or staging environment. The safe testing approach is agreed with you during scoping.
Do I need this before launch or can it wait until after?
Testing before launch catches issues while they are cheapest to fix and before real users can trigger them. If a feature is already live, especially one touching sensitive data or calling tools, assess it promptly. It can reasonably wait only where a model is isolated, handles no sensitive data, and has no live users.
What do you need from me to scope the assessment?
We need a description of your AI features, architecture detail including any RAG, agentic, or integrated tools, an indication of data sensitivity, and access to a staging or test environment. Sharing any existing system prompts and guardrails helps too. Sharing this early produces a tighter scope, and final scope is confirmed at the scoping stage before testing begins.
Ready to scope your LLM security assessment?
Contact us to arrange a scoping conversation and receive a tailored assessment plan for your AI system. We'll help you confirm features, architecture, data sensitivity, and the right testing environment.