Large Language Model (LLM) Assessment
Large Language Model (LLM) Security Assessment
Large Language Model (LLM) Assessment Large Language Model (LLM) Security Assessment As organisations integrate Large Language Models into their products, new attack surfaces emerge. Our LLM assessment tests your AI-powered applications against threats such as prompt injection, data leakage and insecure output handling, aligned to the OWASP Top 10 for LLM Applications.
Overview
What is an LLM security assessment?
An LLM security assessment evaluates applications built on Large Language Models for the unique risks they introduce — prompt injection, training-data and system-prompt leakage, insecure output handling, and abuse of connected tools and plugins.
We combine established application security techniques with AI-specific testing to help you deploy LLM features safely, providing clear remediation guidance for the issues we uncover.
What you'll receive
- ✓Scoping: agreed models, applications, integrations and abuse cases in scope
- ✓Testing: prompt injection, jailbreaks, data leakage and insecure output handling aligned to the OWASP LLM Top 10
- ✓Executive report: an AI risk summary written for stakeholders and product owners
- ✓Technical report: reproducible findings with example prompts and impact analysis
- ✓Remediation: guardrail, filtering and architecture guidance for your AI stack
- ✓Retest & debrief: a retest of fixes and a walkthrough call with your team
Why It Matters
The value of regular security reviews
Evaluate your controls
Regular security reviews enable organisations to comprehensively evaluate their security measures, including the effectiveness of controls, configurations, and policies.
Strengthen your posture
Identifying weaknesses allows organisations to strengthen their security posture and better protect their assets and data against evolving threats.
Meet compliance
Regular security assessments are essential for compliance with industry regulations and data protection laws, demonstrating your commitment to a secure environment.
High-Level Methodology
Our LLM testing focus areas
Prompt Injection
We attempt to override the model's system instructions through direct and indirect prompt injection — including content hidden in documents, web pages and other untrusted data the model processes.
Sensitive Data Leakage
We test whether the model can be coerced into revealing its system prompt, training data, or sensitive information belonging to other users or the host organisation.
Insecure Output Handling
We assess how the application handles model output — checking whether unvalidated responses can drive downstream attacks such as cross-site scripting, SSRF or code execution.
Tools, Plugins & Agents
Where the model can call tools, plugins or agents, we test for excessive agency and over-permissioning that could let an attacker perform actions beyond the intended scope.
Analysis & Reporting
We analyse the impact of the issues discovered and determine the overall risk to the business, then deliver a clear report with prioritised, context-specific remediation guidance.
FAQ
Frequently asked questions
Which LLM applications can you test?
We can assess applications built on commercial models (such as those from OpenAI, Anthropic or Google) as well as self-hosted and open-source models, including chatbots, RAG systems and agentic tools.
How much does an LLM assessment cost?
Cost depends on the complexity of the application, the number of features and integrations in scope, and whether connected tools or agents require testing. Contact us for a customised quote.
What information is required to scope a test?
To accurately scope a penetration test, we typically need information about your network range (IP addresses), domain names, key systems and applications, and any specific security concerns you have.
Do you follow the OWASP Top 10 for LLMs?
Yes. Our methodology is aligned to the OWASP Top 10 for LLM Applications, supplemented by our own testing experience and the specifics of your deployment.
What are the most common issues you find?
Recurring findings include prompt injection, sensitive-information and system-prompt disclosure, insecure output handling, excessive agency over connected tools, and weak controls around plugins and integrations — in line with the OWASP Top 10 for LLMs.
Deploying LLM features safely?
Get a fast, transparent quote for your LLM security assessment, or talk to a consultant about scoping the right engagement.