AI & LLM Security
LLM penetration testing, the way an attacker would.
Hands-on adversarial testing of LLM applications for prompt injection, data exposure, model abuse, insecure integrations and emerging AI attack vectors. Findings arrive as attack paths with business consequence attached.
- Practice
- AI & LLM Security
- Method
- Manual, adversary-led
- Covers
- Apps, RAG, agents, tools
- Mapped to
- OWASP LLM Top 10
What is LLM Penetration Testing?
LLM penetration testing is an offensive security test of an application built on a large language model. Testers attempt what a real attacker would: make the system ignore its instructions, leak data it should not, call tools it should not, or act on content an attacker planted.
It differs from an AI security assessment in emphasis. The assessment reviews the architecture and governance as a whole; the penetration test concentrates on proving, by exploitation, what can actually be done against the running system.
Who it is for.
- Product teams about to launch or expand an LLM feature
- Organisations whose customers or auditors ask for AI-specific test evidence
- Teams running AI agents with access to email, files, tickets, code or internal APIs
- Security teams that want an independent adversarial test of their AI guardrails
What we do.
Prompt injection testing
Direct prompt injection and jailbreaks, plus indirect injection through retrieved documents, web pages, emails and tool output.
Data exposure testing
Attempts to extract system prompts, other users' data, retrieval content and secrets through the model and its integrations.
Agent and tool abuse
Whether an attacker can steer an agent into calling tools, MCP servers or APIs outside its intended purpose, and what those calls can reach.
Integration and application testing
The surrounding web application and APIs: authentication, authorisation, session handling and output handling where model responses are rendered or executed.
AI red teaming
Objective-based scenarios run end to end, the way a motivated adversary would chain these weaknesses together.
What actually arrives.
An engagement that ends at a slide deck leaves you with an opinion. These are the artefacts this one produces.
Executive summary
What an attacker could achieve against the system, in business terms.
Attack-path narratives
How individual weaknesses chain into a real outcome, with the prompts, payloads and evidence that prove it.
Technical findings
Each issue with reproduction steps, affected components and a specific fix, mapped to OWASP LLM Top 10.
Retest
Verification that fixes hold, so the engagement ends at evidence rather than an opinion.
Frequently asked questions.
- Is LLM penetration testing the same as AI red teaming?
- They overlap. Penetration testing aims for coverage of the system's weaknesses; red teaming pursues a specific objective end to end. Most engagements combine both, and we agree the balance during scoping.
- Can you test a third-party model such as a hosted API?
- We test your application and how it uses the model: prompts, retrieval, tools, data and output handling. Testing the provider's own infrastructure is outside scope unless the provider permits it.
- Will testing affect production?
- Scope, environments and safe limits are agreed in writing before testing starts. Where possible we test in a staging environment that mirrors production.
- Do you retest after we fix the findings?
- Yes. An engagement that ends at the report leaves you with an opinion; one that ends at verified closure leaves you with evidence.
Next step
Talk to us about LLM Penetration Testing.
Start with a scoping conversation.
Describe the system and the decision you need to make. You will get a practitioner's view of where the risk actually sits, and what the engagement should cover, before anything is scoped.