AI & LLM Penetration Testing

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Our AI and LLM penetration testing identifies how attackers could manipulate your AI systems, extract sensitive data, or cause unintended behaviour so you know where the real risks are and what to do about them.

IF YOUR ORGANISATION IS BUILDING WITH AI, INTEGRATING AN LLM, OR RUNNING AN AI-POWERED PRODUCT, IT'S IMPORTANT TO KNOW WHETHER YOUR AI IS ACTUALLY SECURE. STANDARD APPLICATION TESTING DOESN'T COVER THE ATTACK SURFACES AI INTRODUCES, AND MOST TEAMS HAVE NEVER HAD IT PROPERLY TESTED.

6+ Years Experience

Delivering security testing for organisations across a wide range of industries.

CREST Approved

Our testing meets industry-standard requirements for professional security assessments.

ISO Certified

Demonstrably rigorous information security management and information security practices.

What Is AI & LLM Penetration Testing?

AI and LLM penetration testing evaluates the security of applications that integrate artificial intelligence systems including chatbots, AI copilots, agents with tool access, and AI-driven decision logic.

Testing focuses on attack surfaces that only exist because AI is involved. This includes how the model responds to adversarial inputs, what it can be made to reveal, and how it interacts with the systems around it.

By testing your AI integration as an attacker would, we identify vulnerabilities that automated scanning cannot detect and give you clear, actionable guidance on what to fix.

Identify AI-specific vulnerabilities before attackers do

Understand the real impact of prompt injection, data leakage, and agent misuse

Receive clear guidance on what should be fixed first

AI Security

Why AI Security Testing Matters

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AI applications introduce risks that traditional security testing does not cover. Prompt injection, system prompt exposure, retrieval attacks, and agent misuse are all attack patterns specific to AI integrations and they are increasingly being exploited in the wild.

When AI vulnerabilities are identified early, they can be understood and remediated by the teams responsible for them. Left untested, they may remain hidden until a real incident occurs.

Clear Risk Visibility

Understand exactly what an attacker could do to your AI system and why it matters.

Actionable Findings

Receive practical remediation guidance your developers and engineering team can act on.

Key Areas

What Our AI & LLM Penetration Testing Covers

Our testing is guided by the OWASP Top 10 for LLM Applications and real-world attacker techniques specific to AI systems.

This means testing goes beyond what is theoretical, assessing how your AI integration could be abused in realistic attack scenarios.

Prompt Injection

Testing whether attackers can manipulate prompts to override system instructions, bypass guardrails, or retrieve restricted information.

System Prompt Exposure

Testing whether internal instructions or hidden context can be extracted — potentially revealing sensitive logic or proprietary data.

Sensitive Data Leakage

Testing whether the AI can be made to expose customer data, internal documents, API keys, or other information it should not return.

RAG & Agent Security

For retrieval-augmented systems and AI agents with tool or API access — testing document injection, data extraction, and unintended automation actions.

Testing scope is tailored to your AI architecture and the risk areas that matter most to your system.

Our Process

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Scope & Discovery

We start by understanding your AI system, its architecture, and the attack surfaces specific to how AI is integrated.

Realistic Testing

Our testers use adversarial techniques based on real-world attacker methods to identify how the AI can be manipulated or abused.

Analysis

We assess the findings, chain attack paths where relevant, and evaluate the real-world impact of each vulnerability.

Clear Reporting

You receive a clear, actionable report your developers and engineering team can use immediately.

Get Started Today In Understanding Your AI Security Risk

If you are building or operating an AI-powered application, we can help you identify the real risks before someone else does.

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FAQs

What types of AI systems can be tested?

We test a wide range of AI integrations — including LLM chatbots, AI copilots within SaaS platforms, agents with tool or API access, retrieval-augmented generation systems, and AI used in automated workflows or decision logic. If you are unsure whether your system is in scope, a short discovery call will clarify this quickly.

What does an AI and LLM penetration test involve?

Testing is tailored to your system. It typically involves adversarial prompt testing, attempts to extract system prompts or sensitive data, testing of retrieval and agent components, and evaluation of how AI outputs interact with backend logic. We focus on realistic abuse scenarios rather than theoretical risks.

Will testing affect our live AI system?

This depends on the architecture and agreed scope. We discuss access requirements and testing approach during scoping, and testing can be performed against staging or production environments based on what is appropriate for your system.

What is the difference between AI penetration testing and standard application testing?

Standard application testing covers traditional web and API vulnerabilities but does not address the attack surfaces introduced by AI. AI and LLM penetration testing specifically targets how the model behaves under adversarial conditions — including prompt injection, data leakage via the model, and agent misuse — which require a different testing methodology entirely.