AI & LLM Penetration Testing

Purpose-built security testing for AI systems, large language models, and the applications built on top of them — producing the independent assessment evidence to ensure your AI integrations are secure before exposing them to users or customers.

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When is AI / LLM security testing essential?

Before deploying AI features into production

AI components introduce new and often non-traditional attack surfaces. Before release, it is critical to validate how your system behaves under adversarial inputs, edge cases, and misuse scenarios — especially when LLMs are connected to internal systems, APIs, or sensitive data.

When integrating LLMs with business logic and tools

If your AI interacts with APIs, databases, plugins, or performs actions on behalf of users (AI agents), it can become an entry point for attackers. Testing ensures that prompt injection, indirect inputs, or malicious data sources cannot trigger unintended actions or data exposure.

For compliance, governance, and risk management

With emerging regulations such as the EU AI Act and EU CRA, organisations are expected to demonstrate control over AI risks. Security testing helps validate safeguards, document risks, and support internal governance and external audits.

How we perform AI security assessments

1. AI Threat Modelling & Architecture Review

We analyse how AI is embedded into your product: data flows, user inputs, system prompts, model selection, and integrations with external tools or internal systems. This helps identify critical trust boundaries and high-risk attack vectors specific to your implementation.

2. Adversarial & Scenario-Based Testing

We conduct manual adversarial testing, simulating real attacker behaviour:

Prompt injection and jailbreak attempts
Indirect attacks via external content (e.g. documents, web data, APIs)
Manipulation of AI outputs to influence decisions or workflows
Abuse of AI agents performing actions (e.g. sending requests, accessing data)

This is supported by AI-assisted techniques to scale testing, explore variations, and uncover edge cases that are difficult to detect manually.

3. Integration & Data Flow Security Validation

We assess how securely the AI component interacts with:

APIs and backend systems
Databases and internal tools
Third-party services and plugins

This includes validating authorisation controls, data exposure risks, and isolation between users and contexts.

4. Risk Analysis & Practical Recommendations

All findings are validated and prioritised based on real-world impact. We provide clear, actionable recommendations, including:

Prompt and system design improvements
Access control and validation mechanisms
Safe integration patterns
Monitoring and guardrail strategies

Our goal is not just to identify issues, but to help you build secure and reliable AI systems in practice.

Methodology

Our pentesting is based on emerging best practices (OWASP Top 10 for LLMs, adversarial testing frameworks) and focuses on real-world attack scenarios:

Prompt Injection & Jailbreaking (bypassing safeguards and system instructions)
Data Leakage & Training Data Exposure (sensitive information disclosure)
Insecure Integrations (LLM connected to APIs, databases, or tools)
Access Control Issues (unauthorised actions via AI agents)
Output Manipulation & Hallucination Risks
Business Logic Abuse via AI workflows
Model misuse and unintended behaviours
Third-party AI risks (OpenAI, Anthropic, open-source models)

We simulate attacker behaviour interacting with AI systems — including multi-step and indirect attacks.

FAQ

Does the EU AI Act require security testing for AI systems?

High-risk AI systems under the EU AI Act (Annex III categories including systems used in critical infrastructure, employment, financial services, and law enforcement) are subject to requirements including robustness testing, logging, and technical documentation. Our AI security assessment produces evidence relevant to these obligations. We advise on your specific system's risk classification as part of the engagement.

Does this apply to systems built on OpenAI / Anthropic / Azure OpenAI / other APIs?

Yes — the vulnerabilities exist at the application layer, regardless of which underlying model is used. We test the application you've built and the trust boundaries you've defined, not the model provider's infrastructure.

What if we use a fine-tuned or self-hosted model?

We cover both. Self-hosted models introduce additional attack surface — model extraction attempts, training data inference, and weight file access controls — that we include in scope on request.

How is this different from standard web application penetration testing?

Standard web app testing covers your API endpoints, authentication layer, and server-side logic — it operates at the HTTP layer. AI security testing operates at the semantic layer: what the model can be made to say, reveal, or do through natural language. These are complementary, not interchangeable. We recommend combining both for AI-powered applications.

What is indirect prompt injection and why does it matter?

Indirect prompt injection occurs when an attacker embeds malicious instructions inside content the LLM will read — a document uploaded by a user, a webpage the agent browses, a customer support ticket, or a database record. The model follows the attacker's instructions as if they came from the system prompt, without the application ever receiving a suspicious request. This is particularly relevant for agentic systems with tool access and for RAG pipelines that ingest external documents.