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AI Security Testing

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AI Security Testing

AI systems introduce a whole new set of security risks that traditional testing does not cover. If your organisation is building, integrating, or deploying AI and machine learning systems, you need to understand where the vulnerabilities are before someone else finds them.

At Clearnet Labs, we test AI systems the way attackers would approach them. That means looking at prompt injection, data leakage, model manipulation, and the security of the infrastructure around your AI components. We focus on practical, real-world risks rather than theoretical concerns.

Whether you are running large language models, using third-party AI APIs, or building custom ML pipelines, our testing covers the specific threats that matter to your setup.

AI Security Testing

What We Test

  • Prompt Injection - Testing for direct and indirect prompt injection attacks against LLMs and chat-based interfaces.
  • Data Leakage - Checking whether your AI systems expose training data, personal information, or sensitive business data through their outputs.
  • Model Input and Output Validation - Reviewing how inputs are sanitised and outputs are filtered to prevent misuse.
  • Authentication and Access Control - Testing how AI endpoints handle identity, permissions, and privilege escalation.
  • API Security - Assessing the security of APIs that serve or interact with AI models, including rate limiting, authentication, and data handling.
  • Supply Chain Risks - Evaluating third-party models, plugins, and dependencies for known vulnerabilities and trust issues.
  • Data Poisoning - Assessing risks around training data integrity and the potential for adversarial manipulation.
  • Infrastructure Security - Reviewing the hosting, networking, and configuration of the systems that run your AI workloads.

Our Approach

We treat AI testing like any other security engagement, with a structured methodology tailored to the specific risks of AI systems.

1. Scoping and Threat Modelling

We start by understanding your AI architecture, what models you use, how they are deployed, and what data flows through them. This lets us focus on the risks that actually matter.

2. Active Testing

We run hands-on tests against your AI systems, including prompt injection attempts, output manipulation, data extraction, and access control testing.

3. Infrastructure Review

We review the surrounding infrastructure, APIs, data pipelines, and integrations to identify weaknesses that could be exploited through or alongside your AI components.

4. Reporting and Remediation

You get a clear report covering every finding, its real-world impact, and specific steps to fix it. We are available to walk through the results with your team.

Deliverables

Every AI security testing engagement includes:

  • Executive Summary - High-level overview of AI-specific risks and their business impact.
  • Technical Findings - Detailed write-ups of each vulnerability with reproduction steps and evidence.
  • Risk Assessment - Prioritised findings based on exploitability and business impact.
  • Remediation Guide - Practical recommendations for fixing each issue, tailored to your stack.
  • Architecture Review - Observations on your AI infrastructure and suggestions for improving its security posture.
  • Retest Option - Follow-up testing to validate that fixes have been properly implemented.

Frequently Asked Questions

We test large language models, chatbots, ML pipelines, AI-powered APIs, recommendation systems, and custom AI applications. If it uses AI or machine learning, we can test it.
Yes. Traditional penetration testing focuses on network, web, and application vulnerabilities. AI security testing looks at risks specific to AI systems, like prompt injection, model manipulation, and data leakage through model outputs. We often recommend doing both.
We can test how your application integrates with third-party AI services and whether those integrations introduce security risks. We focus on the parts you control, like how you handle API keys, user inputs, and model outputs.
A typical engagement runs one to three weeks depending on the number of AI components and the complexity of your setup. We will give you an accurate timeline during scoping.