AI Penetration Testing

What is an AI penetration test?

An AI penetration test goes after applications that run on large language models: customer-facing chatbots, internal assistants that read company data, and agents that take actions through tools and APIs. We try to make the model do what it shouldn't. That means ignoring its instructions, leaking data, misusing its tools or reaching the systems behind it. We test against the OWASP Top 10 for LLM Applications, then follow wherever your application leads.

Why do AI applications need their own test?

A language model takes instructions and data through the same channel: text. That makes prompt injection a different kind of problem from the injection flaws your developers already know how to fix. An attacker doesn't need to touch your code. They just need to get text in front of the model, through a chat box, a document it summarizes, an email it reads or a web page it browses.

If that model can call tools, query databases or send messages, the attacker gets to borrow all of that access.

What do you test?

• Prompt injection. Direct attempts through the chat window, and indirect ones hidden in documents, emails or web pages the model processes.
• Data leakage. Whether the model reveals its system prompt, other users' data or content from connected knowledge bases that the current user shouldn't see.
• Tool and permission abuse. Whether we can steer an agent into calling tools, APIs or plugins in ways you never intended.
• Unsafe output handling. Whether model output flows into a database query, a browser or a command without proper handling.
• Retrieval access control. Whether a user can pull documents through the AI that they couldn't open directly.

Isn't our AI provider responsible for security?

Your provider secures the model. You own everything you build around it: the system prompt, the data you connect, the tools you hand the model and what your application does with its output. Most of the real risk lives in that layer, and that layer is exactly what we test.

Is this just jailbreaking the chatbot?

No. Getting a chatbot to say something embarrassing matters to some businesses, and we'll report it. But we focus on outcomes that actually hurt: data walking out the door, actions taken without authorization and access to systems beyond the model. AI features usually sit inside a regular web application with its own sign-in and access control, so we test that too. If the surrounding application needs a deeper look, a web application penetration test covers it.

What do you need from us?

Access to the application or API, accounts for each role and a description of what the model connects to: data sources, tools and integrations. Architecture diagrams help but are not necessary. We'll also ask what the system should never do under any circumstances, because that list becomes our list of goals.

How long does it take, and what do we get?

Most engagements run one to two weeks, depending on how many AI features, tools and integrations sit in scope. You get a report with every finding validated, the exact prompts or inputs that triggered it, the business impact and a specific fix, whether that means tightening a tool's permissions, filtering output or changing how the application handles retrieved content. We walk your developers through it.

Related services and reading

• Web Application Penetration Testing: for the application your AI features live inside.
• Cloud Penetration Testing: if your AI workloads run in Azure.
• How to prepare for a penetration test: a free checklist that walks your team through scope, timing and rules of engagement.