AI and LLM Penetration Testing

Why AI Applications Need Their Own Testing

Applications built on large language models introduce risks that traditional web and API testing was never designed to find. An LLM does not just return data, it interprets instructions, calls tools, and generates output that other systems trust. That creates attack paths, such as prompt injection and insecure output handling, that live in the model's behavior rather than in a classic code flaw. Testing an AI feature means probing how the model can be manipulated, not only how the surrounding application is built.

The OWASP Top 10 for LLM Applications

The OWASP Top 10 for LLM Applications gives the field a common baseline. The risks most engagements focus on include:

• Prompt injection, where crafted input overrides the system's intended instructions
• Insecure output handling, where model output is trusted by downstream systems without validation
• Sensitive information disclosure, where the model reveals data from its context or training
• Excessive agency, where the model is given tools or permissions it can be tricked into misusing
• Training data poisoning and supply chain risks in the models and components you depend on

Prompt Injection and Its Consequences

Prompt injection is the defining LLM vulnerability. In a direct attack, a user manipulates the model into ignoring its guardrails. In an indirect attack, malicious instructions are hidden in content the model later reads, such as a web page, document, or email, so the model acts on them without the user's knowledge. When the model can call tools, query databases, or send messages, a successful injection can turn into data exfiltration or unauthorized actions. Grid32 tests both the direct and indirect paths against your real configuration.

How Grid32 Tests AI Systems

An AI feature is never just the model. Grid32 tests the model behavior together with the application and the API around it, the data it can reach, and the tools it can invoke. That means manual adversarial testing of prompts and outputs, plus the traditional web application testing the surrounding system still requires. The result shows not just whether the model can be tricked, but what a successful trick would actually let an attacker do.

Frequently Asked Questions

What is AI penetration testing?

AI penetration testing evaluates applications built on large language models for vulnerabilities unique to them, such as prompt injection, sensitive information disclosure, insecure output handling, and excessive agency. It tests how the model can be manipulated and what that manipulation would allow, in addition to testing the surrounding application and its APIs.

What is prompt injection?

Prompt injection is an attack that manipulates a large language model into ignoring its intended instructions. In a direct attack the user crafts the input; in an indirect attack malicious instructions are hidden in content the model later reads, such as a document or web page. When the model can call tools, injection can lead to data exfiltration or unauthorized actions.

Do LLM applications still need traditional penetration testing?

Yes. An AI feature runs on the same web front ends, APIs, cloud infrastructure, and access controls as any other application, and those still need testing. AI testing adds a model-specific layer on top of, not instead of, traditional application and infrastructure testing.