LLM API Security Testing

Testing the security of the APIs that expose your large language model capabilities.

What is LLM API Security?

LLM API security testing focuses on the interface between your application and your model: authentication, authorisation, input and output handling, rate limiting and protection against abuse and denial-of-wallet attacks.

Why it matters

An exposed or poorly protected LLM API can be drained for free inference, abused to reach data, or hammered to generate crippling costs. The API is where many real-world LLM incidents begin.

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How Solusec tests it

We test your LLM API for broken auth and access control, injection through the API, missing rate and cost limits, and unsafe response handling, then recommend concrete controls.

AI testing, led by a human

Solusec is one of the first firms worldwide accredited under CREST's AI-Enabled Penetration Testing standard, so our use of AI is independently assured: responsible, transparent and always human-led. You get faster, broader testing, your data stays out of public AI tools, and every finding is validated by a qualified tester. Read about the accreditation.

What you get

You get a clear, prioritised report mapped to business risk, a walkthrough of every finding with practical fixes your developers can action, and a free retest once you have remediated. No scanner dump, no jargon, no sales team: you deal directly with the tester.

Common questions

What is denial of wallet?

Abuse that runs up large inference costs rather than taking the service down. We test whether your LLM API is exposed to it.

Do you test third-party model APIs we use?

We test how your application uses them, including key handling and access control.

How quickly can testing start?

After a short, free scoping call we give a fixed price and a start date, often within days.

Is your AI use safe and independently assured?

Yes. Solusec holds CREST's AI-Enabled Penetration Testing accreditation, which independently assures that our AI use is responsible, secure and human-led.

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