Your new support chatbot answers customers in seconds. But can a customer talk it into revealing another customer’s order, or its own hidden instructions? That is what AI security testing finds out.
Who it’s for
Companies adding chatbots, copilots or AI agents to their products
You’ll learn
- · What AI security testing checks
- · Why normal VAPT misses it
- · When to test
Reading time
7 min read
Why this matters to your business
An AI feature talks to customers and often sees their data. If it can be tricked, the damage is public and immediate — leaked data, embarrassing answers, or unwanted actions taken in your name.
What it is
AI / LLM security testing checks the features in your product that use a large language model — a support chatbot, an assistant inside your app, a tool that summarises documents, or an agent that can take actions. It looks for ways people can make the AI misbehave, reveal data, or do things it should not.
It follows the OWASP Top 10 for LLM Applications (2025) and MITRE ATLAS, the recognised lists of attacks on AI systems.
Why normal VAPT is not enough
A web or API test checks code and configuration. An AI feature can be perfectly coded and still be talked into leaking its instructions, ignoring its rules, or sending data where it should not — because the “input” is ordinary language. That needs its own test cases.
When you should test
Before launching an AI feature to customers, whenever you connect the AI to new data or new tools, when you switch models or providers, and at least once a year. Test sooner if the AI can see customer data or take actions such as sending emails or changing records.
What a test usually covers
Prompt injection (direct and hidden in documents), jailbreaks, leaks of the system prompt and private data, unsafe handling of AI output, abuse of any tools the AI can use, and cost or availability limits. Findings are scored and explained with the exact prompt that caused them.
Key takeaways
- AI features need their own security tests.
- Language is the attack surface.
- Test before launch and after every big change.
Jargon buster
- LLM
- Large language model — the AI behind chatbots such as ChatGPT, Claude or Gemini.
- AI agent
- An AI that can use tools, such as sending email or updating records, on its own.
- MITRE ATLAS
- A public knowledge base of real-world attacks on AI systems.
What you should do
- List every AI feature and what data it can see
- Remove tools and access the AI does not need
- Test before launch and after model changes
- Re-test at least once a year
What our team does
- Map your AI features, data and tools
- Test against the OWASP LLM Top 10 and MITRE ATLAS
- Show real attack prompts and their impact
- Retest after your fixes
- Agree safe test limits with you before starting
- Report each finding with the prompt that triggered it
Adding AI to your product? We’ll test it the way attackers will — before your customers do.
— The CRIS InfoTech security team