What is AI application security testing?
AI application security testing is an authorized attack on the AI features you build or deploy, such as chatbots, copilots, retrieval-augmented generation (RAG) systems and AI agents, to find how they could be manipulated into leaking data or taking actions they should not.
AI features fail in ways ordinary applications do not. A language model follows instructions it finds anywhere in its input, including a sentence hidden in a document or web page it was asked to summarize. A retrieval system can hand one user another user’s files. An agent with access to tools can be persuaded to use them.
AI application security testing looks for those failures before someone else does. We test the application around the model as well as the model’s behaviour: the retrieval layer, the permissions agents hold, and where the model’s output goes next. Testing is done the way an attacker would approach it, and findings are confirmed by hand.
The result is a report your developers can act on, and evidence your AI controls work, whether for a customer, an auditor or an ISO/IEC 42001 management system.
Who needs it?
- A company that has built a customer-facing chatbot or AI assistant
- A team connecting a language model to internal documents through RAG
- An organization giving AI agents access to tools, APIs or business systems
- A business building plugins or extensions for Microsoft Copilot or another AI platform
What problems does it address?
- Prompt injection, typed directly or hidden in documents, emails and web pages the AI reads
- A RAG system that returns documents the user should not be able to see
- An agent that can be talked into calling a tool or API it should not
- System prompts, keys or customer data exposed through the model's answers
What we test
- Prompt injection, direct and indirect
- Sensitive information disclosure and system prompt leakage
- Retrieval and RAG access control, including leakage between users
- Excessive agency in the tools, plugins and APIs agents can call
- Unsafe handling of model output passed to other systems
- Jailbreaks and guardrail bypass
- Abuse that drives up cost or degrades the service
Methodology
Testing is mapped to the OWASP Top 10 for LLM Applications and MITRE ATLAS. Findings are confirmed by hand, with the prompts and responses that reproduce them, so the report shows what an attacker can actually achieve rather than a list of theoretical model weaknesses.
What you receive
- An executive summary leadership can read quickly
- Technical findings with the prompts and responses that reproduce them
- A prioritized remediation plan for the application, the retrieval layer and agent permissions
What happens afterward?
You get a prioritized remediation plan. Avertix can guide your developers through the fixes and retest the findings. If you also need a management system around your AI, the ISO/IEC 42001 work in AI Governance & Assurance builds on these results.
How the engagement works
Free consultation
A 45-minute call with a senior consultant about your situation. It is a conversation, not an assessment. You leave with a recommended next step.
Scope and rules
Scope, constraints and price are agreed in writing before any work starts.
The work
Carried out by the senior consultant who scoped it.
Report and walkthrough
Findings ranked by business risk, with a walkthrough for the people who will act on them.
