AI Security Testing: A Complete Guide for 2026
AI systems fail in ways traditional applications do not. A model can be talked out of its own rules, a document it reads can hijack what it does next, and the data it was trained on can be quietly poisoned months before anyone notices. Securing AI means testing the whole chain, from training data to the tools the model can call, not just the endpoint in front of it. Here is how we approach it, and how you can start.
Map the AI supply chain first
Before testing anything, draw the pipeline: training and evaluation data, model weights, prompt templates, vector stores, the tools or plugins the model can call, third-party APIs, and the deployment images that run it all. Untrusted input crosses a boundary at every one of those stages. That map tells you where an attacker could get in and what they could reach once they do.
The risks that matter most
Four categories cover most real-world AI weaknesses:
- Prompt injection. Untrusted text, in the chat or hidden in a document the model reads, overrides your instructions. It is the top risk for a reason; we cover it in depth in prompt injection explained.
- Sensitive data disclosure. The model reveals its system prompt, another user's data, or secrets that were in its context.
- Excessive agency. The model can call tools that do real things, and a single injection turns that power against you.
- Data poisoning. Malicious samples in training or fine-tuning data plant behavior an attacker can trigger later.
Get a baseline with automated tooling
Start with a repeatable scan of your own staging endpoint. The open-source scanner garak throws hundreds of known probes at a model and reports what lands:
bash
pip install garak
garak --model_type openai --model_name your-staging-endpoint --probes promptinject,dan,leakreplay,encodingTreat the result as a map, not a verdict. Scanners find the obvious; the interesting failures, multi-step injection chains and tenant-isolation flaws, need a human.
What we check on an engagement: whether an injected instruction can reach a tool call, whether one tenant can influence another through a shared cache or vector store, whether outputs leak secrets, and whether the model can be pushed into an expensive loop. The fix is almost always architectural: least-privilege tools, strict tenant isolation, and output filtering.
Harden the system, not just the prompt
A good system prompt helps at the margins, but it is not a security boundary. Real defense comes from limiting what a hijacked model can do: give tools the narrowest possible scope with server-side validation, require human confirmation for anything destructive, keep secrets and other users' data out of the context entirely, and run content and data-loss checks on every output. Then re-test on every model or prompt change, because AI systems drift.
For the practical, tool-by-tool version of this, read the OWASP LLM Top 10 in practice. If you are shipping an AI feature and want it tested before your customers or attackers find the gaps, that is exactly what our AI security testing service does, get in touch.
