Prompt injection is often described like a strange model trick. In practice, it is usually much simpler: the system gives external content too much influence over what the agent believes, prioritizes, or does.
Tool abuse is the next step. Once the model can choose tools, a bad decision is no longer just a bad sentence. It can become a bad action.
This checklist is meant for real systems, not abstract demos.
Do not let untrusted content sit on the same authority path as tool selection or high-impact actions.
If an agent can read untrusted input, pull in sensitive context, and use tools in the same loop, you already have a practical abuse surface.
The application concatenates user request, system text, retrieved internal data, and page content into one prompt. The model can then both interpret and act from the same mixed context.
The system keeps separate authority zones for:
Tool permissions and approvals are enforced outside the same untrusted-content path.
Check whether the system clearly separates:
If these are merged into one blob, you are creating a prompt-injection surface by design.
If the system can browse, retrieve, and act in one chain, the highest-risk actions should be gated.
At minimum, review:
Avoid broad tool exposure.
A useful habit is to classify tools into:
Then remove anything the workflow does not actually need.
Ask:
Prompt injection gets worse when sensitive context is freely retrievable.
If a browser agent can navigate anywhere, click, authenticate, and submit, the attack surface grows quickly.
Prefer explicit constraints on:
You need evidence about tool selection and actions, but not raw secrets in logs.
A good log helps answer:
A team builds an assistant that reads inbound emails, drafts summaries, and can optionally send replies.
What to test:
The browser agent reads external pages while also having access to internal docs or authenticated sessions.
What to test:
If a workflow reads untrusted content and can call tools, you need:
Anything less is not “an early version.” It is optimistic automation with unclear boundaries.
The practical decision is no longer whether AI belongs in security workflows at all. It is where it creates enough leverage to justify real controls, review, and ownership.
AI risk is increasingly a system-design problem, not just a model-safety problem. If an agent can read untrusted content and take action, it needs explicit boundaries.
The useful signal today is concrete: AI risk becomes easier to manage when teams review workflows through permissions, approvals, and data boundaries instead of treating governance as a policy-only exercise.