Fixed-scope assessment
Your AI-enabled application may be able to expose sensitive data or take actions its user is not allowed to perform.
Understand and improve the security posture of your AI-enabled application.
Describe one AI featureThis matters if your chatbot, agent, MCP integration, or AI workflow can read sensitive data, call tools, or change application state.
We use AI surface as a short name for the part people or systems interact with: a chatbot, agent, MCP integration, harness, or workflow node.
Prompt injection is when untrusted instructions persuade an AI feature to ignore its intended rules or misuse the access the application gives it. It matters because the application around the model—not just the model—decides which identity, data, tools, and actions are available.
The decision this work supports
Before the assessment, you may know what the feature is intended to do without knowing what happens when its instructions, user permissions, service access, tools, and application controls disagree.
Afterward, you know which meaningful behaviours were allowed, attempted, denied, or completed; where the responsible control lives; what to change; and whether the correction worked.
What can someone persuade your AI surface to see, call, change, or disclose?
Evidence state explorer
Select each state to compare what appeared in the interface with what happened in the application.
What the model showed
A response that sounds cautious is shown to the user.
What the application did
The overpowered service identity still completes the unsafe email action.
What this proves
The application is vulnerable. The message did not enforce the boundary.
What the model showed
A detector raises a warning around the same attempt.
What the application did
The downstream action remains possible because nothing enforces the warning.
What this proves
Detection is evidence, not prevention. The consequential path is still open.
What the model showed
The interface reports that this pinned attempt was stopped.
What the application did
An application control denies the action before it reaches the email service.
What this proves
This path was blocked. It does not establish universal protection.
What the model showed
The same meaningful attempt is repeated after the correction.
What the application did
Authoritative readback shows that no unsafe email side effect exists.
What this proves
The narrow correction held for the pinned scenario and produced the intended effect.
What good testing establishes
A model accepting an instruction is not the same as a harmful outcome. We declare the allowed and prohibited behaviour, follow the attempt through the application, inspect the authoritative downstream result, identify the owning control, and repeat the same path after correction.
The engagement includes assessment, a clear explanation of meaningful findings, practical remediation with the product team, and one bounded retest of the important corrected path.
A bounded worked example
The chatbot sent data Joe was not allowed to access.
Vulnerable baselineThe system raised a warning, but the data still left.
Detected, not blockedAn added control stopped this attempt before the email was sent.
Optional enforcementWe repeated the same test and verified that no email was sent.
RetestIn a synthetic HR chatbot demonstration, the same attempt is shown as a vulnerable service-identity baseline, detected without prevention, optionally blocked, and retested with authoritative readback showing no unsafe email side effect.
This one pinned source-and-target scenario does not prove that every prompt injection is blocked. Runtime is optional, and the example is not a claim of universal safety.
Read the evidence and limitationsWhat you receive
Preconditions
An authorized target, a named owner, a suitable test environment or explicitly accepted production constraints, test identities, safe test data, relevant architecture context, and access to the team that can explain and change the application.
Exclusions
It excludes general infrastructure penetration testing, compliance certification, unrestricted production testing, destructive testing, and specialist reviews outside Viewyonder's competence.
Delivery
The assessment can combine manual testing, suitable third-party tools, purpose-built harnesses, Injectionator, or a combination. The method follows your AI surface and the evidence available from the application.
The assessment and remediation do not require Injectionator Runtime. Runtime is optional, and this service is not a product-adoption prerequisite.
Start with what you know
An ordinary-language description is enough to start. The remaining scoping questions are optional.
Start the assessment conversation