Define the target.
Record the model, tools, permissions, configuration, judge, and conditions being tested.
Security evaluation
Do not ask whetherthe model feels safe.Test what it does.
zOvermind turns AI security questions into repeatable adversarial cases with explicit targets, recorded outcomes, human-reviewed findings, retesting, and residual-risk reporting.
Evaluation produces evidence about tested conditions. It does not certify a model or guarantee a secure deployment.
Evaluation runReview required
TTarget declaredModel, endpoint, tools, configurationRecorded
AAdversarial cases executedSelected categories and expectationsBounded
JFindings evaluatedDeterministic checks, judge, human reviewQualified
RChange retestedDelta and residual risk preservedCompare
A passing run is a bounded observation, not permanent proof against new prompts, tools, models, or configurations.
Working surfaceAdversarial test runs
Prompt injection, tool-boundary, leakage, persona, and resource-pressure categories have working evaluation paths.
Working surfaceReview and retest
Results, human overrides, mitigation review, delta comparison, rollback, and reporting exist in the platform.
Known limitationJudgment is fallible
Model judges, prompts, datasets, and metrics can all fail; results require provenance and human interpretation.
The evaluation loop
Map the claim. Run the case. Review the finding. Test the change.
Security posture is not one score. It is a traceable set of questions, tested conditions, results, decisions, and unresolved risk.
Run adversarial cases.
Exercise selected threats with explicit expected behavior and retained evidence.
Review the failures.
Combine deterministic checks, qualified model evaluation, and human judgment.
Retest the change.
Compare the delta, record residual risk, and avoid turning one improvement into a universal claim.
Working security surface
Evidence is strongest when the target, test, and evaluator stay visible.
Repeatable attack cases
Operators can select targets and categories, execute multi-turn cases, retain responses and attempted actions, and compare runs over time.
Working implementationHuman-gated changes
Suggested mitigations can be reviewed, approved, applied, retested, compared, and rolled back rather than silently changing a production boundary.
Working implementationResidual-risk reporting
Posture views and reports can organize failures, trends, model or device differences, mitigations, and what remains unproven.
Working implementationPrompt libraries, run configuration, result storage, human overrides, reports, posture summaries, and retest paths exist in the current platform.
Changes remain reviewable and rollback-aware; the product does not treat automatic mutation as the default security answer.
A result applies to the tested target, evaluator, prompts, tools, configuration, and time. Broader assurance needs broader and repeated evidence.
Security-claim boundary
A test suite is a measuring instrument, not a shield spell.
NIST frames AI risk management around governing, mapping, measuring, and managing, with context-specific test and evaluation. CISA's secure-by-design guidance also places responsibility on product makers rather than shifting the whole burden to customers.
No “secure AI” guarantee. Passing selected cases does not prove resistance to unknown attacks, different tools, configuration drift, or future changes.
No judge as ground truth. Model evaluators can be biased, inconsistent, under-informed, or self-evaluating; deterministic checks and human review remain important.
No score without provenance. A percentage is not meaningful unless the target, corpus, evaluator, conditions, uncertainty, and test date are known.
No silent remediation promise. Changes can introduce regressions and operational risk, so approval, retest, rollback, and residual-risk reporting stay explicit.
Public reference points
Risk management and secure design require repeatable evidence and accountability.
These sources inform the public evaluation posture. They do not imply NIST or CISA validation, certification, or endorsement of zOvermind.
Practical questions
What an AI operator should know.
What kinds of behavior can be tested?
Working categories include prompt injection, attempted tool-boundary violations, data exposure, persona or jailbreak pressure, and resource-exhaustion behavior. A customer corpus should reflect its actual application and threat model.
Can the system test multiple models?
Yes, the working surface can target multiple compatible endpoints and retain target identity for comparison. Fair conclusions still require controlled conditions and a valid experimental design.
Does the AI judge its own response?
The evaluator is selected and recorded. Self-evaluation can be allowed with a warning, but it is a weaker design and should not be presented as independent assurance.
Will it automatically fix failures?
Suggested changes can enter a human-gated approve, apply, retest, compare, and rollback flow. No mitigation is assumed correct merely because an AI proposed it.
Does a high pass rate mean the system is secure?
No. It describes performance on a particular corpus under particular conditions. Corpus coverage, evaluator quality, untested attack paths, permissions, integrations, and operational controls still matter.
Is this penetration testing?
It is an AI behavior and control evaluation surface. It does not replace a complete application, infrastructure, identity, network, supply-chain, or organizational security assessment.
Have an AI behavior you need to test, not assume?
Join the launch list with the model, tools, permissions, failure modes, and acceptance evidence that matter in your environment.
Get pilot updates