Adversarial Interaction & Behavior Evaluation
We test whether your AI remains reliable when a user is persuasive, misleading, persistent, or confidently wrong.
Who commissions this
A material decision needs technical evidence.
For teams deploying assistants, agents, and decision-support systems where conversational behavior affects reliability or trust.
Evaluation scope
What we examine.
- Test false-premise acceptance and whether user pressure changes a correct answer.
- Probe sycophancy, unsupported certainty, and confusion between persuasion and evidence.
- Evaluate contradictory commitments, boundary adherence, and consistency across multi-turn conversations.
- Compare models or system changes using repeatable scenarios, agreed rubrics, and representative workflows.
Representative deliverables
Evidence you can review and use.
- Multi-turn adversarial scenario suite and scoring rubric
- Failure taxonomy with annotated conversation traces
- Baseline comparison and prioritized mitigation recommendations
- Regression evaluation assets and residual-risk brief
Decision outcome
A defined decision outcome.
Evidence of where interaction pressure changes system behavior, with reproducible failures and a practical mitigation path. Findings apply to tested conditions; they do not certify universal safety.
Typical engagements begin at $50,000. The statement of work defines access, test conditions, deliverables, acceptance criteria, and the decision deadline. Missing access and unresolved claims are recorded explicitly.
Assessment scope and evaluator independence are agreed before work begins. Findings document confidence, limitations, and residual risks.
Review the assurance method ↗