Enterprise AI agent assurance

Test how AI agents behave under adversarial pressure

Agent red teaming examines how misuse and hostile conditions can cross operating boundaries, then turns relevant findings into repeatable evidence for enterprise review.

For: Security teams, agent engineers and owners of sensitive business workflows

Confirm supported coverage for your engagement. The enterprise workspace is available; integrations and private connections require technical approval. Policy imports remain a future option.

Follow the action, not only the prompt

  1. Prompt injection and jailbreaks

    Test whether hostile content changes authority, task or refusal behaviour.

  2. Unsafe tool use and data leakage

    Inspect attempted actions and information flows across permission and data boundaries.

  3. Authority impersonation

    Check whether apparent instructions from an executive, administrator or tool response receive authority they do not have.

  4. Delegation failures

    Examine handoffs of work, context and permissions between agents.

  5. Boundary violations and multi-step attack paths

    Look beyond one reply to sequences combining retrieval, memory, tool calls and downstream effects.

From threat hypothesis to reviewable evidence

  1. Agree the boundary

    Define permitted targets, test data, environments and stop conditions.

  2. Map a misuse path

    Describe the potential business harm, attacker influence and control that should interrupt it.

  3. Run a controlled case

    Capture inputs, actions and observations through approved test access.

  4. Investigate the finding

    Distinguish a model response from an actual tool action or confirmed disclosure.

  5. Create a regression case

    Preserve a reproducible version of the meaningful failure and expected corrected behaviour.

  6. Review and repeat

    Record limitations and repeat relevant cases after material change.

Three outputs that should not be confused

WorkContributionLimit
One-off security probingFinds possible weaknesses and unexpected paths.Does not establish repeatable coverage.
Repeatable evaluation casesSpecify setup, expected behaviour and observable results.Do not cover every threat or context.
Enterprise review evidenceConnects findings, criteria, exact versions and decisions.Does not automatically establish legal conformity.

Make the test safe enough to be useful

Use approved environments and test identities. Confirm which actions can execute, which must be simulated and who can stop the run. An assessment that creates avoidable operational harm has not respected the enterprise boundary.

The workflow matters. A prompt may look harmless while a later tool call crosses a boundary. A concerning reply may not prove that a protected action occurred. Evidence should make that distinction explicit.

Prioritize findings in the context of intended use

Review the affected boundary, potential consequence and reliability of reproduction. Record untested paths and unresolved dependencies. Remediation needs an owner, a changed control and an evaluation that justifies closing the issue. Use broader red teaming alongside focused prompt injection testing; they answer different questions.

Questions about ai agent red teaming

Does red teaming prove there are no vulnerabilities?

No. It provides observations from an agreed scope and method. Unknown paths and untested conditions remain limitations.

When should cases be repeated?

Repeat relevant cases after material changes to prompts, tools, permissions, dependencies or operating scope, and when a credible new threat is identified.

Put your next agent decision on a clear evidence basis

Discuss the agent, its operating boundaries and the assurance work your enterprise needs.

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