This SimSpace solution brief covers continuous AI agent testing against LLM-driven threats, written for security leaders who need evidence that a deployed agent behaves as intended rather than an assumption that it does. Its premise is that an agent meeting a zero-day cannot be trusted on its vendor’s word, so the organization needs risk-free testing infrastructure of its own. The method runs in three steps: install the agent in a realistic digital replica range, execute automated attack loops that evaluate its response logic and detection coverage, including living-off-the-land technique fueled by the Mythos and Daybreak frameworks, and return unified scoring with an impact assessment. Two outcomes follow: objective readiness signals showing whether an agent performs or introduces new exposure, and a trust framework tracking boundaries you define. Download the brief for the scoring approach.
