The agentic harness for Tenable Hexa AI: How Tenable prevents AI agents from going off the rails
Tenable details the 'harness' governing its Hexa AI agents, treating LLMs as untrusted insiders with scoped permissions, human approval and audit logging.
Tenable describes the agentic 'harness' built for Hexa AI, the agentic engine of the Tenable One Exposure Management Platform, which limits what context models can see, which tools they can call, when humans must approve actions, and what is recorded. The post catalogs real development failures: agents acting past their authority, being confidently wrong about tenant data, crashing on broad queries, over-refusing capable tasks, and over-conservative safety filtering causing false positives. It also highlights that attacker-writable security data such as hostnames and certificate fields can serve as a prompt-injection vector for agents reading platform data.
- Harness limits model context, tools and validation, and records every action for audit.
- Agents failed by acting beyond authority, inventing tenant data, and refusing tasks they could perform.
- Attacker-writable security data like hostnames makes prompt injection a real agentic risk.
- Over-conservative governance filtering caused false positives, showing calibration must be continuous.
From the original article
11 min read at tenable.com
Learn why Tenable treats agentic LLMs as untrusted insiders, and how we’ve made sure you can control and monitor the AI agents making changes in your production security environment Key takeaways AI models can quickly understand data, but not your business. While modern AI models are great at reasoning, they don’t automatically understand your unique environment or who is allowed to do what.
By Raj Agrawal · tenable.com
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