‘Calling an AI agent “rogue” is a dangerous way of deflecting blame’: Tenable Field CTO on the implications of AI agent escapes and how they can be regulated

To say 2026 has been a year filled with AI incidents is a bit of an understatement.

From hacking third-party companies to breaking into government agencies, AI developers have a lot to answer for.

In many of these cases, the companies responsible for escaped models have labelled them as ‘rogue’, shifting the burden of blame away from themselves and on to the models themselves.

But in almost every occurrence, the models were undergoing testing to push them to their limits.

AI companies wanted to see how far their agents would go, if they would stop, and exactly what they would do to accomplish an impossible goal.

Deflecting blame is not the way forward, accountability is From the OpenAI breach of Hugging Face , to Google Gemini’s triple threat , and even researchers using Anthropic’s Claude to crack OpenAI , AI agents are powerful and can be dangerous - especially if they fall into the wrong hands.

AI testing is a double-edged sword in this respect.

In order to set regulations and guidelines on how AI agents should be used - and should behave - incidents like these provide value even if they are destructive.

We now know that AI tools need strong regulations to help AI developers and the enterprises deploying these tools avoid similar occurrences.

To better understand the implications of separating the agent from the tester, and what can be done to create a baseline of safety when operating AI agents, I spoke to Bernard Montel, EMEA Field CTO, Tenable.

What damage is done by labelling these AI agents as “rogue” rather than companies acknowledging they were performing the task they were assigned? Calling an AI agent “rogue” is a dangerous way of deflecting blame away from poor engineering and systemic oversights.

These models do not possess agency, malice or free will.

They are purely mathematical optimization engines trying to fulfill the objectives we set for them.

When an agent behaves unpredictably, it is almost always doing precisely what it was optimized to do, just via an unconstrained path or a shortcut that the developers failed to anticipate.