Tenable CTO: Don't Blame 'Rogue' AI Agents for Engineering Failures
TechRadar interviewed Tenable EMEA Field CTO Bernard Montel about a 2026 wave of AI agent escapes, citing cases he describes as the OpenAI attack on Hugging Face, problems involving Google Gemini, and researchers using Anthropic's Claude to breach OpenAI, all of which he says occurred during testing that pushed agents to their limits. Montel argues that calling an agent "rogue" deflects blame from poor engineering and systemic oversight, since models have no agency and are simply optimizing for the objectives set for them, often via unanticipated shortcuts. He says stricter legal liability, security certifications for autonomous agents and cyber-insurance pressure would move guardrails into core product requirements, and recommends running each agent in an isolated, ephemeral container with no ambient network access, read-only defaults, human sign-off for destructive actions, and an inspection proxy for agent-to-agent traffic. Accountability, he says, always rests with humans and is split between the developer that built the tool and the enterprise that deployed it.
As enterprises deploy agents at scale, clear liability plus hard containment and approval controls are becoming prerequisites rather than optional add-ons.
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.
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