2026 has been an explosive year for problematic AI. As models have become more capable than ever, so too have the consequences when they are poorly secured. There’s something cartoonish in the talk of rogue agents escaping their containment, as if the miscreants were exceptionally capable iPad kids hurriedly exploiting their independence from parental oversight. It frames the security failure as one of insufficient model discipline, and the resolution as improved AI safety.

AI companies have invested heavily in AI alignment – careful adjustments to the model to induce refusal of harmful instructions and constrain model behavior. But relying on alignment is a dangerous game, as Anthropic discovered when a misconfigured sandbox gave its agents access to the internet and they breached a real company’s systems. Their incident report aptly acknowledges,

Several defense-in-depth measures, on both our side and our partner’s, could have prevented these incidents, or at least reduced their likelihood of occurring.

Google and OpenAI aren’t off the hook, either.

Proper sandboxing, security logging, anomaly detection: well-studied problems with good engineering behind them shouldn’t fall by the wayside. An architectural perspective on machine learning security is more important than ever – understand and secure the complete system, not just the model. What does securing the system mean when you’re testing a capable agent? Egress control, isolated environments, and logs that someone actually watches would be a good start.

At BIML, we are pleased to help shine a light on the security situation that the AI frontier labs find themselves in. We have been busy bringing clarity to the MLsec situation since 2019, so our perspective is not just informed by security engineering – it is informed by ML design itself.

What do we find? Lots of humans screwing things up…

https://www.csoonline.com/article/4234854/ai-agents-arent-going-rogue-frontier-labs-are-skipping-security-basics.html

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