Experts are pushing back on classifying AI escape incidents as "going rogue" because it risks obscuring the real security problems behind these events, they say.
The tech ecosystem has been inundated with stories of large language model (LLM) agents "going rogue," specifically referring to models breaking out of their sandboxes, harnesses, and other containments in some way, and causing trouble by interacting with and breaching third-party organizations. The incident that kicked off much of this discourse came in July, when OpenAI disclosed that two of its frontier models autonomously hacked AI model store Hugging Face during a security exercise. Other major firms, including Meta, Anthropic, and Google, soon disclosed their own AI escape incidents.
The response to these incidents has been far-reaching, well outside the boundaries of the security community. To some degree, this is no surprise: The idea of an LLM going LLM going rogue calls to mind images of The Terminator'sSkynet, where a hostile, sentient AI attempts to destroy humanity. And even large companies like OpenAI and Anthropic have advocated for greater government regulation for AI, with tech leaders across the spectrum warning of frontier AI's "existential threat." The mainstream discourse has become such that President Donald Trump and AI CEOs signed an AI safety pledge this week.
But is describing models as "going rogue" appropriate or helpful, particularly from a security standpoint? LLMs are software systems, not sentient actors capable of independently assuming responsibility for their behavior. AI can be helpful for a wide range of tasks in the enterprise, but when AI escapes its guardrails, it's often a situation where the boundaries set by the model's operators weren't properly tuned, sometimes on purpose. In the Hugging Face incident, for instance, OpenAI's model guardrails were deliberately dialed back for a benchmark test.