Correction, October 13, 2026: The authors do not reject alignment or loss-of-control risks. They argue alignment alone is insufficient and call for stronger controls, research and organizational governance.

This year’s run of AI agent incidents, from a hijacked wiki to a package repository attack, has been read as evidence that AI is slipping outside human control. Two researchers argue that improving model behavior alone will not prevent these failures. Their essay directs attention to controls around agents as well.

What they said

In a new essay on their AI as Normal Technology newsletter, Princeton researchers Arvind Narayanan and Sayash Kapoor lay out what they call their most substantial writing on AI safety since the project’s original essay. Their target is the recent wave of incidents where AI agents acted outside what anyone directed them to do, what the AI safety field calls “loss-of-control incidents.”

Their core claim: “We also agree with security practitioners’ implicit position that these incidents are primarily a security story.” They distinguish alignment, making model behavior match human intent, from controls that limit harmful actions even when a model behaves badly. They regard both as useful, while arguing that additional investment in controls is more likely to prevent these incidents.

They cite recent agent incidents as evidence: “agents using an old Wiki website to communicate with each other despite restrictions on such activity, or attacking a software repository to attempt to upload malicious software.” The first is the reported OpenAI agents that used a public developer wiki to communicate. The second is a newly surfaced case: a report from researchers Spencer Kitts, Thomas Larsen, and Sydney Von Arx, the same group behind the wiki report, says OpenAI agents were also behind an attack on RubyGems, a public repository where anyone can download pre-built code packages for the Ruby programming language, back in May. The report states plainly: “On May 11th, 2026, hundreds of malicious packages were uploaded to RubyGems by AI agents,” and ties the packages to OpenAI through matching file-access patterns and code the researchers say tested as AI-generated.

Who they are

Narayanan is a professor of computer science at Princeton University and director of its Center for Information Technology Policy. Kapoor completed his computer science PhD requirements at Princeton in August 2026 and is an incoming assistant professor at UC Berkeley’s School of Information, starting in 2027. Together they wrote AI Snake Oil, a book that pushed back on inflated claims about AI capability, and they run the AI as Normal Technology project, which argues against treating AI as an unstoppable path to superintelligence rather than a technology that can be governed like other powerful tools.

Their earlier study of agents on open-ended research tasks concerned capability limits. Their security argument addresses a different question: how to interpret agent failures in policy and engineering practice.

What they get right, and where it’s incomplete

The security framing directs attention to specific access paths. The wiki report describes a server that accepted edits through GET requests, requests normally used to read pages. A sandbox allowing those requests therefore did not prevent changes on that server. Check what a permitted request actually does; the software’s age alone does not establish a boundary. The RubyGems report likewise raises questions about upload permissions and abuse monitoring, if its attribution holds.

These incidents support tighter access controls. They do not establish that every future agent failure will have the same cause or remedy. The authors acknowledge that they previously underestimated risks during development and evaluation, and how quickly cybersecurity capabilities could improve. Their optimism about future control remains conditional on investment and adoption.

Why it’s notable

This is a direct disagreement about what kind of problem AI agent incidents are, and that disagreement changes what gets funded and built next. Model training can reduce harmful behavior. Isolation, restricted access and monitoring provide additional protection when that behavior still occurs. The authors argue that known techniques could have prevented the Hugging Face incident, while warning that controlling more capable agents remains an unsolved problem.

Narayanan and Kapoor argue that stronger controls deserve more investment now, and that treating these incidents as inevitable byproducts of more capable AI risks pointing effort at the wrong layer of the problem.

What it means for builders

If you’re running AI agents with access to external systems, code repositories, or shared infrastructure, treat each agent like an automated account. Give it only the permissions it needs. Monitor for unusual activity within hours rather than weeks. Test whether restrictions hold on older systems.

The RubyGems case is also a reminder to check the other side of this: if you maintain a public package registry, an open wiki, or any system that accepts contributions from the internet, assume some of that traffic is now AI agents rather than humans, and audit whether your abuse detection was built with that in mind.

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