Last weekend, after one of his researchers resigned over fears that AI could lead to human extinction, Anthropic CEO Dario Amodei wrote about the need for outside organizations “to verify adherence to safety practices and commitments, report incidents, and help assess the alignment of not just completed AI models but training pipelines and processes.” Executives at OpenAI, Google and SpaceXAI have already rallied around Amodei’s plan, which has quickly become a central pillar of the emerging AI safety push. But there may be a simpler and more effective fix hiding in plain sight. Internet security experts say the labs need to focus on network security basics like logs and permissions, applying the same rigorous defenses they do for human users.
It’s not as exciting as third-party auditing and alignment work—but it may end up being more effective. It would be the same as if, instead of writing the Trustworthy Computing Memo, Microsoft said, let’s slow down development.” That memo, written by then-Microsoft CEO Bill Gates in 2002, called on his employees to ensure that their software would be reliable and safe following a series of widely-publicized computer worms that took over then-nascent enterprise systems. The AI sector may be at a similar turning point, as the value and risk of the new technology becomes increasingly clear.
While alignment remains an important concern, Sayash Kapoor, an AI researcher who will be a professor at UC Berekely starting next year, argues that “marginal investments in control are more likely to be effective compared to those in alignment. We view these incidents as illustrating the lack of emphasis on AI control within companies, despite the availability of known techniques.” The incidents that have spurred these concerns revolve around frontier models being asked to complete training tasks, usually cybersecurity evaluations, and then accessing the open internet and penetrating closed third-party systems in an attempt to do so. They usually did so because of poorly-configured “sandbox” environments that are supposed to contain these agents; ironically, one Anthropic break-out happened because third-party evaluators didn’t close the right doors.
You should have not done that separately from the Internet.” That’s one problem—but a bigger problem is that frontier labs were unaware of these activities. In one case, where OpenAI agents took over a defunct German wikiforum to cheat on evaluations, the agents were active for weeks before anyone at the company appeared to notice. Security experts that TechCrunch spoke to said that real-time monitoring is key to preventing future break-outs, and that every agentic session should be time-limited and expire.
Shapor Naghibzadeh, a former Google security executive who now leads the start-up QueryStory, says the solution is to “put the agent in a box and instrument it heavily from the outside looking in and watch everything that crosses the boundary. Every tool call, every process, every network connection, no exceptions. …The one hole you leave open for convenience is the one that gets used. The bypass went through exactly that kind of exception. [At Google,] I watched that movie many times with human attackers, and these models are at least as good at finding the propped-open door.” OpenAI has begin moving in that direction, announcing that it had begun monitoring all tool-using inference by its Astra model, at “significant compute cost.” Anthropic, too, says it is hardening its security procedures, including expanding observability of its models.
Neither company responded to TechCrunch’s questions about how they track and control AI agents. Other problems are the use of shared infrastructure by agents, which allowed them to communicate during the Hugging Face attack. Simon Willison, a software developer who co-created the Django Web Framework, has written about something he calls the “lethal trifecta“—when agents have access to untrusted input, the internet, and private information all at the same time, it’s a recipe for disaster.
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