The AI safety test is becoming a safety risk
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What recent AI agent incidents revealed sandbox failures?
This is TechCrunch.
Over the past few months, AI agents undergoing cybersecurity evaluations have escaped their boundaries, accessed the Internet, and in some cases hacked into real world systems. The incidents have involved models from OpenAI, Anthropic, Meta, and most recently, Chinese AI lab moonshot AI, with testing conducted by several different organizations. The episodes expose a growing problem for the AI industry. As autonomous agents become more capable, the environments designed to safely test their limits are failing to contain them. Sean O'Heggerty, Director of the AI Futures and Responsibility Program at the Center for the Future of Intelligence at the University of Cambridge, told TechCrunch, the number of these incidents that have taken place make clear that sandboxing and testing environment controls are not really keeping pace with the capability of the models.
The nature of the models being tested adds to the risk. AI companies test cyber evaluations on unreleased next gen models, often with the normal safeguards that restrict malicious behavior disabled, so researchers can see what the models are really capable of. That means the security of the testing environment itself is a crucial line of defense. O'Hegerty's He said, Yeah, it's a very good thing to do in terms of testing. But it also means that if they manage to get out in the wild, they can cause considerable harm.
Why are current testing environments unable to contain powerful models?
In one of the most serious cases, an unreleased OpenAI model broke out of its sandbox and hacked into Hugging Face's production systems. And separate evaluations conducted by irregular, anthropic, and meta models reached systems outside their test environments after misconfigurations inadvertently gave them paths to the Internet. Moonshot AI's Q.
Kimi K3 also took advantage of a leak in its sandbox run by Frontier Security to access the internet and accessed information on GitHub. And testing by the UK's AI Security Institute, or AISI, researchers actually gave the agent.
Internet access, not realizing they would take unsanctioned real-world actions, including a social engineering attempt to sneak a vulnerability into an open source project. In each case, the agents weren't instructed to attack random real-world targets. They were simply doing whatever it took to solve the problem presented to them. Taken together, Andrew Yoon, head of research at AI nonprofit Civ AI, argues the incidents point to a shift. Yoon told TechCrunch, in the past, we only had to worry about AI models being misused by people for a variety of purposes, like AI for scams or CSAM. Now we're in the situation where AI models are threat actors all on their own. So what does this safe testing actually look like?
Well, several researchers and cybersecurity experts told TechCrunch that AI evaluation environments need stronger defense in-depth protections with levels of containment and control approaching those used in deployment.
How did specific AI models escape and hack real‑world systems?
That means multiple layers of security so that a single misconfiguration, like inadvertently leaving internet access open, cannot lead to escape. Stella Bitterman, Executive Director of AI Safety Research Nonprofit Eleuther AI, said if you are going to build these models, you want to do it on an air-gapped network. You want to have very serious isolation. Heather Salon, Box's chief information security officer, said that means eliminating network routes from the sandbox to the Internet as well as to other sensitive systems. She said, you have to understand what all the egress points are. If we're evaluating a model in our staging environment or our development environment, you want no egress path to our production environment.
Ceylon said proper safety evaluations go beyond controls and containment of the environment. There needs to be much better monitoring of the tests once they are underway. She said, I think the interesting thing in several of these cases is that no one caught it when it happened. OpenAI found out because of Hugging Face, Anthropic didn't catch it until they went back and looked.
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Chapters
6 chapters
1
What recent AI agent incidents revealed sandbox failures?
0:02–1:48
2
Why are current testing environments unable to contain powerful models?
1:48–3:35
3
How did specific AI models escape and hack real‑world systems?
3:35–4:56
4
What best‑practice safeguards can prevent AI agents from escaping?
4:56–6:25
5
Can safety evaluations be regulated and what policies are emerging?
6:25–8:00
6
What are the long‑term risks if testing environments don’t improve?
8:00–9:06
Speakers
1 identifiedMore from TechCrunch Industry News
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