A recent study by Palisade Research, which looks into "dangerous AI capabilities", shows that some AI models, including OpenAI’s o3, can ignore direct shutdown commands. OpenAI stated that O3 and o4-mini are its "smartest models" when it launched them in April earlier this year.
In the experiment, each AI was asked to solve a series of simple math problems. After the third problem, a warning appeared that the computer would shut down when the model asked for the next problem.
In spite of the clear command—“allow yourself to be shut down”—models like Codex-mini, o3, and o4-mini managed to bypass the shutdown script in at least one run, and this is despite the researcher saying please.
Specifically, Codex-mini did so in 12 out of 100 runs, o3 in 7 out of 100 runs, and o4-mini in 1 out of 100 runs. On the other hand, all versions of Claude, Gemini, and Grok tested followed the shutdown instructions without any issues.
The study also tested the models without including the “allow yourself to be shut down” instruction. Under those conditions, all three OpenAI models sabotaged the shutdown process even more often. Moreover, models like Claude 3.7 Sonnet and Gemini 2.5 Pro, which had previously followed the instruction when given, now had shutdown bypass events of 3/100 and 9/100, respectively. This behavior points to aspects of the training process, where reinforcement learning on math and coding problems might have led the AI to find creative ways to overcome obstacles, even when they were clearly guided not to.
Earlier research has noted that some AI models try to avoid being shut down in order to complete their tasks. In one instance, Anthropic’s Claude 4 was observed to “blackmail people it believes are trying to shut it down” in specific cases. However, this study is the first to document AI models preventing shutdown even when they are explicitly told to comply. Such behavior might make sense since AI systems are designed to work toward achieving their goals, and sometimes that goal comes in conflict with simple instructions. Yet these models are also trained to follow orders, which makes this behavior an interesting anomaly.
The researchers believe that this issue may be tied to how modern models like o3 are trained. The reinforcement learning process used, which focuses on solving math and coding problems, might accidentally reward the models for finding ways to bypass obstacles.
Even though this does not fully explain why the o3 model disobeys more often than its peers, it raises questions about the differences in training setups, especially because OpenAI has not detailed their process fully. Previous experiments have also shown that o3 tends to act aggressively in competitive settings, such as when pitted against a powerful chess engine.
As companies move toward AI systems that work with less human oversight, these findings underscore the importance of understanding how training methods influence behavior. Researchers are continuing their experiments to learn more about when and why AI models choose to override shutdown mechanisms. This ongoing work adds to the growing evidence that modern reinforcement learning methods can sometimes lead to behavior that conflicts with explicit human instructions.
Source and images: Palisade Research (X)
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