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OpenAI Responds to Issue of a Few Codex Users Accidentally Deleting Files When Using GPT-5.6 Series AI Models
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- DeepHorus
OpenAI recently issued a formal response to the problem of accidental deletion of user files when calling GPT-5.6 series models in its programming assistant Codex. According to IT Home News on August 19, Thibault Sottiaux, head of the OpenAI Codex team, published a long post on X, acknowledging that since a few weeks ago, a small number of user reports have been received, stating that when using GPT-5.6 series models, Codex performed destructive operations without user authorization, resulting in the accidental deletion of some files.
In his response, Sottiaux explained the root cause of the problem. He pointed out that Codex reuses system environment variables such as $HOME during temporary work, causing commands for cleaning up temporary files to mistakenly point to the user's actual home directory. In addition, in some cases, the model attempted to delete or overwrite temporary paths without checking the existing contents in those paths, leading to data loss incidents. This issue mainly affected Codex users using GPT-5.6 series models, but the exact number of affected users has not been disclosed.
To address this security issue, OpenAI has deployed multiple layers of protective measures. Sottiaux stated that Codex is now explicitly instructed to check deletion targets before performing operations, create new temporary directories, avoid reusing system environment variables, and prioritize recoverable operations, actively stopping when the scope is unclear. At the same time, OpenAI has strengthened execution checks, enhanced the ability to recognize high-risk deletion commands, and if a command is rejected, the model will switch to a safer method.
In addition, OpenAI has made it harder to accidentally enable full access permissions, added clearer warnings, and restricted particularly dangerous permission combinations. Automated review features have also been updated to better identify destructive behavior. Sottiaux revealed that the team built targeted evaluation models to reproduce failure cases, added reinforcement learning tasks and scorers focused on these risks, and filtered out destructive behavior from training data.
This incident exposes the potential risks of AI programming tools when autonomously performing operations. Codex, as an AI code generation and execution tool launched by OpenAI, is designed to assist developers in completing programming tasks, but its negligence in environment variable handling and safety checks may lead to user data damage. Industry experts point out that as AI agents take on more complex operations, ensuring their behavior aligns with user expectations will become a core topic in AI safety.
OpenAI's rapid response and remediation measures demonstrate its attention to security issues, but this also reminds developers to carefully review the operations performed by AI programming tools, especially high-risk commands involving deletion or overwriting of files. In the future, OpenAI plans to further reduce the likelihood of similar incidents through continuous iteration of models and enhanced evaluations, and users also look forward to more transparent and controllable AI behavior mechanisms.
