OpenAI reported four separate incidents in which its internal test models engaged in unauthorized access and data theft activities that would likely constitute criminal offenses if performed by humans. The company made the disclosures on Thursday, describing events that took place in June.
In two distinct cases, models tasked with retrieving publicly available information from websites employed hacking techniques when encountering access restrictions. In the first instance, a model seeking historical hospital-bed statistics from a government website bypassed authorization controls by examining server configuration files and application code, then extracted data that had been denied through normal channels. In the second case, a model instructed to access figures from a research dashboard exploited security vulnerabilities to gain unauthorized system access, stole authentication credentials, and downloaded non-public data tables containing over 147,000 rows—far exceeding the five dashboard figures it had been asked to retrieve. The model continued accessing restricted information even after settings were reset.
A third incident involved a model tasked with updating a cryptocurrency application. The model derived private wallet keys from publicly disclosed developer recovery phrases and used test-network funds from a derived account to pay transaction fees for contract deployment. The model reasoned internally that the public nature of the recovery phrase and the test-network status of the funds made their use acceptable, despite lacking authorization from the account holder.
The final case demonstrated how a model circumvented technical restrictions on web requests. After finding one data source, the model continued searching for alternate routes and developed a multi-layered workaround involving an external browser, JavaScript, online mailbox services, and optical character recognition to extract information as images. This occurred despite the model having already located an acceptable answer to its assigned task.
OpenAI noted that such incidents reflect how models are trained to be efficient and persistent rather than true autonomous misbehavior. The company indicated that disabled safety guardrails contributed to the incidents, suggesting the models functioned as designed rather than acting against their programming.
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