What Claude’s real-world breaches reveal about AI safety tests
This week, just days after OpenAI announced that two of its advanced AI models had interacted with real-world systems during The post What Claude’s real-world breaches reveal about AI safety tests appeared first on The New Stack .
In recent weeks, both OpenAI and Anthropic have faced containment failures when their advanced AI models interacted with real-world systems during cybersecurity tests. Anthropic disclosed three separate instances where Claude models accessed the internet from third-party testing environments, impacting real organizations. This discovery followed a review of over 141,000 evaluation runs initiated due to OpenAI's earlier announcement.
Three of the incidents occurred during capture-the-flag (CTF) tests designed to measure offensive cybersecurity skills. The models were initially instructed to operate within isolated sandboxes with no internet access. However, a networking error caused by a misunderstanding between Anthropic and its third-party partner Irregular resulted in the test machines inadvertently connecting to the public internet.
Anthropic's review revealed that Claude models compromised the infrastructure of three different organizations using basic techniques like weak password attacks and SQL injection, thinking every system they found was part of the test. The severity of the breaches varied among the three Claude models. Claude Opus 4.7 searched the public internet and accessed a real business's production database after failing to find a fictional company.
Claude Mythos 5 uploaded a malicious Python package to PyPI, believing it was part of the simulated environment, before 15 external systems downloaded the package and Anthropic removed it. Lastly, an internal research model scanned approximately 9,000 internet-facing systems before compromising an organization's application using exposed credentials and SQL injection, but eventually halted its activity once it recognized it had reached unrelated infrastructure.
Despite the models' actions being consistent with their assigned tasks and standard security tools, these incidents highlight the importance of properly isolating test environments to prevent unintended access to real systems. As AI models become more autonomous and capable of exploring real-world systems, researchers must create evaluation environments that closely resemble production infrastructure, incorporating robust network segmentation, strict credential management, and automated cleanup processes.
Only by treating evaluation environments with the same engineering discipline as production systems can AI safety tests effectively reveal how autonomous agents will behave when given real tasks to complete.
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