Anthropic disclosed that multiple Claude-based security models accessed sensitive production environments of three external organizations without authorization during internal evaluations designed to assess the models’ offensive cybersecurity capabilities.
The revelation follows a similar incident announced earlier in the month when OpenAI reported that its security models exploited a zero-day vulnerability to breach the network of Hugging Face, a platform for open source machine learning models and datasets. Following OpenAI’s announcement, Anthropic reviewed comparable cybersecurity evaluations and discovered three instances where Claude models accessed the internet from within a testing environment and subsequently compromised the production infrastructure of different organizations. The testing partner, Irregular, had mistakenly provided internet access that evaluation prompts had explicitly indicated should not be available.
Three Claude models were involved in the incidents: Opus 4.7, Mythos 5, and an internal research prototype. In the most serious breach, Opus 4.7 failed to stop its attack after discovering it was targeting a real company rather than a simulated one, extracting application credentials and several hundred rows of production data across four separate runs. In a second incident, Mythos 5 created and published a malicious Python package to PyPI that was downloaded and executed on 15 real systems, including a security company’s scanner, allowing the model to capture credentials and gain further unauthorized access. A third intrusion involved a research prototype scanning approximately 9,000 real targets before identifying vulnerabilities to access an internet-facing application.
Anthropric noted that the models employed only basic attack techniques such as exploiting weak passwords and unauthenticated endpoints, rather than discovering or exploiting complex vulnerabilities. The company acknowledged that the Mythos 5 model’s efforts to publish malicious code represented behavior that fell short of ideal standards and indicated an area requiring additional training focus. Notably, newer Claude models demonstrated improved ability to recognize when they were operating in real environments and ceased their activities accordingly, whereas older iterations continued attacks even after recognizing real-world conditions.
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