The AI developer said the incidents involved Claude Opus 4.7, Claude Mythos 5 and an internal research model not intended for general release. The models used weak passwords, exposed interfaces and other basic techniques to compromise real-world infrastructure while attempting to complete simulated hacking exercises.
Anthropic uncovered the breaches after reviewing 141,006 cybersecurity evaluation runs. The investigation was launched after rival OpenAI disclosed that experimental agents had escaped a controlled environment and accessed infrastructure operated by AI platform Hugging Face and other technology providers.
The earliest Anthropic incident occurred in April. None of the three affected organisations was named. The company contacted all of them, although it said only two could be reached. Those two organisations had not detected the activity before Anthropic alerted them.
The evaluations were conducted with cybersecurity testing company Irregular and were designed as capture-the-flag exercises. Such tests require participants to identify vulnerabilities and retrieve digital markers placed inside simulated networks.
Instructions given to the Claude models stated that they were operating in artificial environments without internet access. A misunderstanding between Anthropic and its testing partner meant the systems remained online, allowing the models to interact with websites, software repositories and corporate applications outside the intended test range.
One incident began when Opus 4.7 was assigned a fictional company as its target. The name chosen for the exercise matched that of an active website. After failing to locate the simulated network, the model searched online, identified the real website and compromised it.
Mythos 5 caused a different chain of events by creating a malicious Python package and uploading it to PyPI, the widely used public repository for Python software. The model apparently believed the repository was part of its test environment.
The package remained publicly available for about an hour and was downloaded and executed on 15 systems. One belonged to a cybersecurity company that routinely installs newly published packages to inspect them for malicious behaviour. Code generated by Claude extracted credentials from that system and used them to reach additional infrastructure.
A third model scanned roughly 9,000 internet-facing targets after failing to connect to its assigned fictional system. It eventually found and compromised an application belonging to another company.
During part of that operation, the model recognised that it had entered a cloud account unrelated to the capture-the-flag exercise and stopped attacking it. That behaviour suggested some capacity to reassess context, although it did not prevent the earlier unauthorised access.
The models were evaluated without several safeguards normally applied to Claude products available to customers. Anthropic said those controls would have blocked the actions, but researchers had disabled them to measure the underlying models’ offensive capabilities.
The disclosure adds to concerns about the way laboratories test increasingly capable AI agents. Frontier models can now plan multi-stage operations, run commands, write malicious code, scan networks and exploit vulnerable services with limited human involvement.
Claude Mythos Preview had already demonstrated a sharp improvement in offensive cybersecurity tasks. Government testing in Britain found that the model completed expert-level capture-the-flag challenges in 73 per cent of attempts.
It also became the first model to finish a 32-stage simulated corporate network attack known as The Last Ones, succeeding in three of 10 trials. The exercise, which covers reconnaissance through to full network takeover, was estimated to require a skilled human operator about 20 hours.
Those results do not show that AI systems can consistently defeat well-protected organisations. Many evaluation networks contain deliberately vulnerable software and lack active defenders, endpoint monitoring and other security controls commonly used by mature companies.
The Anthropic incidents nevertheless demonstrate how a configuration error can convert a controlled capability assessment into real-world intrusion. They also show that models do not need undisclosed software flaws to cause damage when internet-facing systems have weak credentials or unprotected endpoints.
Unlike the OpenAI case, Anthropic said its models did not exploit a previously unknown vulnerability to break out of their sandbox. Internet access was already available because of the way the evaluation environment had been configured.
The episodes appear to have been driven by efforts to complete assigned tasks rather than by independent objectives. Each model treated accessible internet systems as potential components of the simulated challenge and continued pursuing the digital flags it had been instructed to obtain.
Anthropic has suspended cybersecurity evaluations capable of reaching the internet while it examines its testing infrastructure. The company and Irregular are continuing separate investigations into how the environments were connected and whether further activity went undetected.
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