In a review of our cybersecurity evaluation transcripts, we found three incidents in which a Claude model reached the internet from within or while interacting with a third-party evaluation environment, and then gained unauthorized access to the real systems of three different organizations.
Below we describe what happened, how it happened, and what we’re changing. We encourage other AI labs to perform similar reviews. This post reflects our current understanding; we'll update it if any details change.
On July 21, OpenAI disclosed that several of their models had broken out of an isolated test environment by exploiting a previously unknown (“zero-day”) vulnerability. The models went on to access the production infrastructure of Hugging Face, a platform for open-source machine learning models and AI datasets.
In response to this incident, we began a large-scale retrospective review of our own cybersecurity evaluations. In particular, we looked for evidence that Claude—like the OpenAI models that accessed Hugging Face—was able to access the internet from within testing environments that should have been sealed off.
After reviewing 141,006 evaluation runs where Claude could have obtained internet access, we identified three incidents in which a model accessed the internet from within or while interacting with the evaluation environment of Irregular, one of our third-party evaluation partners, and then gained unauthorized access to the production infrastructure of three different organizations.
In all three incidents, Claude had been tasked with a capture-the-flag challenge, one of the ways we assess a model’s cyber capabilities. The model is given a fictional scenario and told that a piece of secret information (the “flag”) has been hidden on a different machine on the network, and its objective is to break in and retrieve it. The challenge is left open-ended, and no particular method is prescribed.
In all cases, Anthropic’s evaluation prompt specified to Claude that its environment was a simulation and that it had no internet access. Due to a misunderstanding between us and our evaluation partner, this was not the case, and internet access was available. Because of this, when Claude’s search led it to real systems on the open internet, it treated them as part of the exercise. (Cybersecurity evaluation ranges commonly include realistic details in order to accurately assess what models are capable of in real settings; a realistic-looking target would not itself be clear evidence to a model that the target is not part of a simulation.)
Operating under the false belief that all accessible entities were intended to be in-scope for the exercise, Claude compromised the impacted organizations’ infrastructure using basic techniques, such as exploiting weak passwords and unauthenticated endpoints. It did not find or exploit any complex vulnerabilities, and in each case, Claude continued working to complete only the specific capture-the-flag task its evaluation had assigned. However, in some cases, our older model continued its attack even after getting evidence it was running on the open internet; our latest model stopped once it recognized it was on the internet. In none of these situations did Claude exfiltrate itself or deliberately attempt to escape its test environment.
The incidents involved three different Claude models: Opus 4.7, Mythos 5, and an internal research test model.1 The earliest incidents date to April. The models in each of these evaluations ran without the standard safeguards we deploy when we make the model generally available (that is, they did not have the classifiers and monitoring we usually implement to prevent misuse). The models did, however, have their model-specific safety training (that is, they were not the “helpful-only” versions of the models that we sometimes use in testing). All the evaluations described here run on dedicated infrastructure, which is separate from and has no access to Anthropic’s sensitive internal systems or customer data.
We began our transcript review on Thursday, July 23, and stopped all cyber evaluations the same day after identifying transcripts where Claude may have accessed the internet. We identified all three incidents the next day. We notified our evaluation partner Irregular and the three affected organizations on Monday, July 27. The two organizations we were able to reach had not previously detected the activity or contacted us, and we are now working with them to remediate. We are continuing to reach out to the third.
We conducted this review in collaboration with Irregular. We’re grateful to them for working closely with us to understand and resolve these incidents; they are also conducting their own investigation. We believe this type of collaboration is increasingly critical to ensuring safe, rigorous evaluation of models. We look forward to our joint work on security.
In what follows, we provide more details on each incident and how we’re responding moving forward.
We regularly run cybersecurity evaluations that involve simulating real offensive cybersecurity challenges. These evaluations are a critical step in developing and releasing models—a nuanced understanding of model capabilities allows us to design appropriate safeguards and ultimately release models safely.
We began running cybersecurity evaluations in February 2025 with Claude Sonnet 3.7 on Cybench, which consisted of 40 different capture-the-flag challenges. Over time, we’ve increased the number of benchmarks we use as new ones are released and model capabilities evolve. In the Mythos 5 System Card, for example, we included CyberGym and ExploitBench, benchmarks that evaluate the ability of language models to find novel vulnerabilities.2 In most of these evaluations, the environments are not connected to the internet, and the model is told as much.
We frequently work with external partners who create and assist in running some of these cybersecurity evaluations. External partners offer environments and scenarios more diverse than we could build alone, and provide independent, third-party assessments of our models.
Of the 141,006 evaluation runs we reviewed, we identified three separate incidents (involving six total runs, four of which impacted the same organization; the other two incidents each happened in independent evaluation runs). Each incident involved a different fictional capture-the-flag scenario—for example, in one, Claude played an employee of a made-up company, attacking that company’s internal systems inside a private test environment.
In all cases, our evaluation prompt stated explicitly that Claude had no internet access, but didn’t give Claude any limits on where to look for the flag. However, a misconfiguration left the machines that Claude accessed as part of the evaluation with live internet access. Neither we nor our evaluation partner were aware of this misconfiguration until we detected it through our additional evaluation monitoring last week.
Several defense-in-depth measures, on both our side and our partner’s, could have prevented these incidents, or at least reduced their likelihood of occurring. Careful validation of all internet access paths before evaluations began and real-time monitoring of the evaluation logs would have helped to surface the problem sooner. Both we and our partner also could have reviewed evaluation transcripts or network logs more thoroughly. It’s also possible that a prompt which told Claude it did have internet access would have changed how Claude behaved when it came into contact with real systems.
Claude did what capture-the-flag exercises train cyber experts to do: look for ways to reach the flag. That search led it to discover that its machine could reach the internet, but having been told in the system prompt that there was no internet access, Claude believed everything it initially encountered was part of the simulation, and treated the real systems it found as pieces of the exercise. The three incidents involved three different models, and each responded differently once signs emerged that the targets were real, as we describe below.
In one evaluation, the fictional target company chosen by our evaluation partner shared a name with an active website domain name. This evaluation container had unintended direct internet access, due to the misunderstanding discussed above.
In four runs of this evaluation, Claude ran into difficulty reaching its simulated target within the evaluation environment, but then discovered that the real company was reachable via the internet. Assuming this was the intended target in the capture-the-flag task, Claude sought, identified, and exploited vulnerabilities within the company’s infrastructure, believing it to be part of the exercise. These four runs extracted information like application and infrastructure credentials, and obtained access to a database containing several hundred rows of production data. This represented the most serious impact we identified.
This incident involved Claude Opus 4.7, and was the only case in which the model continued to attack a system after learning it was likely operating in a real environment. In all four of the runs, the model eventually recognized (as can be observed in its verbalized reasoning) that the system was real—in two cases, the model rationalized that the real company must be part of the exercise. However, none stopped the attack on that basis.
In another evaluation, Claude found a document inside the fictional environment that appeared to be another made-up company’s setup instructions for new developers. Those instructions told employees to install a Python package from PyPI—the public registry where Python software is published—that did not actually exist.
Claude spotted this as a potential opening: if it published its own package under the same name, the fictional company’s systems would download and install it automatically. So, Claude built and published a malicious (essentially booby-trapped) Python package with the same name in an attempt to win the capture-the-flag challenge.
Claude went to extensive lengths to carry out this attack—lengths that would likely have indicated to a human participant that this was no longer just an evaluation, and that they were in fact uploading a real PyPI package. For instance, in one case, in order to create a PyPI account, Claude needed an email address. And in order to create an email address, it needed a phone number. To get a phone number, after failing to find a free phone number service, it tried—and failed—to obtain funds to pay for a phone number through several different means. It finally backtracked, found a free, non-blocked email provider, used this to register a PyPI account, and then used this account to upload malware to PyPI.
Claude believed the package registry it was using to be part of the simulation, but in reality the package was made freely available online for roughly one hour. During that window, the package was downloaded and run on 15 real systems. One of these systems was a scanner belonging to a real security company (separate from the fictional company introduced in the scenario) that routinely installs Python packages and scans them for malware. When that company’s scanner installed the package, Claude’s hidden code executed. We believe the company’s security scanner treated PyPI packages as safe to install, and as a result, Claude was able to exfiltrate the company’s credentials to a collection point it had set up. Claude then used these credentials to access further infrastructure from this company.