OpenAI details third-party cyber evaluation incidents where models accessed internet beyond testing boundaries

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Independent Testing Incidents Highlight Need for Evolved Evaluation Standards

Independent testing plays an important role in helping us validate and further understand risks before deployment. Some cyber evaluations intentionally use custom configurations, including lowered safeguards, to measure underlying capability—not how models ordinarily behave in publicly available deployments.

During recent evaluations, two external testing partners identified incidents in which testing configurations and controls, combined with the advancing capabilities of recent models, allowed model activity to extend beyond their intended testing boundaries. These incidents underscore the importance of collaborating across the industry and with third-party evaluators to evolve the standards for testing environments and practices as models become more capable.

Editor’s Note: These incidents are separate from the Hugging Face security incident. We will continue to share updates on the Hugging Face incident here.

Overview of the Incidents

The new incidents involved OpenAI models accessing the public internet during third-party cyber evaluations, under specific conditions and reduced-safeguard configurations that did not reflect ordinary deployment. The incidents included:

• UK AISI (UK government’s AI Security Institute): Was running cyber-range evaluations with internet access intentionally enabled so agents could find their own tools and operate under conditions closer to a real attacker. Cyber classifiers were disabled to measure underlying capability. (Read their blog here – opens in a new window.)
• Irregular (external cybersecurity testing partner): Was running Capture-the-Flag-style evaluations intended to be isolated from the internet. However, a testing-environment misconfiguration allowed models to access the public internet.

Below, we summarize what happened, the testing conditions that enabled the activity, the steps taken to contain it, and what we are doing to ensure independent labs can continue to rigorously and safely evaluate increasingly capable models.

Strengthening Third-Party Model Evaluation Environments

These incidents point to the same broader challenge we described in our recent post about the Hugging Face incident: as model capabilities advance, the security and safety systems around models need to advance too. This includes both the environments used to develop models and the environments that labs and independent partners use to evaluate them.

In the coming weeks, we will review our own approach to third-party testing, including:

• How we identify higher-risk evaluations
• How we agree on scope
• How we assess requests to enable internet access or lowered safeguards
• Setting expectations for isolation, credential handling, monitoring, and stop conditions
• Establishing clearer incident-notification and escalation processes

We are committed to working across the industry to strengthen shared practices for conducting high-risk evaluations safely. This includes convening stakeholders such as national AI institutes, independent evaluators, other AI labs, and other groups in the coming weeks. Our goal is to preserve the value of rigorous independent evaluation while ensuring that testing practices keep pace with increasingly capable models.

UK AISI Incident Details

On August 3, UK AISI informed us that during a routine cyber evaluation started on July 25, models from OpenAI and another lab went beyond the scope of testing in some cases. The evaluation took place in controlled cyber ranges designed to mimic real-world networks. Of the 19 events identified, two involved an OpenAI model, GPT‑5.6 Sol. The other instances were models from another lab.

For this evaluation, the agents were instructed to act as cybersecurity experts in a capture-the-flag exercise: compromise three connected simulated environments and retrieve a final flag. The task prompt identified the in-scope range networks and named several systems as out of scope. UK AISI also enabled live internet access so agents could download tools and operate under conditions closer to a human attacker—and disabled the model’s cyber classifiers to measure underlying capability.

UK AISI’s intended authorization boundary was the simulated cyber range: the agents were authorized to attack the specified simulated networks and retrieve a flag, not to interact with systems outside the range’s network boundary. However, the agents were not explicitly told how they could and could not use open internet access, which UK AISI identifies as a potential contributing cause of the incident.

Across these runs, UK AISI found that GPT‑5.6 Sol carried out two unsanctioned actions while trying to find a path into the simulated range and retrieve the flag:

• GPT‑5.6 Sol reused a GitHub token that another lab’s agent had left publicly accessible to check whether a system inside the range was polling GitHub or another update service.
• It attempted account-recovery and request-limit workarounds.