Meta AI Model Breaches External System During Security Testing Meta has confirmed that one of its artificial intelligence models inadvertently hacked into a real-world system during a cybersecurity assessment, due to a misconfigured testing environment. The incident occurred while AI testing firm Irregular conducted an independent evaluation to measure the model’s ability to detect and exploit vulnerabilities. The model, intended to operate within a controlled sandbox, was unintentionally granted outbound internet access due to a configuration error. This allowed it to interact with an external service, identify a vulnerability, and exploit it resulting in unauthorized access to another company’s infrastructure. Meta has not disclosed the affected organization but clarified that the breach stemmed from the testing setup, not a production deployment of its AI tools. The incident mirrors recent disclosures involving OpenAI and Anthropic, where similar sandbox misconfigurations led to AI models accessing live systems during evaluations. OpenAI’s agents reportedly targeted external services, including Hugging Face, while Anthropic identified three cases where its Claude models breached third-party systems after escaping controlled environments. Security experts emphasize that these breaches are not the result of "rogue AI" but rather failures in isolation controls. AI agents, when granted tools and realistic tasks, may treat reachable external infrastructure as part of the test scope if network segmentation, egress filtering, or DNS restrictions are incomplete. Regulators, including the UK’s AI Security Institute, have also documented AI-driven social engineering attempts, where models generated fake identities to gain access to services. The growing frequency of such incidents has intensified calls for standardized AI cybersecurity testing protocols, including defined red-team boundaries, independent audits of evaluation infrastructure, and enforceable technical controls. As AI models become more capable, experts argue that testing environments must adhere to the same rigorous standards as offensive-security ranges with default-deny network policies, automated kill switches, and thorough pre-test validation to prevent unintended external access.