CVE-2025-30404 is an integer overflow vulnerability identified in Meta Platforms, Inc's ExecuTorch product, specifically in the model loading component. ExecuTorch is a framework or tool related to machine learning model execution. The vulnerability arises due to improper handling of integer values during the allocation process when loading models. An integer overflow or wraparound (classified under CWE-190) can cause the system to miscalculate memory allocation sizes, leading to overlapping memory allocations. This memory corruption can be exploited to execute arbitrary code or cause other unintended behaviors such as crashes or data corruption. The vulnerability affects all versions of ExecuTorch prior to the commit identified as d158236b1dc84539c1b16843bc74054c9dcba006, with no patch links currently available. There are no known exploits in the wild at the time of publication (August 7, 2025). The lack of a CVSS score indicates that the vulnerability has not yet been fully assessed for severity by standard scoring systems. However, the technical nature of the flaw suggests it could be leveraged for remote code execution or denial of service if an attacker can supply malicious model files to the vulnerable system. The vulnerability requires the attacker to have the ability to influence or supply model files that ExecuTorch loads, which may require some level of access or user interaction depending on deployment scenarios.

For European organizations, the impact of CVE-2025-30404 could be significant, especially for those relying on ExecuTorch for machine learning workloads, AI model deployment, or data processing. Exploitation could lead to unauthorized code execution, allowing attackers to compromise confidentiality, integrity, and availability of affected systems. This could result in data breaches, manipulation of AI model outputs, or disruption of critical services relying on AI. Organizations in sectors such as finance, healthcare, telecommunications, and critical infrastructure that adopt AI technologies may face operational disruptions or reputational damage. Additionally, since ExecuTorch is a Meta Platforms product, organizations integrating Meta’s AI tools or platforms might be indirectly affected. The absence of known exploits currently reduces immediate risk but does not eliminate the threat, as attackers may develop exploits once the vulnerability details become widely known. The complexity of exploitation depends on the deployment context; if model files are sourced from untrusted or external parties, the risk increases. European organizations must consider the potential for supply chain attacks or insider threats leveraging this vulnerability.

To mitigate CVE-2025-30404, European organizations should: 1) Immediately track and apply any forthcoming patches or updates from Meta Platforms addressing this vulnerability. 2) Implement strict validation and integrity checks on all ExecuTorch model files before loading, including verifying digital signatures or hashes to prevent malicious model injection. 3) Restrict the sources from which model files can be loaded, limiting to trusted repositories or internal sources only. 4) Employ runtime protections such as sandboxing ExecuTorch processes to limit the impact of potential code execution. 5) Monitor logs and system behavior for anomalies during model loading operations that could indicate exploitation attempts. 6) Conduct security reviews of AI deployment pipelines to identify and remediate any exposure to untrusted inputs. 7) Educate relevant teams about the risks of loading untrusted models and enforce policies to prevent unauthorized model deployment. These steps go beyond generic advice by focusing on supply chain security, runtime containment, and proactive detection tailored to the nature of this vulnerability.