CVE-2025-54952 is an integer overflow vulnerability identified in Meta Platforms, Inc's ExecuTorch product, specifically affecting versions prior to commit 8f062d3f661e20bb19b24b767b9a9a46e8359f2b. ExecuTorch is a platform used for loading and executing machine learning models. The vulnerability arises during the loading process of ExecuTorch models, where an integer overflow can occur. This overflow leads to the allocation of smaller-than-expected memory buffers. When the system subsequently writes data into these buffers, it can cause a buffer overflow condition. Buffer overflows are critical because they can overwrite adjacent memory, potentially allowing an attacker to execute arbitrary code, cause denial of service, or corrupt data. The underlying weakness is classified under CWE-680 (Integer Overflow to Buffer Overflow), which highlights the risk of arithmetic operations on integers leading to memory allocation errors. The vulnerability does not currently have a CVSS score assigned, and no known exploits have been reported in the wild as of the publication date (August 7, 2025). However, the nature of the vulnerability suggests it could be exploited by an attacker who can supply maliciously crafted ExecuTorch models to a vulnerable system. This could be done remotely if the system accepts models from untrusted sources or locally if an attacker has access to the environment. The absence of a patch link indicates that a fix may not yet be publicly available or fully integrated into the product. Given the role of ExecuTorch in executing machine learning models, exploitation could lead to unauthorized code execution within environments that rely on this platform, potentially compromising the confidentiality, integrity, and availability of the affected systems.

For European organizations, the impact of CVE-2025-54952 could be significant, especially for those integrating ExecuTorch into their AI/ML workflows or products. Successful exploitation could allow attackers to execute arbitrary code, leading to system compromise, data breaches, or disruption of AI-driven services. This is particularly critical for sectors relying heavily on AI, such as finance, healthcare, automotive, and telecommunications, where ExecuTorch might be embedded in critical infrastructure or decision-making processes. The vulnerability could undermine trust in AI model execution integrity and lead to cascading failures if exploited in production environments. Additionally, organizations handling sensitive personal data under GDPR must consider the legal and reputational risks associated with breaches resulting from this vulnerability. The lack 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 potential for remote exploitation, depending on deployment scenarios, increases the attack surface for European enterprises using ExecuTorch.

1. Immediate assessment of ExecuTorch usage within the organization to identify all instances and versions deployed. 2. Restrict the sources of ExecuTorch models to trusted and verified origins only, implementing strict validation and integrity checks on all input models. 3. Employ runtime protections such as memory safety tools, sandboxing, or containerization to limit the impact of potential buffer overflows. 4. Monitor vendor communications closely for patches or updates addressing this vulnerability and prioritize timely application once available. 5. Implement network segmentation and access controls to limit exposure of ExecuTorch environments to untrusted networks or users. 6. Conduct code reviews and penetration testing focused on model loading components to detect similar integer overflow issues proactively. 7. Prepare incident response plans that include scenarios involving AI model execution compromise. 8. Consider alternative AI execution platforms with a strong security track record if immediate patching is not feasible.