CVE-2025-54950 is a critical security vulnerability classified as an out-of-bounds read (CWE-125) affecting Meta Platforms, Inc's ExecuTorch product. ExecuTorch is a runtime environment used for loading and executing machine learning models. The vulnerability arises during the loading process of ExecuTorch models, where an out-of-bounds memory access can occur. This flaw can cause the runtime to crash, leading to denial of service, but more critically, it may allow an attacker to execute arbitrary code or cause other unintended behaviors. The vulnerability affects all versions of ExecuTorch prior to the commit b6b7a16df5e7852d976d8c34c8a7e9a1b6f7d005, indicating that the issue has been addressed in later versions. The CVSS v3.1 score is 9.8, reflecting a critical severity with network attack vector, no required privileges or user interaction, and high impact on confidentiality, integrity, and availability. No known exploits are currently reported in the wild, but the ease of exploitation and the potential impact make this a high-risk vulnerability. The absence of patch links suggests that users must update to the fixed commit or later versions once available. Given that ExecuTorch is used for executing AI/ML models, exploitation could compromise the integrity of AI workflows, leak sensitive model data, or disrupt AI-driven services.

For European organizations, the impact of CVE-2025-54950 could be significant, especially those relying on AI and machine learning workloads powered by ExecuTorch. Confidentiality breaches could expose proprietary models or sensitive data embedded in models, while integrity violations could allow attackers to manipulate AI outputs, potentially leading to flawed decision-making or automated processes. Availability impacts could disrupt critical AI services, causing operational downtime. Sectors such as finance, healthcare, automotive, and telecommunications, which increasingly integrate AI for analytics, diagnostics, autonomous systems, and customer services, are particularly at risk. Additionally, organizations involved in AI research or deploying AI at scale may face intellectual property theft or sabotage. The critical severity and network exploitability mean attackers could remotely compromise systems without authentication, increasing the threat surface. The lack of known exploits currently provides a window for mitigation, but proactive patching and monitoring are essential to prevent future attacks.

European organizations should immediately assess their use of ExecuTorch and identify affected versions. The primary mitigation is to update ExecuTorch to the fixed version that includes commit b6b7a16df5e7852d976d8c34c8a7e9a1b6f7d005 or later. If immediate updating is not feasible, organizations should implement network-level controls to restrict access to ExecuTorch runtime environments, such as firewall rules and network segmentation, limiting exposure to untrusted networks. Employ runtime application self-protection (RASP) or behavior monitoring to detect abnormal crashes or suspicious model loading activities. Conduct thorough code reviews and testing of AI model inputs to ensure they do not trigger out-of-bounds conditions. Additionally, implement strict input validation and sandboxing of model execution environments to contain potential exploitation. Regularly monitor threat intelligence feeds for emerging exploits and apply security patches promptly. Finally, integrate ExecuTorch usage into vulnerability management and incident response plans to ensure rapid detection and remediation.