CVE-2025-54949 is a critical heap-based buffer overflow vulnerability (CWE-122) found in Meta Platforms, Inc's ExecuTorch product. ExecuTorch is a framework or tool related to loading and executing machine learning models. The vulnerability arises during the loading process of ExecuTorch models, where improper handling of heap memory leads to a buffer overflow condition. This flaw can be exploited remotely without any authentication or user interaction, as indicated by the CVSS vector (AV:N/AC:L/PR:N/UI:N). Successful exploitation allows an attacker to execute arbitrary code with the privileges of the ExecuTorch process, potentially leading to full system compromise. The vulnerability affects all versions prior to the commit ede82493dae6d2d43f8c424e7be4721abe5242be, with no patch links currently provided. The CVSS v3.1 base score is 9.8, reflecting its critical severity due to high impact on confidentiality, integrity, and availability, combined with ease of exploitation. Although no known exploits are reported in the wild yet, the nature of the vulnerability and the widespread use of Meta's AI tools suggest a high risk of future exploitation. The vulnerability is particularly dangerous because it can be triggered remotely without any prerequisites, making it a prime target for attackers aiming to compromise AI infrastructure or systems running ExecuTorch models.

For European organizations, the impact of CVE-2025-54949 can be severe. Many enterprises and research institutions in Europe utilize AI frameworks and tools from Meta Platforms for machine learning workloads, data analysis, and AI-driven services. Exploitation of this vulnerability could lead to unauthorized code execution, data breaches, disruption of AI services, and potential lateral movement within corporate networks. This could compromise sensitive data, intellectual property, and critical AI model integrity. Additionally, organizations relying on AI for decision-making or operational automation may face significant operational disruptions. Given the criticality and remote exploitability, attackers could leverage this vulnerability to deploy ransomware, steal confidential information, or sabotage AI-driven processes. The lack of patches at the time of disclosure increases the urgency for European organizations to implement mitigations to protect their AI infrastructure and maintain compliance with data protection regulations such as GDPR.

1. Immediate mitigation should include isolating ExecuTorch workloads in segmented network environments to limit exposure. 2. Employ runtime application self-protection (RASP) and memory protection mechanisms such as Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP) to reduce exploitation success. 3. Monitor network traffic and logs for unusual activity related to ExecuTorch model loading, including anomalous input sizes or malformed model files. 4. Restrict access to ExecuTorch services to trusted users and systems only, using network-level controls and strong authentication. 5. Implement strict input validation and sandboxing for model files before loading them into ExecuTorch environments. 6. Stay alert for official patches or updates from Meta Platforms and apply them promptly once available. 7. Conduct regular security assessments and penetration testing focused on AI infrastructure to detect similar vulnerabilities. 8. Develop incident response plans specifically addressing AI platform compromises to minimize damage in case of exploitation.