CVE-2025-30404 is a critical integer overflow vulnerability identified in Meta Platforms, Inc's ExecuTorch product, specifically affecting versions prior to commit d158236b1dc84539c1b16843bc74054c9dcba006. ExecuTorch is a framework used for loading and executing machine learning models. The vulnerability arises during the loading process of ExecuTorch models, where an integer overflow or wraparound (classified under CWE-190) can occur. This overflow leads to overlapping memory allocations, which can corrupt memory management and potentially allow an attacker to execute arbitrary code or cause other unintended behaviors such as denial of service or data corruption. The vulnerability is remotely exploitable without requiring authentication or user interaction, as indicated by the CVSS vector (AV:N/AC:L/PR:N/UI:N). The CVSS v3.1 base score of 9.8 underscores the critical severity, reflecting high impact on confidentiality, integrity, and availability. Although no known exploits are currently reported in the wild, the nature of the vulnerability and its ease of exploitation make it a significant threat. The absence of available patches at the time of reporting suggests that organizations using ExecuTorch should prioritize mitigation and monitoring. Given ExecuTorch’s role in AI/ML workflows, exploitation could compromise the integrity of AI models and the systems relying on them, potentially leading to broader operational and security consequences.

For European organizations, the impact of CVE-2025-30404 could be substantial, especially those integrating ExecuTorch within AI and machine learning pipelines. Compromise of ExecuTorch could lead to unauthorized code execution, allowing attackers to manipulate AI model behavior, exfiltrate sensitive data, or disrupt critical services. This is particularly concerning for sectors heavily reliant on AI, such as finance, healthcare, automotive, and telecommunications. The vulnerability could undermine trust in AI-driven decision-making systems and cause regulatory compliance issues under frameworks like GDPR if personal data is exposed or integrity is compromised. Additionally, disruption of AI services could impact innovation and operational efficiency. The remote, unauthenticated nature of the exploit increases the risk of widespread attacks, potentially affecting cloud-hosted AI services and on-premises deployments alike. European organizations with AI research centers or those collaborating with Meta Platforms may be at heightened risk due to direct usage or integration of ExecuTorch components.

1. Immediate mitigation involves monitoring for updates from Meta Platforms and applying patches or commits that address the integer overflow vulnerability as soon as they become available. 2. Until patches are released, organizations should implement strict network segmentation and firewall rules to limit exposure of ExecuTorch services to untrusted networks. 3. 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. 4. Conduct thorough code audits and static analysis on AI model loading components to detect anomalous memory allocation patterns. 5. Implement anomaly detection systems to monitor for unusual behavior in AI model execution environments, including unexpected crashes or memory corruption indicators. 6. Restrict the use of untrusted or third-party AI models within ExecuTorch to minimize attack surface. 7. Maintain comprehensive logging and incident response plans tailored to AI infrastructure to enable rapid detection and containment of potential exploitation attempts.