CVE-2025-30405 is a critical integer overflow vulnerability (CWE-190) identified in Meta Platforms, Inc's ExecuTorch product. ExecuTorch is a framework or library used for loading and executing machine learning models. The vulnerability arises during the loading process of ExecuTorch models, where an integer overflow or wraparound can occur. This overflow leads to incorrect calculations of memory allocation sizes, causing objects to be placed outside their intended memory boundaries. Such memory corruption can result in arbitrary code execution, allowing an attacker to execute malicious code with the privileges of the ExecuTorch process. The vulnerability affects all versions of ExecuTorch prior to the commit 0830af8207240df8d7f35b984cdf8bc35d74fa73, indicating that a patch or fix has been committed but not yet widely released or deployed. The CVSS v3.1 base score is 9.8, reflecting the vulnerability's critical severity, with an attack vector of network (AV:N), low attack complexity (AC:L), no privileges required (PR:N), no user interaction (UI:N), and impacts on confidentiality, integrity, and availability (C:H/I:H/A:H). No known exploits are currently reported in the wild, but the potential for exploitation is high given the nature of the vulnerability and its ease of exploitation. This vulnerability could be exploited remotely without authentication or user interaction, making it a significant risk for any organization using ExecuTorch for machine learning workloads.

For European organizations, the impact of CVE-2025-30405 could be severe. Organizations leveraging ExecuTorch in their AI/ML pipelines, especially those processing sensitive or critical data, face risks of unauthorized code execution, data breaches, and service disruptions. The ability to execute arbitrary code remotely without authentication means attackers could gain control over systems running ExecuTorch, potentially leading to lateral movement within networks, data exfiltration, or sabotage of AI models and outputs. This could affect sectors such as finance, healthcare, telecommunications, and critical infrastructure, where AI models are increasingly integrated. Additionally, compromised AI models could lead to erroneous decision-making or manipulation of automated processes, amplifying operational risks. The lack of known exploits in the wild currently provides a window for proactive mitigation, but the critical severity score necessitates urgent attention to prevent exploitation.

1. Immediate application of the patch or update containing commit 0830af8207240df8d7f35b984cdf8bc35d74fa73 once it is officially released by Meta Platforms. 2. Until patches are available, restrict network access to ExecuTorch services to trusted internal networks only, employing network segmentation and firewall rules to minimize exposure. 3. Implement runtime application self-protection (RASP) or memory protection mechanisms such as Address Space Layout Randomization (ASLR) and Data Execution Prevention (DEP) to mitigate exploitation impact. 4. Conduct thorough code audits and fuzz testing on ExecuTorch model loading routines to detect similar integer overflow issues proactively. 5. Monitor logs and network traffic for anomalous behavior indicative of exploitation attempts, including unexpected process executions or memory access violations. 6. Employ strict input validation and sanitization on model files before loading to prevent malformed or malicious models from triggering the overflow. 7. Develop incident response plans specific to AI/ML infrastructure compromise scenarios to enable rapid containment and recovery.