CVE-2025-54951 is a heap-based buffer overflow vulnerability (CWE-122) identified in Meta Platforms, Inc's ExecuTorch product, specifically in the model loading component. ExecuTorch is a runtime environment used for executing machine learning models, and the vulnerability arises from improper handling of data during the loading of these models. When malformed or specially crafted model data is processed, it can cause a buffer overflow on the heap, leading to a crash of the runtime environment. More critically, this overflow can be exploited to achieve arbitrary code execution or trigger other unintended behaviors. The vulnerability affects all versions of ExecuTorch prior to the commit cea9b23aa8ff78aff92829a466da97461cc7930c, indicating that a patch or fix has been committed but not yet widely distributed or released. No known exploits are currently reported in the wild, but the nature of heap-based buffer overflows—especially in a runtime environment handling potentially untrusted model data—makes this a significant security concern. The lack of a CVSS score suggests that the vulnerability is newly disclosed and pending further assessment. The vulnerability could be triggered remotely if an attacker can supply or influence the model data loaded by ExecuTorch, potentially without requiring authentication or user interaction, depending on deployment scenarios. This vulnerability falls under the category of memory corruption issues, which are often leveraged for privilege escalation or persistent compromise if exploited successfully.

For European organizations, the impact of CVE-2025-54951 could be substantial, particularly for those leveraging Meta's ExecuTorch runtime in AI/ML workflows, including sectors such as finance, healthcare, telecommunications, and technology. Successful exploitation could lead to unauthorized code execution within the runtime environment, potentially allowing attackers to execute arbitrary commands, access sensitive data, or disrupt critical AI-driven services. This could compromise the confidentiality, integrity, and availability of AI models and the data they process. Given the increasing reliance on AI for decision-making and automation, disruption or manipulation of these models could have cascading effects on business operations and regulatory compliance, especially under GDPR and other data protection frameworks. Additionally, if ExecuTorch is integrated into cloud or edge computing environments, exploitation could provide a foothold for lateral movement within networks. The absence of known exploits in the wild provides a window for proactive mitigation, but the potential for rapid weaponization remains high due to the commonality of buffer overflow exploitation techniques.

Organizations should immediately identify and inventory all instances of ExecuTorch in their environments. Since a fix has been committed (commit cea9b23aa8ff78aff92829a466da97461cc7930c), applying the latest patches or updates from Meta Platforms as soon as they become available is critical. In the interim, organizations should implement strict input validation and sanitization for all model data loaded into ExecuTorch to prevent malformed or malicious inputs. Employ runtime protections such as heap memory protection mechanisms (e.g., ASLR, DEP) and enable any available ExecuTorch security features that limit code execution privileges. Network segmentation and strict access controls should be enforced to restrict who can supply or update model data. Monitoring and logging of ExecuTorch runtime behavior should be enhanced to detect anomalies indicative of exploitation attempts. Additionally, consider deploying application-layer firewalls or sandboxing ExecuTorch processes to contain potential impacts. Finally, organizations should engage with Meta Platforms for timely security advisories and participate in threat intelligence sharing to stay informed about emerging exploit techniques related to this vulnerability.