CVE-2025-54952 is a critical integer overflow vulnerability identified in Meta Platforms, Inc's ExecuTorch product, specifically affecting versions prior to commit 8f062d3f661e20bb19b24b767b9a9a46e8359f2b. 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 can occur. This overflow leads to the allocation of memory regions smaller than expected. When the system subsequently writes data to these undersized buffers, it can cause a buffer overflow condition (CWE-680). Such buffer overflows can corrupt memory, potentially allowing an attacker to execute arbitrary code, cause denial of service, or trigger other unintended behaviors. The CVSS v3.1 base score of 9.8 reflects the high severity of this vulnerability, indicating it is remotely exploitable without authentication or user interaction, and impacts confidentiality, integrity, and availability. Although no public exploits are currently known, the vulnerability's nature and severity suggest that exploitation could be straightforward once a crafted malicious model is introduced to the vulnerable ExecuTorch environment. The lack of available patches at the time of publication further increases the risk for users of affected versions.

For European organizations, the impact of CVE-2025-54952 could be significant, especially for those leveraging Meta's ExecuTorch for machine learning workloads. Successful exploitation could lead to full system compromise, data breaches, or service disruptions. Organizations in sectors such as finance, healthcare, telecommunications, and critical infrastructure that rely on AI/ML models for decision-making or automation could face operational interruptions or data integrity issues. Given the remote exploitability and lack of required authentication, attackers could target exposed ExecuTorch deployments or supply chains where models are shared or loaded from untrusted sources. This could also facilitate lateral movement within networks or persistent footholds. The critical severity underscores the urgency for European entities to assess their exposure and implement mitigations promptly to prevent potential espionage, sabotage, or ransomware attacks leveraging this vulnerability.

1. Immediate assessment of ExecuTorch usage: Identify all instances and versions of ExecuTorch deployed within the organization. 2. Apply updates: Although no patches are currently listed, monitor Meta Platforms' official channels for security updates or commits addressing this vulnerability and apply them promptly. 3. Input validation and model source control: Restrict loading of ExecuTorch models to trusted, verified sources only. Implement strict validation and integrity checks on model files before loading to detect malformed or malicious inputs. 4. Network segmentation: Isolate ExecuTorch environments from untrusted networks to reduce exposure to remote attacks. 5. Runtime protections: Employ memory protection mechanisms such as Address Space Layout Randomization (ASLR), Data Execution Prevention (DEP), and control flow integrity to mitigate exploitation impact. 6. Monitoring and detection: Deploy anomaly detection and logging around ExecuTorch model loading activities to identify suspicious behavior indicative of exploitation attempts. 7. Incident response readiness: Prepare playbooks for rapid containment and remediation if exploitation is suspected. 8. Engage with Meta Platforms' security advisories and community forums for updates and shared mitigation strategies.