CVE-2025-30402 is a heap-based buffer overflow vulnerability identified in the ExecuTorch component developed by Meta Platforms, Inc. ExecuTorch appears to be a runtime or execution environment that loads and runs methods, likely related to machine learning or AI workloads given the name similarity to PyTorch. The vulnerability arises during the loading of ExecuTorch methods, where improper handling of heap memory leads to a buffer overflow condition. This flaw can cause the runtime to crash, resulting in denial of service, but more critically, it may allow an attacker to execute arbitrary code or trigger other unintended behaviors. The vulnerability is classified under CWE-122, which involves writing outside the bounds of allocated heap buffers, a common and dangerous memory corruption issue. The affected versions are those prior to a specific commit (93b1a0c15f7eda49b2bc46b5b4c49557b4e9810f), indicating that a patch or fix has been committed but not necessarily widely deployed yet. No CVSS score has been assigned, and there are no known exploits in the wild at this time. The vulnerability was reserved in March 2025 and published in July 2025, indicating recent discovery and disclosure. The lack of a patch link suggests that users should monitor Meta’s official channels for updates or apply the fix from the referenced commit if possible. Given the nature of heap buffer overflows, exploitation could be complex but feasible, especially if attackers can supply malicious method payloads to the ExecuTorch runtime. This vulnerability could be leveraged in environments where ExecuTorch is used to process untrusted inputs or where attackers have some level of access to submit code or data to the runtime.

For European organizations, the impact of CVE-2025-30402 depends largely on the adoption of ExecuTorch within their infrastructure. Organizations leveraging Meta’s AI or machine learning runtimes, particularly those integrating ExecuTorch for model execution or development, could face significant risks. Exploitation could lead to runtime crashes causing service disruptions or denial of service, impacting availability of critical AI-driven applications. More severely, successful exploitation could allow attackers to execute arbitrary code within the context of the vulnerable process, potentially leading to full system compromise, data exfiltration, or lateral movement within networks. This is particularly concerning for sectors relying on AI for sensitive operations such as finance, healthcare, telecommunications, and critical infrastructure. The absence of known exploits currently reduces immediate risk, but the vulnerability’s presence in a widely used AI runtime component could attract attackers once exploit techniques mature. European organizations must consider the confidentiality, integrity, and availability risks posed by this vulnerability, especially in environments where ExecuTorch processes untrusted or external inputs. The potential for code execution elevates the threat to a high level, warranting urgent attention to patching and mitigation.

1. Immediate application of patches or updates from Meta Platforms once officially released is critical. Organizations should track the specific commit (93b1a0c15f7eda49b2bc46b5b4c49557b4e9810f) and apply the fix to all ExecuTorch deployments. 2. Until patches are available, restrict access to ExecuTorch runtimes to trusted users and networks only, minimizing exposure to untrusted inputs that could trigger the vulnerability. 3. Implement strict input validation and sanitization on all data or method payloads submitted to ExecuTorch to reduce the risk of maliciously crafted inputs causing buffer overflows. 4. Employ runtime protections such as heap memory protection mechanisms (e.g., heap canaries, ASLR, DEP) to mitigate exploitation impact. 5. Monitor ExecuTorch runtime logs and system behavior for crashes or anomalies indicative of attempted exploitation. 6. Conduct thorough code reviews and security testing on any custom extensions or integrations with ExecuTorch to identify and remediate similar memory safety issues. 7. Incorporate ExecuTorch vulnerability awareness into incident response plans, preparing for potential exploitation scenarios. 8. Engage with Meta’s security advisories and community forums to stay informed about updates, patches, and exploit developments.