CVE-2025-63396 is a vulnerability identified in PyTorch versions 2.5 and 2.7.1 related to the improper handling of the profiler.stop() method within the torch.profiler.profile (PythonTracer) component. PyTorch is a widely used open-source machine learning framework, and its profiling tools help developers analyze performance metrics. The vulnerability arises when the profiler.stop() method is omitted, causing the profiling process to either crash or hang during the finalization phase. This behavior leads to a Denial of Service (DoS) condition by disrupting the availability of the profiling functionality and potentially the application using it. The issue is classified under CWE-667 (Improper Locking), indicating a concurrency or synchronization problem during profiler finalization. The CVSS score is 3.3 (low), reflecting the limited impact confined to availability without affecting confidentiality or integrity. Exploitation requires local privileges (AV:L), low attack complexity (AC:L), and no user interaction (UI:N). No known exploits have been reported in the wild, and no official patches have been released at the time of publication. This vulnerability primarily affects developers and organizations that rely on PyTorch's profiling features for performance tuning and debugging in machine learning workflows.

For European organizations, the primary impact of CVE-2025-63396 is a potential Denial of Service affecting applications or services that utilize PyTorch's profiling tools. This can lead to interruptions in machine learning model development, testing, and performance analysis, potentially delaying project timelines and reducing operational efficiency. Since the vulnerability does not compromise data confidentiality or integrity, the risk is limited to availability. Organizations heavily invested in AI and ML, especially those using PyTorch for research, development, or production workloads, may experience degraded service or crashes if profiling is improperly managed. This could affect sectors such as automotive, finance, healthcare, and technology where AI workloads are prevalent. However, the requirement for local privileges to exploit the vulnerability limits the attack surface primarily to internal users or compromised systems. The absence of known exploits reduces immediate risk but does not eliminate the need for vigilance.

To mitigate CVE-2025-63396, organizations should ensure that all PyTorch profiling code explicitly calls profiler.stop() to properly finalize profiling sessions and avoid hangs or crashes. Developers should review and audit codebases for correct profiler lifecycle management. Incorporating automated testing to detect hangs or crashes during profiling can help identify vulnerable code paths. Monitoring application logs and performance metrics for signs of profiling failures or unexpected hangs is recommended. Until official patches are released, consider isolating profiling workloads in controlled environments to minimize impact. Additionally, restrict local access to systems running PyTorch profiling to trusted users only, reducing the risk of exploitation. Keeping PyTorch versions updated and subscribing to vendor advisories will facilitate timely application of patches once available. Educating development teams on proper profiler usage and the implications of this vulnerability will further reduce risk.