CVE-2025-12343 is a memory management vulnerability identified in FFmpeg version 6.1, specifically within the TensorFlow backend implementation in the libavfilter/dnn_backend_tf.c source file. The vulnerability arises in the dnn_execute_model_tf() function, where a task object is freed multiple times along certain error-handling paths. This double-free condition can corrupt the heap, causing FFmpeg or any dependent application to crash unexpectedly when processing TensorFlow-based deep neural network (DNN) models. While double-free bugs can sometimes lead to arbitrary code execution, in this case, the flaw only results in denial-of-service (DoS) by crashing the process, without evidence of code execution or privilege escalation. The vulnerability requires local access and user interaction to trigger, as it occurs during specific TensorFlow DNN model processing workflows. The CVSS v3.1 score of 3.3 reflects a low severity, with attack vector limited to local (AV:L), low complexity (AC:L), no privileges required (PR:N), but requiring user interaction (UI:R). No patches or exploits are currently publicly available, but the issue is documented and published as of February 2026. This vulnerability is relevant for applications and systems that integrate FFmpeg 6.1 for TensorFlow DNN processing, potentially impacting media processing pipelines, AI workloads, or multimedia applications that rely on FFmpeg’s filtering capabilities.

The primary impact of CVE-2025-12343 is denial-of-service due to application crashes triggered by the double-free condition. Organizations using FFmpeg 6.1 for TensorFlow-based DNN processing may experience unexpected service interruptions, degraded availability, or system instability. This can affect media streaming services, video processing pipelines, AI inference workloads, and any software embedding FFmpeg for neural network filtering. Although the vulnerability does not allow code execution or data compromise, the resulting crashes could disrupt critical workflows, cause downtime, and require manual intervention to restart services. In environments with high availability requirements or automated processing, such disruptions could lead to operational delays and increased support costs. Since exploitation requires local access and user interaction, remote exploitation risk is minimal, reducing the threat to internet-facing systems. However, insider threats or compromised user accounts could trigger the vulnerability. No known exploits in the wild further reduce immediate risk, but organizations should remain vigilant.

To mitigate CVE-2025-12343, organizations should upgrade FFmpeg to a version where this double-free flaw is patched once available. Until a patch is released, consider disabling or avoiding the use of the TensorFlow backend filter (libavfilter/dnn_backend_tf.c) in FFmpeg, especially in environments processing untrusted or user-supplied TensorFlow DNN models. Implement strict access controls to limit local user permissions and reduce the risk of exploitation via user interaction. Employ application whitelisting and monitoring to detect abnormal FFmpeg crashes or unusual TensorFlow model processing activity. In containerized or virtualized environments, isolate FFmpeg processes to minimize impact scope. Regularly audit and update all dependencies related to FFmpeg and TensorFlow integration. Finally, maintain robust incident response procedures to quickly recover from any denial-of-service events caused by this vulnerability.