Описание
A flaw was found in FFmpeg’s TensorFlow backend within the libavfilter/dnn_backend_tf.c source file. The issue occurs in the dnn_execute_model_tf() function, where a task object is freed multiple times in certain error-handling paths. This redundant memory deallocation can lead to a double-free condition, potentially causing FFmpeg or any application using it to crash when processing TensorFlow-based DNN models. This results in a denial-of-service scenario but does not allow arbitrary code execution under normal conditions.
Пакеты
| Пакет | Статус | Версия исправления | Релиз | Тип |
|---|---|---|---|---|
| ffmpeg | fixed | 7:7.1.2-1 | package | |
| ffmpeg | not-affected | bookworm | package | |
| ffmpeg | not-affected | bullseye | package |
Примечания
Fixed by: https://git.ffmpeg.org/gitweb/ffmpeg.git/commit/b8d5f65b9e89d893f27cf00799dbc15fc0ca2f8e (master)
Fixed by: https://git.ffmpeg.org/gitweb/ffmpeg.git/commit/729dd9b2865bedd6a8160ef574db0d391c491bee (n7.1.2)
Introduced by: https://git.ffmpeg.org/gitweb/ffmpeg.git/commit/af052f9066a14ab633ef8e0bda49a432771efe74 (n6.1)
EPSS
Связанные уязвимости
A flaw was found in FFmpeg’s TensorFlow backend within the libavfilter/dnn_backend_tf.c source file. The issue occurs in the dnn_execute_model_tf() function, where a task object is freed multiple times in certain error-handling paths. This redundant memory deallocation can lead to a double-free condition, potentially causing FFmpeg or any application using it to crash when processing TensorFlow-based DNN models. This results in a denial-of-service scenario but does not allow arbitrary code execution under normal conditions.
A flaw was found in FFmpeg’s TensorFlow backend within the libavfilter/dnn_backend_tf.c source file. The issue occurs in the dnn_execute_model_tf() function, where a task object is freed multiple times in certain error-handling paths. This redundant memory deallocation can lead to a double-free condition, potentially causing FFmpeg or any application using it to crash when processing TensorFlow-based DNN models. This results in a denial-of-service scenario but does not allow arbitrary code execution under normal conditions.
A flaw was found in FFmpeg’s TensorFlow backend within the libavfilter/dnn_backend_tf.c source file. The issue occurs in the dnn_execute_model_tf() function, where a task object is freed multiple times in certain error-handling paths. This redundant memory deallocation can lead to a double-free condition, potentially causing FFmpeg or any application using it to crash when processing TensorFlow-based DNN models. This results in a denial-of-service scenario but does not allow arbitrary code execution under normal conditions.
A flaw was found in FFmpeg’s TensorFlow backend within the libavfilter/dnn_backend_tf.c source file. The issue occurs in the dnn_execute_model_tf() function, where a task object is freed multiple times in certain error-handling paths. This redundant memory deallocation can lead to a double-free condition, potentially causing FFmpeg or any application using it to crash when processing TensorFlow-based DNN models. This results in a denial-of-service scenario but does not allow arbitrary code execution under normal conditions.
EPSS