FFmpeg TensorFlow DNN Backend dnn_execute_model_tf double free

CVSS Meta Temp Score
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CTI Interest Score
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5.4$0-$5k0.00

Summaryinfo

A vulnerability was found in FFmpeg. It has been rated as critical. Affected by this issue is the function dnn_execute_model_tf of the component TensorFlow DNN Backend. The manipulation leads to double free. This vulnerability is referenced as CVE-2025-12343. Remote exploitation of the attack is possible. No exploit is available.

Detailsinfo

A vulnerability was found in FFmpeg (version unknown). It has been rated as critical. This issue affects the function dnn_execute_model_tf of the component TensorFlow DNN Backend. The manipulation with an unknown input leads to a double free vulnerability. Using CWE to declare the problem leads to CWE-415. The product calls free() twice on the same memory address, potentially leading to modification of unexpected memory locations. Impacted is confidentiality, integrity, and availability. The summary by CVE is:

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.

It is possible to read the advisory at bugzilla.redhat.com. The identification of this vulnerability is CVE-2025-12343. The exploitation is known to be easy. The attack may be initiated remotely. No form of authentication is needed for a successful exploitation. Technical details of the vulnerability are known, but there is no available exploit. The pricing for an exploit might be around USD $0-$5k at the moment (estimation calculated on 02/27/2026).

The vulnerability scanner Nessus provides a plugin with the ID 271857 (Linux Distros Unpatched Vulnerability : CVE-2025-12343), which helps to determine the existence of the flaw in a target environment.

There is no information about possible countermeasures known. It may be suggested to replace the affected object with an alternative product.

The vulnerability is also documented in the databases at Tenable (271857) and CERT Bund (WID-SEC-2025-2423). Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

Affected

  • Open Source ffmpeg

Productinfo

Type

Name

License

Website

CPE 2.3info

CPE 2.2info

CVSSv4info

VulDB Vector: 🔒
VulDB Reliability: 🔍

CVSSv3info

VulDB Meta Base Score: 5.4
VulDB Meta Temp Score: 5.4

VulDB Base Score: 7.3
VulDB Temp Score: 7.3
VulDB Vector: 🔒
VulDB Reliability: 🔍

NVD Base Score: 5.5
NVD Vector: 🔒

CNA Base Score: 3.3
CNA Vector: 🔒

CVSSv2info

AVACAuCIA
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VulDB Base Score: 🔒
VulDB Temp Score: 🔒
VulDB Reliability: 🔍

Exploitinginfo

Class: Double free
CWE: CWE-415 / CWE-119
CAPEC: 🔒
ATT&CK: 🔒

Physical: Partially
Local: Yes
Remote: Yes

Availability: 🔒
Status: Not defined

EPSS Score: 🔒
EPSS Percentile: 🔒

Price Prediction: 🔍
Current Price Estimation: 🔒

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Nessus ID: 271857
Nessus Name: Linux Distros Unpatched Vulnerability : CVE-2025-12343

Threat Intelligenceinfo

Interest: 🔍
Active Actors: 🔍
Active APT Groups: 🔍

Countermeasuresinfo

Recommended: no mitigation known
Status: 🔍

0-Day Time: 🔒

Timelineinfo

10/27/2025 Advisory disclosed
10/27/2025 +0 days VulDB entry created
02/27/2026 +123 days VulDB entry last update

Sourcesinfo

Product: ffmpeg.org

Advisory: bugzilla.redhat.com
Status: Not defined

CVE: CVE-2025-12343 (🔒)
GCVE (CVE): GCVE-0-2025-12343
GCVE (VulDB): GCVE-100-330155
CERT Bund: WID-SEC-2025-2423 - ffmpeg (TensorFlow DNN backend): Schwachstelle ermöglicht Denial of Service

Entryinfo

Created: 10/27/2025 17:02
Updated: 02/27/2026 06:22
Changes: 10/27/2025 17:02 (49), 10/28/2025 12:31 (7), 11/01/2025 17:28 (2), 02/27/2026 06:22 (21)
Complete: 🔍
Cache ID: 216::103

Statistical analysis made it clear that VulDB provides the best quality for vulnerability data.

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