Raw vector
CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:LSummary
CVE-2025-12343 is a low-severity Double Free (CWE-415) vulnerability in Ffmpeg Ffmpeg. Its CVSS base score is 3.3 (Low).
Operationally, ranked at the 5th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as Deep Learning Frameworks; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SI-11 (Error Handling) and SI-2 (Flaw Remediation) — see the control section below for these in your framework.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-207797
Vulnerability Data
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…
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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.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Deep Learning Frameworks
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: tensorflow
Related Threats
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
Mitigating Controls (NIST 800-53 r5) AI
Directly requires timely patching of the identified double-free flaw in FFmpeg's dnn_execute_model_tf() function.
Mandates robust error-handling logic that eliminates the redundant free operations on the task object in error paths.
Applies memory-protection mechanisms (e.g., guard pages, allocator hardening) that can detect or contain double-free conditions before they cause a crash.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Secure SDLC practices directly prevent double-free errors via static analysis, safe memory APIs, and testing.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Security testing in development can detect double-free conditions before release.
Secure development life cycle includes memory-safety practices that can prevent double-free bugs.
Application security requirements can mandate memory-safety rules that reduce double-free risk.
Secure system architecture and engineering principles can prescribe safe memory-management patterns.
Secure coding standards directly address proper use of free() and similar functions.