CVE-2022-21737
Published: 03 February 2022
Summary
CVE-2022-21737 is a medium-severity Improper Check for Unusual or Exceptional Conditions (CWE-754) vulnerability in Google Tensorflow. Its CVSS base score is 6.5 (Medium).
Operationally, ranked at the 44.7th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Deep Learning Frameworks.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2022-0300
Vulnerability details
Tensorflow is an Open Source Machine Learning Framework. The implementation of `*Bincount` operations allows malicious users to cause denial of service by passing in arguments which would trigger a `CHECK`-fail. There are several conditions that the input arguments must satisfy.…
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Some are not caught during shape inference and others are not caught during kernel implementation. This results in `CHECK` failures later when the output tensors get allocated. The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.
- CWE(s)
AI Security AnalysisAI
- AI Category
- Deep Learning Frameworks
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: tensorflow, machine learning, tensorflow, tensorflow, tensorflow, tensorflow
Related Threats
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Requires detection and response to audit logging failures as an unusual or exceptional condition.
Implements detection of unusual or exceptional conditions followed by safe mode entry, reducing the window for exploitation of unchecked conditions.
Training ensures users perform required checks for unusual or exceptional conditions as part of contingency roles, limiting attacker leverage from skipped validations.
IR testing directly validates checks for unusual or exceptional conditions that could indicate security incidents.
Requires ongoing monitoring of organization-defined metrics and analysis, enabling checks for unusual or exceptional conditions.
Security testing routinely checks for unusual or exceptional inputs/conditions, identifying missing validation steps that flaw remediation then resolves.
Requires detection of unusual conditions followed by a controlled transition to the defined failure state.
MTTF determination forces explicit checks for conditions that precede predictable component failure.