CVE-2022-23593
Published: 04 February 2022
Summary
CVE-2022-23593 is a medium-severity Improper Check for Unusual or Exceptional Conditions (CWE-754) vulnerability in Google Tensorflow. Its CVSS base score is 5.9 (Medium).
Operationally, ranked in the top 45.5% of CVEs by exploit likelihood; 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-2022-0286
Vulnerability details
Tensorflow is an Open Source Machine Learning Framework. The `simplifyBroadcast` function in the MLIR-TFRT infrastructure in TensorFlow is vulnerable to a segfault (hence, denial of service), if called with scalar shapes. If all shapes are scalar, then `maxRank` is 0,…
more
so we build an empty `SmallVector`. The fix will be included in TensorFlow 2.8.0. This is the only affected version.
- 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
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.