CVE-2022-21732
Published: 03 February 2022
Summary
CVE-2022-21732 is a medium-severity Allocation of Resources Without Limits or Throttling (CWE-770) vulnerability in Google Tensorflow. Its CVSS base score is 4.3 (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-2022-0295
Vulnerability details
Tensorflow is an Open Source Machine Learning Framework. The implementation of `ThreadPoolHandle` can be used to trigger a denial of service attack by allocating too much memory. This is because the `num_threads` argument is only checked to not be negative,…
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but there is no upper bound on its value. 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.
This control implements explicit throttling on session allocation, addressing the weakness of allocating resources without limits.
Plan testing exercises resource allocation limits and throttling during simulated failures, directly addressing weaknesses that allow unbounded resource use.
Contingency plan updates ensure recovery strategies address unbounded resource allocation, making it harder for attackers to exploit lack of throttling to cause prolonged outages.
Provides continuity when unbounded resource allocation at the primary site leads to exhaustion and downtime.
Alternate services allow operations to continue when primary allocation of resources lacks limits or throttling.
Explicit planning of security-related actions requires defining limits, windows, and resource allocations, making allocation without throttling far less likely.
Measures of performance include tracking allocation behavior and throttling effectiveness, reducing the window for resource exhaustion attacks.
Imposes an inactivity-based limit on network resource allocation, throttling the number of concurrently held connections.