HighVulnerability

CVE-2021-37665: TensorFlow MKL requantization flaw allows out-of-bounds heap access

Source: NVD(opens in a new tab)
Identifier: CVE-2021-37665
Published: August 12, 2021
Record updated: October 9, 2026

Affected: tensorflow < 2.3.4, fixed in 2.3.4, released August 10, 2021, 3 days before disclosure
tensorflow >= 2.4.0, < 2.4.3, fixed in 2.4.3, released August 10, 2021, 3 days before disclosure
tensorflow = 2.5.0, fixed in 2.5.1, released August 10, 2021, 3 days before disclosure
and 6 more
Known exploitation: Not listed in the CISA Known Exploited Vulnerabilities catalog at the last check.
EPSS: 0.2%

Summary

TensorFlow's MKL implementation of requantization fails to fully validate its inputs. In `mkl_requantization_range_per_channel_op.cc`, the `input` tensor's dimensions are not checked, and `MklRequantizePerChannelOp` in `mkl_requantize_per_channel_op.cc` similarly skips full validation of its input arguments. An attacker can trigger undefined behavior by binding a reference to a null pointer, or read data outside the bounds of heap-allocated arrays.

Mitigation

Patched in GitHub commits 9e62869465573cb2d9b5053f1fa02a81fce21d69 and 203214568f5bc237603dbab6e1fd389f1572f5c9. The fix will be included in TensorFlow 2.6.0, and the commit will be cherry-picked to TensorFlow 2.5.1, 2.4.3 and 2.3.4.

Details

Scores and exploitation

CVSS score: 7.8(high)
Patch available: Yes
EPSS: 0.2%Estimated probability that this CVE is exploited in the next 30 days, from the FIRST Exploit Prediction Scoring System. Read on November 4, 2026.

Weaknesses and techniques

Weakness (CWE): CWE-20

Affected

Packages: tensorflow@< 2.3.4 (fixed: 2.3.4)
tensorflow@>= 2.4.0, < 2.4.3 (fixed: 2.4.3)
tensorflow@= 2.5.0 (fixed: 2.5.1)
tensorflow-cpu@< 2.3.4 (fixed: 2.3.4)
tensorflow-cpu@>= 2.4.0, < 2.4.3 (fixed: 2.4.3)
tensorflow-cpu@= 2.5.0 (fixed: 2.5.1)
tensorflow-gpu@< 2.3.4 (fixed: 2.3.4)
tensorflow-gpu@>= 2.4.0, < 2.4.3 (fixed: 2.4.3)
tensorflow-gpu@= 2.5.0 (fixed: 2.5.1)
Vendors: Google

Classification

Attack type: Other
Attack sophistication: Moderate
AI component targeted: Framework
Impact: Confidentiality,
Integrity,
Availability
Labels: security

How this record was produced

Summaries, mitigations and labels on this page are generated automatically from the source. The linked source is authoritative. The source gives this advisory a description and no title, so the title above is a headline generated from the source text.

Original source: https://nvd.nist.gov/vuln/detail/CVE-2021-37665

First tracked: February 15, 2026 at 08:39 PM ET, from NVD/CVE Database

Classifier: claude-haiku-5-5, prompt v4

Classifier confidence: 95% (self-assessed)

Affected packages in the Exposure Registry

Matched by package name and ecosystem. Each entry shows whether the package delegates to a language model and how many tracked packages depend on it.

tensorflowPyPINo LLM dependency detected · 26 tracked dependents
tensorflow-gpuPyPINo LLM dependency detected · 2 tracked dependents

Summary

Mitigation

Affected packages in the Exposure Registry

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