TensorflowApplication · Google

CVE-2021-37682

HIGH · 7.1 CVSS v3.1 Published 2021-08-12
Fix available
A fix is available. Upgrade to 2.3.4 / 2.4.3 or later.
See remediation →
73/100
Remediation priority · Elevated
Zero-click Patch available

Official description Straight from the sourceThe vendor's or NVD's own wording, published unedited. Authoritative, but often terse — it says what broke, rarely what to do.

NVD · unedited
TensorFlow is an end-to-end open source platform for machine learning. In affected versions all TFLite operations that use quantization can be made to use unitialized values. [For example](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/depthwise_conv.cc#L198-L200). The issue stems from the fact that `quantization.params` is only valid if `quantization.type` is different that `kTfLiteNoQuantization`. However, these checks are missing in large parts of the code. We have patched the issue in GitHub commits 537bc7c723439b9194a358f64d871dd326c18887, 4a91f2069f7145aab6ba2d8cfe41be8a110c18a5 and 8933b8a21280696ab119b63263babdb54c298538. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

Technical summary Written by usOur analysis, written from the advisory, the CVSS vector and the affected-version data. It adds context the advisory leaves out, and never invents facts that are not in the source.

dbcve analysis
How this class of weakness works · CWE-908

Memory or a resource is used before it has been initialised, so its contents are whatever happened to be there — sometimes leaking earlier data, sometimes values an attacker can influence. Behaviour becomes unpredictable and occasionally exploitable. Remediation is initialising every resource before use and ensuring initialisation happens on all code paths.

General guidance for the use of uninitialized resource class — the official description and references above are authoritative for this specific CVE. Want a bespoke review and a reviewed fix? Ask our team →

Affected products & versions What the vendor confirmedThe version ranges the vendor confirmed as vulnerable. If your version sits inside a range here, treat yourself as exposed until you have upgraded.

NVD · CPE data
TensorflowApplication
Affected:>= 2.3.0, < 2.3.4>= 2.4.0, < 2.4.3= 2.5.0= 2.6.0

CVSS breakdown How the score is builtThe industry scoring standard. It rates how the flaw is reached, what it takes to exploit, and what an attacker gains — the score is derived from those, not the other way round.

From the vector
Attack vector
Local
Complexity
Low
Privileges
Low
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
High
Availability
High

CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H

Remediation Closing itWhat it takes to close this. Where a vendor fix exists we point at it; where none exists we say so plainly, and can build one. Effort estimates are scoped from the advisory, not from your codebase.

dbcve · scoped
Upgrade available Upgrade to 2.3.4 / 2.4.3 or later
Fixed in 2.3.42.4.3
Vendor patch github.com →
Recommended fix High confidence

TensorFlow 2.6.0 (or latest stable 2.x release)

  1. 1. Identify your current TensorFlow version using `pip show tensorflow` or `import tensorflow as tf; print(tf.__version__)`
  2. 2. Determine which upgrade path applies based on your current version: if on 2.3.x, upgrade to 2.3.4 or later; if on 2.4.x, upgrade to 2.4.3 or later; if on 2.5.x, upgrade to 2.5.1 or later; if on 2.6.0, upgrade to 2.6.1 or later
  3. 3. Update TensorFlow using pip: `pip install --upgrade tensorflow==2.6.0` (or newer version if available)
  4. 4. Verify the upgrade: `python -c "import tensorflow as tf; print(tf.__version__)"`
  5. 5. Test your machine learning workloads to ensure compatibility with the new TensorFlow version
  6. 6. Review TensorFlow 2.6.0 release notes for any breaking changes that may affect your models or custom ops
Caveat TensorFlow 2.6.0 introduced breaking changes; review the release notes for deprecated APIs, TFLite compatibility changes, and migration guidance before upgrading production workloads

Generated from the published advisory — verify against the referenced sources before acting.

We can perform the upgrade in your staging environment and verify nothing breaks — typical engagement from $1,950. Get the upgrade done

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References Go to the primary sourcePrimary sources — vendor advisories, patches and trackers. Where our summary and a reference disagree, the reference wins.

Primary sources

Practitioner notes

Contributed

Peer-ranked notes from engineers who’ve handled CVE-2021-37682 in production — separate from our analysis above.

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What belongs here
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  • Version or environment caveats, and links to real fixes
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