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TensorflowApplication · Google

CVE-2022-21731

MEDIUM · 6.5 CVSS v3.1 Published 2022-02-03
Fix available
A fix is available. Upgrade to after 2.6.2 or later.
See remediation →
71/100
Remediation priority · Elevated
Public exploit Remotely reachable 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 Open Source Machine Learning Framework. The implementation of shape inference for `ConcatV2` can be used to trigger a denial of service attack via a segfault caused by a type confusion. The `axis` argument is translated into `concat_dim` in the `ConcatShapeHelper` helper function. Then, a value for `min_rank` is computed based on `concat_dim`. This is then used to validate that the `values` tensor has at least the required rank. However, `WithRankAtLeast` receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented. Due to the fact that `min_rank` is a 32-bits value and the value of `axis`, the `rank` argument is a negative value, so the error check is bypassed. 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.

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-843

A resource is accessed as one type when it was actually allocated as another, so the code misreads memory layout — in interpreters and language runtimes this is frequently a direct path to code execution. It often arises from unchecked casts on attacker-influenced objects. The fix is strict type checks before casts and memory-safe access patterns.

General guidance for the type confusion 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:<= 2.5.2>= 2.6.0, <= 2.6.2= 2.7.0

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
Network
Complexity
Low
Privileges
Low
User interaction
None
Scope
Unchanged
Confidentiality
None
Integrity
None
Availability
High

CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:N/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 a release after 2.6.2
Vendor patch github.com →
Recommended fix High confidence

TensorFlow 2.5.3, 2.6.3, 2.7.1, or 2.8.0 (depending on your current version)

  1. Identify your current TensorFlow version using `pip show tensorflow` or `import tensorflow; print(tensorflow.__version__)`
  2. Upgrade to a fixed version based on your current TensorFlow release: For 2.5.x, upgrade to 2.5.3; For 2.6.x, upgrade to 2.6.3; For 2.7.0, upgrade to 2.7.1
  3. Run `pip install --upgrade tensorflow==<fixed_version>` or `pip install tensorflow==<fixed_version>`
  4. Verify the upgrade was successful by running `python -c "import tensorflow; print(tensorflow.__version__)"`
  5. Test your models and pipelines to ensure compatibility with the upgraded TensorFlow version
Caveat Minor version upgrades within the same major series typically have minimal breaking changes, but test your inference pipelines to ensure compatibility

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

We can identify the exact fixed release, upgrade, and verify it in staging — typical engagement from $1,600. 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-2022-21731 in production — separate from our analysis above.

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What this is

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What belongs here
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