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🛡️ CVE-2022-29212 — tensorflow

🟡 CVSS 5.0 — Medium ⚠️ Exploit Public CWE-20 OSV
5.0
CVSS Score
0 Low4 Medium7 High9 Critical10

Description

Core dump when loading TFLite models with quantization in TensorFlow

Impact

Certain TFLite models that were created using TFLite model converter would crash when loaded in the TFLite interpreter. The culprit is that during quantization the scale of values could be greater than 1 but code was always assuming sub-unit scaling.

Thus, since code was calling [QuantizeMultiplierSmallerThanOneExp](https://github.com/tensorflow/tensorflow/blob/f3b9bf4c3c0597563b289c0512e98d4ce81f886e/tensorflow/lite/kernels/internal/quantization_util.cc#L114-L123), the TFLITE_CHECK_LT assertion would trigger and abort the process.

Patches

We have patched the issue in GitHub commit [a989426ee1346693cc015792f11d715f6944f2b8](https://github.com/tensorflow/tensorflow/commit/a989426ee1346693cc015792f11d715f6944f2b8).

The fix will be included in TensorFlow 2.9.0. We will also cherrypick this commit on TensorFlow 2.8.1, TensorFlow 2.7.2, and TensorFlow 2.6.4, as these are also affected and still in supported range.

For more information

Please consult [our security guide](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported externally via a [GitHub issue](https://github.com/tensorflow/tensorflow/issues/43661).

How this vulnerability can be exploited

This issue can be reached with local access to the system, attack complexity is low, an attacker needs low-level privileges on the target. No user interaction is required. The scope is unchanged, so the impact stays within the vulnerable component. Rated impact: confidentiality none, integrity none, availability high.

CVSS metrics in full

The score comes from this vector: CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H

  • Attack vector: Local — a local account, shell or session on the host is needed.
  • Attack complexity: Low — the attack works reliably, with no preparation.
  • Privileges required: Low — an ordinary user account is enough.
  • User interaction: None — nobody has to be tricked into anything.
  • Scope: Unchanged — the damage stays inside the vulnerable component.
  • Confidentiality impact: None.
  • Integrity impact: None.
  • Availability impact: High — total loss, or loss the attacker controls.

Weakness class

CVE-2022-29212 is classified as CWE-20: Improper Input Validation. The application accepts input without checking that it has the expected form, so malformed values reach code that assumes they are well formed.

Affected software

CVE-2022-29212 is recorded against 3 packages.

  • tensorflow
  • tensorflow-cpu (from 2.8.0 up to 2.8.1)
  • tensorflow-gpu (from 2.8.0 up to 2.8.1)

Timeline and source

Published on 6 March 2024 and last revised on 13 July 2026. A public exploit is known to exist, which raises the urgency of patching considerably. A vendor advisory or fix has been published. Record sourced from OSV.

References

github.com (Web)
github.com (Web)
github.com (Web)
github.com (Web)
github.com (Web)
github.com (Web)
github.com (Web)
github.com (Web)
nvd.nist.gov (Web)

Other advisories for this package

tensorflow has other advisories on record. If you are patching this one, these are worth checking on the same host:

Same weakness in other software

These advisories are the same class of weakness (CWE-20: Improper Input Validation) in other software:

Details

Severity MEDIUM
CVSS Score 5.0
CVSS Vector CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
CWE CWE-20
Public Exploit ⚠️ Yes
Source OSV
Published 2024-03-06
Updated 2026-08-20
Modified 2026-07-13

Affected Packages

Software From version Fixed in
tensorflow — —
tensorflow-cpu 2.8.0 2.8.1
tensorflow-gpu 2.8.0 2.8.1

Similar Threats

Exploit Protection

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ⓘ Data Notice: The information presented above has been compiled from publicly available internet sources. Boteraser aggregates this data solely for informational purposes and does not independently classify, evaluate, or endorse any findings about the vulnerabilities listed. The accuracy and completeness of this information is the sole responsibility of the original publishers. Boteraser and its operators accept no liability for any decisions made based on this data.

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