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CVE-2022-29212

MEDIUM
Published 2022-05-20T23:15:15.000Z
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CVSS Score

V3.1
5.5
/10
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H
Base Score Metrics
Exploitability: N/A Impact: N/A

EPSS Score

v2025.03.14
0.001
probability
of exploitation in the wild

There is a 0.1% chance that this vulnerability will be exploited in the wild within the next 30 days.

Updated: 2025-06-25
Exploit Probability
Percentile: 0.316
Higher than 31.6% of all CVEs

Attack Vector Metrics

Attack Vector
LOCAL
Attack Complexity
LOW
Privileges Required
LOW
User Interaction
NONE
Scope
UNCHANGED

Impact Metrics

Confidentiality
NONE
Integrity
NONE
Availability
HIGH

Description

TensorFlow is an open source platform for machine learning. Prior to versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4, 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`, the `TFLITE_CHECK_LT` assertion would trigger and abort the process. Versions 2.9.0, 2.8.1, 2.7.2, and 2.6.4 contain a patch for this issue.

Available Exploits

No exploits available for this CVE.

Related News

No news articles found for this CVE.

Affected Products

GitHub Security Advisories

Community-driven vulnerability intelligence from GitHub

✓ GitHub Reviewed MODERATE

Core dump when loading TFLite models with quantization in TensorFlow

GHSA-8wwm-6264-x792

Advisory Details

### 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).

Affected Packages

PyPI tensorflow
ECOSYSTEM: ≥0 <2.6.4
PyPI tensorflow
ECOSYSTEM: ≥2.7.0 <2.7.2
PyPI tensorflow
ECOSYSTEM: ≥2.8.0 <2.8.1
PyPI tensorflow-cpu
ECOSYSTEM: ≥0 <2.6.4
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.7.0 <2.7.2
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.8.0 <2.8.1
PyPI tensorflow-gpu
ECOSYSTEM: ≥0 <2.6.4
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.7.0 <2.7.2
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.8.0 <2.8.1

CVSS Scoring

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

Advisory provided by GitHub Security Advisory Database. Published: May 24, 2022, Modified: June 6, 2022

References

Published: 2022-05-20T23:15:15.000Z
Last Modified: 2025-04-22T17:57:26.021Z
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