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CVE-2021-41201

HIGH
Published 2021-11-05T20:05:12
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CVSS Score

V3.1
7.8
/10
CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Base Score Metrics
Exploitability: N/A Impact: N/A

EPSS Score

v2025.03.14
0.000
probability
of exploitation in the wild

There is a 0.0% chance that this vulnerability will be exploited in the wild within the next 30 days.

Updated: 2025-06-25
Exploit Probability
Percentile: 0.043
Higher than 4.3% of all CVEs

Attack Vector Metrics

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

Impact Metrics

Confidentiality
HIGH
Integrity
HIGH
Availability
HIGH

Description

TensorFlow is an open source platform for machine learning. In affeced versions during execution, `EinsumHelper::ParseEquation()` is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, as these are also affected and still in supported range.

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 HIGH

Unitialized access in `EinsumHelper::ParseEquation`

GHSA-j86v-p27c-73fm

Advisory Details

### Impact During execution, [`EinsumHelper::ParseEquation()`](https://github.com/tensorflow/tensorflow/blob/e0b6e58c328059829c3eb968136f17aa72b6c876/tensorflow/core/kernels/linalg/einsum_op_impl.h#L126-L181) is supposed to set the flags in `input_has_ellipsis` vector and `*output_has_ellipsis` boolean to indicate whether there is ellipsis in the corresponding inputs and output. However, the code only changes these flags to `true` and never assigns `false`. ```cc for (int i = 0; i < num_inputs; ++i) { input_label_counts->at(i).resize(num_labels); for (const int label : input_labels->at(i)) { if (label != kEllipsisLabel) input_label_counts->at(i)[label] += 1; else input_has_ellipsis->at(i) = true; } } output_label_counts->resize(num_labels); for (const int label : *output_labels) { if (label != kEllipsisLabel) output_label_counts->at(label) += 1; else *output_has_ellipsis = true; } ``` This results in unitialized variable access if callers assume that `EinsumHelper::ParseEquation()` always sets these flags. ### Patches We have patched the issue in GitHub commit [f09caa532b6e1ac8d2aa61b7832c78c5b79300c6](https://github.com/tensorflow/tensorflow/commit/f09caa532b6e1ac8d2aa61b7832c78c5b79300c6). The fix will be included in TensorFlow 2.7.0. We will also cherrypick this commit on TensorFlow 2.6.1, TensorFlow 2.5.2, and TensorFlow 2.4.4, 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.

Affected Packages

PyPI tensorflow
ECOSYSTEM: ≥2.6.0 <2.6.1
PyPI tensorflow
ECOSYSTEM: ≥2.5.0 <2.5.2
PyPI tensorflow
ECOSYSTEM: ≥0 <2.4.4
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.6.0 <2.6.1
PyPI tensorflow-cpu
ECOSYSTEM: ≥2.5.0 <2.5.2
PyPI tensorflow-cpu
ECOSYSTEM: ≥0 <2.4.4
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.6.0 <2.6.1
PyPI tensorflow-gpu
ECOSYSTEM: ≥2.5.0 <2.5.2
PyPI tensorflow-gpu
ECOSYSTEM: ≥0 <2.4.4

CVSS Scoring

CVSS Score

7.5

CVSS Vector

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

Advisory provided by GitHub Security Advisory Database. Published: November 10, 2021, Modified: November 13, 2024

References

Published: 2021-11-05T20:05:12
Last Modified: 2024-08-04T03:08:31.506Z
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