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Security Advisories: MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLXFnNDgtODVoZy1tcWM1

Division by 0 in `DenseCountSparseOutput`

Impact

An attacker can cause a denial of service via a FPE runtime error in tf.raw_ops.DenseCountSparseOutput:

import tensorflow as tf

values = tf.constant([], shape=[0, 0], dtype=tf.int64)
weights = tf.constant([])

tf.raw_ops.DenseCountSparseOutput(
  values=values, weights=weights,
  minlength=-1, maxlength=58, binary_output=True)

This is because the implementation computes a divisor value from user data but does not check that the result is 0 before doing the division:

int num_batch_elements = 1;
for (int i = 0; i < num_batch_dimensions; ++i) {
  num_batch_elements *= data.shape().dim_size(i);
}
int num_value_elements = data.shape().num_elements() / num_batch_elements;

Since data is given by the values argument, num_batch_elements is 0.

Patches

We have patched the issue in GitHub commit da5ff2daf618591f64b2b62d9d9803951b945e9f.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, and TensorFlow 2.3.3, as these are also affected.

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution

This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.

Permalink: https://github.com/advisories/GHSA-qg48-85hg-mqc5
JSON: https://advisories.ecosyste.ms/api/v1/advisories/MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLXFnNDgtODVoZy1tcWM1
Source: GitHub Advisory Database
Origin: Unspecified
Severity: Low
Classification: General
Published: about 3 years ago
Updated: over 1 year ago


CVSS Score: 2.5
CVSS vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

Identifiers: GHSA-qg48-85hg-mqc5, CVE-2021-29554
References: Repository: https://github.com/tensorflow/tensorflow
Blast Radius: 12.2

Affected Packages

pypi:tensorflow-gpu
Dependent packages: 155
Dependent repositories: 11,499
Downloads: 295,179 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3
Fixed in: 2.4.2, 2.3.3
All affected versions: 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.12.0
pypi:tensorflow-cpu
Dependent packages: 88
Dependent repositories: 2,483
Downloads: 724,152 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3
Fixed in: 2.4.2, 2.3.3
All affected versions: 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 1.15.0, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0
pypi:tensorflow
Dependent packages: 2,172
Dependent repositories: 73,755
Downloads: 18,567,498 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3
Fixed in: 2.4.2, 2.3.3
All affected versions: 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.1.4, 2.2.0, 2.2.1, 2.2.2, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0