Division by 0 in `QuantizedBiasAdd`
Impact
An attacker can trigger an integer division by zero undefined behavior in tf.raw_ops.QuantizedBiasAdd:
import tensorflow as tf
input_tensor = tf.constant([], shape=[0, 0, 0, 0], dtype=tf.quint8)
bias = tf.constant([], shape=[0], dtype=tf.quint8)
min_input = tf.constant(-10.0, dtype=tf.float32)
max_input = tf.constant(-10.0, dtype=tf.float32)
min_bias = tf.constant(-10.0, dtype=tf.float32)
max_bias = tf.constant(-10.0, dtype=tf.float32)
tf.raw_ops.QuantizedBiasAdd(input=input_tensor, bias=bias, min_input=min_input,
max_input=max_input, min_bias=min_bias,
max_bias=max_bias, out_type=tf.qint32)
This is because the implementation of the Eigen kernel does a division by the number of elements of the smaller input (based on shape) without checking that this is not zero:
template <typename T1, typename T2, typename T3>
void QuantizedAddUsingEigen(const Eigen::ThreadPoolDevice& device,
const Tensor& input, float input_min,
float input_max, const Tensor& smaller_input,
float smaller_input_min, float smaller_input_max,
Tensor* output, float* output_min,
float* output_max) {
...
const int64 input_element_count = input.NumElements();
const int64 smaller_input_element_count = smaller_input.NumElements();
...
bcast[0] = input_element_count / smaller_input_element_count;
...
}
This integral division by 0 is undefined behavior.
Patches
We have patched the issue in GitHub commit 67784700869470d65d5f2ef20aeb5e97c31673cb.
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/67784700869470d65d5f2ef20aeb5e97c31673cb
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m34j-p8rj-wjxq
- https://nvd.nist.gov/vuln/detail/CVE-2021-29546
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-474.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-672.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-183.yaml
- 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 15:17
Sources and licenses
OSV / GitHub Advisory Database - PYSEC-2021-183 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-474 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-672 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-m34j-p8rj-wjxq - 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 `QuantizedBiasAdd` this entry
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