GitHub Advisoryadvisory · PyPI
GHSA-3h8m-483j-7xxm Heap out of bounds read in `RequantizationRange`
LOW
Official page ↗
- Aliases
- CVE-2021-29569
- Ecosystem
- PyPI
- Packages
- tensorflow, tensorflow-cpu, tensorflow-gpu
- Severity
- Low
- CVSS vector
- CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
- Weaknesses
- CWE-125
- Reviewed
- 2021-05-18
Affected packages
• PyPI tensorflow: < 2.1.4 → fixed in 2.1.4
• PyPI tensorflow: >= 2.2.0, < 2.2.3 → fixed in 2.2.3
• PyPI tensorflow: >= 2.3.0, < 2.3.3 → fixed in 2.3.3
• PyPI tensorflow: >= 2.4.0, < 2.4.2 → fixed in 2.4.2
• PyPI tensorflow-cpu: < 2.1.4 → fixed in 2.1.4
• PyPI tensorflow-cpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3
• PyPI tensorflow-cpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3
• PyPI tensorflow-cpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2
• PyPI tensorflow-gpu: < 2.1.4 → fixed in 2.1.4
• PyPI tensorflow-gpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3
• PyPI tensorflow-gpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3
• PyPI tensorflow-gpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2
Description
Impact
The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` can cause reads outside of bounds of heap allocated data if attacker supplies specially crafted inputs:
```python
import tensorflow as tf
input = tf.constant([1], shape=[1], dtype=tf.qint32)
input_max = tf.constant([], dtype=tf.float32)
input_min = tf.constant([], dtype=tf.float32)
tf.raw_ops.RequantizationRange(input=input, input_min=input_min, input_max=input_max)
```
The implementation assumes that the `input_min` and `input_max` tensors have at least one element, as it accesses the first element in two arrays:
```cc
const float input_min_float = ctx->input(1).flat()(0);
const float input_max_float = ctx->input(2).flat()(0);
```
If the tensors are empty, `.flat()` is an empty object, backed by an empty array. Hence, accesing even the 0th element is a read outside the bounds.
Patches
We have patched the issue in GitHub commit ef0c008ee84bad91ec6725ddc42091e19a30cf0e.
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 Ying Wang and Yakun Zhang of Baidu X-Team.
Related
Alias · CVE (1)
- CVE-2021-29569 TensorFlow is an end-to-end open source platform for machine learning. vulnerability
Weakness · CWE (1)
- CWE-125 Out-of-bounds Read weakness
References
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-3h8m-483j-7xxm web
- https://nvd.nist.gov/vuln/detail/CVE-2021-29569 advisory
- https://github.com/tensorflow/tensorflow/commit/ef0c008ee84bad91ec6725ddc42091e19a30cf0e web
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-497.yaml web
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-695.yaml web
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-206.yaml web
- https://github.com/tensorflow/tensorflow package