First published: Fri May 14 2021(Updated: )
### Impact An attacker can cause a heap buffer overflow in `QuantizedMul` by passing in invalid thresholds for the quantization: ```python 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](https://github.com/tensorflow/tensorflow/blob/87cf4d3ea9949051e50ca3f071fc909538a51cd0/tensorflow/core/kernels/quantized_mul_op.cc#L287-L290) assumes that the 4 arguments are always valid scalars and tries to access the numeric value directly: ```cc 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](https://github.com/tensorflow/tensorflow/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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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.
Credit: security-advisories@github.com security-advisories@github.com
Affected Software | Affected Version | How to fix |
---|---|---|
pip/tensorflow-gpu | >=2.4.0<2.4.2 | 2.4.2 |
pip/tensorflow-gpu | >=2.3.0<2.3.3 | 2.3.3 |
pip/tensorflow-gpu | >=2.2.0<2.2.3 | 2.2.3 |
pip/tensorflow-gpu | <2.1.4 | 2.1.4 |
pip/tensorflow-cpu | >=2.4.0<2.4.2 | 2.4.2 |
pip/tensorflow-cpu | >=2.3.0<2.3.3 | 2.3.3 |
pip/tensorflow-cpu | >=2.2.0<2.2.3 | 2.2.3 |
pip/tensorflow-cpu | <2.1.4 | 2.1.4 |
pip/tensorflow | >=2.4.0<2.4.2 | 2.4.2 |
pip/tensorflow | >=2.3.0<2.3.3 | 2.3.3 |
pip/tensorflow | >=2.2.0<2.2.3 | 2.2.3 |
pip/tensorflow | <2.1.4 | 2.1.4 |
TensorFlow Keras | <2.1.4 | |
TensorFlow Keras | >=2.2.0<2.2.3 | |
TensorFlow Keras | >=2.3.0<2.3.3 | |
TensorFlow Keras | >=2.4.0<2.4.2 |
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CVE-2021-29535 is classified as a high-severity vulnerability due to its potential for causing heap buffer overflows.
To mitigate CVE-2021-29535, upgrade to TensorFlow versions 2.4.2, 2.3.3, 2.2.3, or 2.1.4.
An attacker can exploit CVE-2021-29535 by providing invalid thresholds that lead to heap buffer overflows in the QuantizedMul function.
TensorFlow versions 2.4.0 and earlier, including 2.3.0, 2.2.0, and 2.1.4, are affected by CVE-2021-29535.
More information about CVE-2021-29535 can be found in the official release notes and security advisories from TensorFlow.