CVE-2021-29610: Invalid validation in `QuantizeAndDequantizeV2`
Impact The validation in tf.rawops.QuantizeAndDequantizeV2 allows invalid values for axis argument:
python import tensorflow as tf
inputtensor = tf.constant([0.0], shape=[1], dtype=float) inputmin = tf.constant(-10.0) inputmax = tf.constant(-10.0)
tf.rawops.QuantizeAndDequantizeV2( input=inputtensor, inputmin=inputmin, inputmax=inputmax, signedinput=False, numbits=1, rangegiven=False, roundmode='HALFTOEVEN', narrowrange=False, axis=-2)
The validation uses || to mix two different conditions:
cc OPREQUIRES(ctx, (axis == -1 || axis < input.shape().dims()), errors::InvalidArgument(...));
If axis < -1 the condition in OPREQUIRES will still be true, but this value of axis results in heap underflow. This allows attackers to read/write to other data on the heap.
Patches We have patched the issue in GitHub commit c5b0d5f8ac19888e46ca14b0e27562e7fbbee9a9.
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.
Other sources
TensorFlow is an end-to-end open source platform for machine learning. The validation in tf.rawops.QuantizeAndDequantizeV2 allows invalid values for axis argument:. The validation(https://github.com/tensorflow/tensorflow/blob/eccb7ec454e6617738554a255d77f08e60ee0808/tensorflow/core/kernels/quantizeanddequantizeop.cc#L74-L77) uses || to mix two different conditions. If axis < -1 the condition in OPREQUIRES will still be true, but this value of axis results in heap underflow. This allows attackers to read/write to other data on the heap. 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.
Affected Software
Remediation
Event History
Frequently Asked Questions
What is the severity of CVE-2021-29610?
CVE-2021-29610 is classified as a medium severity vulnerability.
How do I fix CVE-2021-29610?
To address CVE-2021-29610, upgrade to TensorFlow version 2.4.2 or later.
Which versions of TensorFlow are affected by CVE-2021-29610?
CVE-2021-29610 affects TensorFlow versions prior to 2.1.4, between 2.2.0 and 2.2.3, between 2.3.0 and 2.3.3, and between 2.4.0 and 2.4.2.
What components are vulnerable in CVE-2021-29610?
The vulnerability exists in the tf.raw_ops.QuantizeAndDequantizeV2 operation of TensorFlow.
Is CVE-2021-29610 a remote code execution vulnerability?
CVE-2021-29610 is not a remote code execution vulnerability; it is related to input validation issues.