CVE-2021-29531

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a CHECK fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is nullptr. Hence, when calling png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., image.flat<T>().data()) is NULL. This then triggers the CHECK_NOTNULL in the first line of png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since image is null, this results in abort being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a CHECK fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is nullptr. Hence, when calling png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., image.flat<T>().data()) is NULL. This then triggers the CHECK_NOTNULL in the first line of png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since image is null, this results in abort being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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.

Impact

An attacker can trigger a CHECK fail in PNG encoding by providing an empty input tensor as the pixel data:

import tensorflow as tf

image = tf.zeros([0, 0, 3])
image = tf.cast(image, dtype=tf.uint8) 
tf.raw_ops.EncodePng(image=image) 

This is because the implementation only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is nullptr. Hence, when calling png::WriteImageToBuffer, the first argument (i.e., image.flat<T>().data()) is NULL. This then triggers the CHECK_NOTNULL in the first line of png::WriteImageToBuffer.

template <typename T>
bool WriteImageToBuffer(
    const void* image, int width, int height, int row_bytes, int num_channels,
    int channel_bits, int compression, T* png_string,
    const std::vector<std::pair<std::string, std::string> >* metadata) {
  CHECK_NOTNULL(image);
  ...
}

Since image is null, this results in abort being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack.

Patches

We have patched the issue in GitHub commit 26eb323554ffccd173e8a79a8c05c15b685ae4d1.

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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a CHECK fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is nullptr. Hence, when calling png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., image.flat<T>().data()) is NULL. This then triggers the CHECK_NOTNULL in the first line of png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since image is null, this results in abort being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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.

TensorFlow is an end-to-end open source platform for machine learning. An attacker can trigger a CHECK fail in PNG encoding by providing an empty input tensor as the pixel data. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L57-L60) only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is nullptr. Hence, when calling png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/kernels/image/encode_png_op.cc#L79-L93), the first argument (i.e., image.flat<T>().data()) is NULL. This then triggers the CHECK_NOTNULL in the first line of png::WriteImageToBuffer(https://github.com/tensorflow/tensorflow/blob/e312e0791ce486a80c9d23110841525c6f7c3289/tensorflow/core/lib/png/png_io.cc#L345-L349). Since image is null, this results in abort being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack. 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.

Impact

An attacker can trigger a CHECK fail in PNG encoding by providing an empty input tensor as the pixel data:

import tensorflow as tf

image = tf.zeros([0, 0, 3])
image = tf.cast(image, dtype=tf.uint8) 
tf.raw_ops.EncodePng(image=image) 

This is because the implementation only validates that the total number of pixels in the image does not overflow. Thus, an attacker can send an empty matrix for encoding. However, if the tensor is empty, then the associated buffer is nullptr. Hence, when calling png::WriteImageToBuffer, the first argument (i.e., image.flat<T>().data()) is NULL. This then triggers the CHECK_NOTNULL in the first line of png::WriteImageToBuffer.

template <typename T>
bool WriteImageToBuffer(
    const void* image, int width, int height, int row_bytes, int num_channels,
    int channel_bits, int compression, T* png_string,
    const std::vector<std::pair<std::string, std::string> >* metadata) {
  CHECK_NOTNULL(image);
  ...
}

Since image is null, this results in abort being called after printing the stacktrace. Effectively, this allows an attacker to mount a denial of service attack.

Patches

We have patched the issue in GitHub commit 26eb323554ffccd173e8a79a8c05c15b685ae4d1.

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.