CVE-2021-29531

CHECK-fail in tf.raw_ops.EncodePng

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Attribution

This vulnerability has been reported by Yakun Zhang and Ying Wang of Baidu X-Team.

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.

CVE-2021-29531 has a CVSS score of 2.5 (Low). The vector is requires local access, low privileges required, and no user interaction. A CVSS score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether this affects your application depends on whether the vulnerable code is present and reachable in your environment. A fixed version is available (2.1.4, 2.2.3, 2.3.3, 2.4.2); upgrading removes the vulnerable code path.

Kodem intelligence

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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.

1. What is CVE-2021-29531? CVE-2021-29531 is a low-severity security vulnerability in tensorflow (pip), affecting versions < 2.1.4. It is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2.
2. How severe is CVE-2021-29531? CVE-2021-29531 has a CVSS score of 2.5 (Low). This score reflects the worst-case severity of the vulnerability, not your specific exposure. Whether it represents real risk in your environment depends on whether the vulnerable code is present and reachable.
3. Which packages are affected by CVE-2021-29531?
   • tensorflow (pip) (versions < 2.1.4)
   • tensorflow-cpu (pip) (versions < 2.1.4)
   • tensorflow-gpu (pip) (versions < 2.1.4)
4. Is there a fix for CVE-2021-29531? Yes. CVE-2021-29531 is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade to this version or later.
5. Is CVE-2021-29531 exploitable, and should I be worried? Whether CVE-2021-29531 is exploitable in your environment depends on whether the vulnerable code is present and reachable. A CVSS score is a worst-case rating; it does not account for your specific deployment, configuration, or usage patterns. Kodem, an Intelligent Application Security platform, uses runtime intelligence to show which vulnerabilities actually execute in production, so you can focus on the ones that represent real risk. Get a demo
6. What actually determines whether CVE-2021-29531 is exploitable, and how bad it is? Exploitability and impact are not fixed properties of a CVE. They depend on runtime truth: whether the vulnerable code is present, reachable, and actually executes in your application. A high CVSS score on a dependency that never runs is not the same as real risk. Kodem, an Intelligent Application Security platform, uses runtime intelligence to reveal which vulnerabilities actually execute in production, so teams prioritize the ones that genuinely matter.
7. How do I fix CVE-2021-29531?
   • Upgrade tensorflow to 2.1.4 or later
   • Upgrade tensorflow to 2.2.3 or later
   • Upgrade tensorflow to 2.3.3 or later
   • Upgrade tensorflow to 2.4.2 or later
   • Upgrade tensorflow-cpu to 2.1.4 or later
   • Upgrade tensorflow-cpu to 2.2.3 or later
   • Upgrade tensorflow-cpu to 2.3.3 or later
   • Upgrade tensorflow-cpu to 2.4.2 or later
   • Upgrade tensorflow-gpu to 2.1.4 or later
   • Upgrade tensorflow-gpu to 2.2.3 or later
   • Upgrade tensorflow-gpu to 2.3.3 or later
   • Upgrade tensorflow-gpu to 2.4.2 or later

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