CVE-2021-29549

Division by 0 in QuantizedAdd

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Attribution

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

An attacker can cause a runtime division by zero error and denial of service in tf.raw_ops.QuantizedAdd:

import tensorflow as tf

x = tf.constant([68, 228], shape=[2, 1], dtype=tf.quint8)
y = tf.constant([], shape=[2, 0], dtype=tf.quint8)

min_x = tf.constant(10.723421015884028)
max_x = tf.constant(15.19578006631113)
min_y = tf.constant(-5.539003866682977)
max_y = tf.constant(42.18819949559947)

tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)

This is because the implementation computes a modulo operation without validating that the divisor is not zero.

void VectorTensorAddition(const T* vector_data, float min_vector,
                          float max_vector, int64 vector_num_elements,
                          const T* tensor_data, float min_tensor,
                          float max_tensor, int64 tensor_num_elements,
                          float output_min, float output_max, Toutput* output) {
  for (int i = 0; i < tensor_num_elements; ++i) {
    const int64 vector_i = i % vector_num_elements;
    ...
  }
}

Since vector_num_elements is determined based on input shapes, a user can trigger scenarios where this quantity is 0.

CVE-2021-29549 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 744009c9e5cc5d0447f0dc39d055f917e1fd9e16.

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-2021-29549? CVE-2021-29549 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-2021-29549? CVE-2021-29549 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-2021-29549?
   • 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-2021-29549? Yes. CVE-2021-29549 is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade to this version or later.
5. Is CVE-2021-29549 exploitable, and should I be worried? Whether CVE-2021-29549 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-2021-29549 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-2021-29549?
   • 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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