CVE-2021-29614

Interpreter crash from tf.io.decode_raw

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The implementation of tf.io.decode_raw produces incorrect results and crashes the Python interpreter when combining fixed_length and wider datatypes.

import tensorflow as tf

tf.io.decode_raw(tf.constant(["1","2","3","4"]), tf.uint16, fixed_length=4)

The implementation of the padded version is buggy due to a confusion about pointer arithmetic rules.

First, the code computes the width of each output element by dividing the fixed_length value to the size of the type argument:

int width = fixed_length / sizeof(T);

The fixed_length argument is also used to determine the size needed for the output tensor:

TensorShape out_shape = input.shape();
out_shape.AddDim(width);
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output("output", out_shape, &output_tensor));

auto out = output_tensor->flat_inner_dims<T>();
T* out_data = out.data();
memset(out_data, 0, fixed_length * flat_in.size());

This is followed by reencoding code:

for (int64 i = 0; i < flat_in.size(); ++i) {
  const T* in_data = reinterpret_cast<const T*>(flat_in(i).data());

  if (flat_in(i).size() > fixed_length) {
    memcpy(out_data, in_data, fixed_length);
  } else {
    memcpy(out_data, in_data, flat_in(i).size());
  }
  out_data += fixed_length;
}

The erroneous code is the last line above: it is moving the out_data pointer by fixed_length * sizeof(T) bytes whereas it only copied at most fixed_length bytes from the input. This results in parts of the input not being decoded into the output.

Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory.

A write operation targets a memory location beyond the intended buffer boundary. Typical impact: memory corruption, crash, or arbitrary code execution.

CVE-2021-29614 has a CVSS score of 7.1 (Medium). 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 698e01511f62a3c185754db78ebce0eee1f0184d.

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-2021-29614? CVE-2021-29614 is a medium-severity out-of-bounds write 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. A write operation targets a memory location beyond the intended buffer boundary.
2. How severe is CVE-2021-29614? CVE-2021-29614 has a CVSS score of 7.1 (Medium). 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-2021-29614?
   • 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-2021-29614? Yes. CVE-2021-29614 is fixed in 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade to this version or later.
5. Is CVE-2021-29614 exploitable, and should I be worried? Whether CVE-2021-29614 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-2021-29614 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-2021-29614?
   • 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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