CVE-2021-37647

Null pointer dereference in SparseTensorSliceDataset

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Attribution

This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

When a user does not supply arguments that determine a valid sparse tensor, tf.raw_ops.SparseTensorSliceDataset implementation can be made to dereference a null pointer:

import tensorflow as tf

tf.raw_ops.SparseTensorSliceDataset(
  indices=[[],[],[]],
  values=[1,2,3],
  dense_shape=[3,3])

The implementation has some argument validation but fails to consider the case when either indices or values are provided for an empty sparse tensor when the other is not.

If indices is empty (as in the example above), then code that performs validation (i.e., checking that the indices are monotonically increasing) results in a null pointer dereference:

    for (int64_t i = 0; i < indices->dim_size(0); ++i) {
      int64_t next_batch_index = indices->matrix<int64>()(i, 0);
      ...
    }

If indices as provided by the user is empty, then indices in the C++ code above is backed by an empty std::vector, hence calling indices->dim_size(0) results in null pointer dereferencing (same as calling std::vector::at() on an empty vector).

The application dereferences a null pointer, causing a crash. Typical impact: denial of service via crash.

CVE-2021-37647 has a CVSS score of 7.7 (High). The vector is requires local access, no 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 (2.3.4, 2.4.3, 2.5.1); upgrading removes the vulnerable code path.

Kodem intelligence

Already deployed Kodem?

We have patched the issue in GitHub commit 02cc160e29d20631de3859c6653184e3f876b9d7.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

1. What is CVE-2021-37647? CVE-2021-37647 is a high-severity null pointer dereference vulnerability in tensorflow (pip), affecting versions < 2.3.4. It is fixed in 2.3.4, 2.4.3, 2.5.1. The application dereferences a null pointer, causing a crash.
2. How severe is CVE-2021-37647? CVE-2021-37647 has a CVSS score of 7.7 (High). 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-2021-37647?
   • tensorflow (pip) (versions < 2.3.4)
   • tensorflow-cpu (pip) (versions < 2.3.4)
   • tensorflow-gpu (pip) (versions < 2.3.4)
4. Is there a fix for CVE-2021-37647? Yes. CVE-2021-37647 is fixed in 2.3.4, 2.4.3, 2.5.1. Upgrade to this version or later.
5. Is CVE-2021-37647 exploitable, and should I be worried? Whether CVE-2021-37647 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-2021-37647 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-2021-37647?
   • Upgrade tensorflow to 2.3.4 or later
   • Upgrade tensorflow to 2.4.3 or later
   • Upgrade tensorflow to 2.5.1 or later
   • Upgrade tensorflow-cpu to 2.3.4 or later
   • Upgrade tensorflow-cpu to 2.4.3 or later
   • Upgrade tensorflow-cpu to 2.5.1 or later
   • Upgrade tensorflow-gpu to 2.3.4 or later
   • Upgrade tensorflow-gpu to 2.4.3 or later
   • Upgrade tensorflow-gpu to 2.5.1 or later

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