CVE-2022-21731

Type confusion leading to segfault in Tensorflow

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Attribution

This vulnerability has been reported by Yu Tian of Qihoo 360 AIVul Team.

The implementation of shape inference for ConcatV2 can be used to trigger a denial of service attack via a segfault caused by a type confusion:

import tensorflow as tf

@tf.function
def test():
  y = tf.raw_ops.ConcatV2(
    values=[[1,2,3],[4,5,6]],
    axis = 0xb500005b)
  return y

test()

The axis argument is translated into concat_dim in the ConcatShapeHelper helper function. Then, a value for min_rank is computed based on concat_dim. This is then used to validate that the values tensor has at least the required rank:

  int64_t concat_dim;
  if (concat_dim_t->dtype() == DT_INT32) {
    concat_dim = static_cast<int64_t>(concat_dim_t->flat<int32>()(0));
  } else {
    concat_dim = concat_dim_t->flat<int64_t>()(0);
  }

  // Minimum required number of dimensions.
  const int min_rank = concat_dim < 0 ? -concat_dim : concat_dim + 1;

  // ...
  ShapeHandle input = c->input(end_value_index - 1);
  TF_RETURN_IF_ERROR(c->WithRankAtLeast(input, min_rank, &input));

However, WithRankAtLeast receives the lower bound as a 64-bits value and then compares it against the maximum 32-bits integer value that could be represented:

Status InferenceContext::WithRankAtLeast(ShapeHandle shape, int64_t rank,
                                         ShapeHandle* out) {
  if (rank > kint32max) {
    return errors::InvalidArgument("Rank cannot exceed kint32max");
  }
  // ...
}

Due to the fact that min_rank is a 32-bits value and the value of axis, the rank argument is a negative value, so the error check is bypassed.

An object is accessed using a type that is incompatible with its actual type, causing the runtime to interpret memory incorrectly. Typical impact: memory safety violations, unexpected behavior, or code execution.

CVE-2022-21731 has a CVSS score of 6.5 (High). The vector is network-reachable, 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 (2.5.3, 2.6.3, 2.7.1); upgrading removes the vulnerable code path.

Kodem intelligence

Already deployed Kodem?

We have patched the issue in GitHub commit 08d7b00c0a5a20926363849f611729f53f3ec022.

The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, as these are also affected and still in supported range.

1. What is CVE-2022-21731? CVE-2022-21731 is a high-severity type confusion vulnerability in tensorflow (pip), affecting versions < 2.5.3. It is fixed in 2.5.3, 2.6.3, 2.7.1. An object is accessed using a type that is incompatible with its actual type, causing the runtime to interpret memory incorrectly.
2. How severe is CVE-2022-21731? CVE-2022-21731 has a CVSS score of 6.5 (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-2022-21731?
   • tensorflow (pip) (versions < 2.5.3)
   • tensorflow-cpu (pip) (versions < 2.5.3)
   • tensorflow-gpu (pip) (versions < 2.5.3)
4. Is there a fix for CVE-2022-21731? Yes. CVE-2022-21731 is fixed in 2.5.3, 2.6.3, 2.7.1. Upgrade to this version or later.
5. Is CVE-2022-21731 exploitable, and should I be worried? Whether CVE-2022-21731 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-2022-21731 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-2022-21731?
   • Upgrade tensorflow to 2.5.3 or later
   • Upgrade tensorflow to 2.6.3 or later
   • Upgrade tensorflow to 2.7.1 or later
   • Upgrade tensorflow-cpu to 2.5.3 or later
   • Upgrade tensorflow-cpu to 2.6.3 or later
   • Upgrade tensorflow-cpu to 2.7.1 or later
   • Upgrade tensorflow-gpu to 2.5.3 or later
   • Upgrade tensorflow-gpu to 2.6.3 or later
   • Upgrade tensorflow-gpu to 2.7.1 or later

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