CVE-2021-37685

Heap OOB in TFLite

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Attribution

This vulnerability has been reported by Yakun Zhang of Baidu Security.

TFLite's expand_dims.cc contains a vulnerability which allows reading one element outside of bounds of heap allocated data:

  if (axis < 0) { 
    axis = input_dims.size + 1 + axis;
  }   
  TF_LITE_ENSURE(context, axis <= input_dims.size);

  TfLiteIntArray* output_dims = TfLiteIntArrayCreate(input_dims.size + 1);
  for (int i = 0; i < output_dims->size; ++i) {
    if (i < axis) {
      output_dims->data[i] = input_dims.data[i];
    } else if (i == axis) {
      output_dims->data[i] = 1;
    } else {
      output_dims->data[i] = input_dims.data[i - 1];
    }
  }

If axis is a large negative value (e.g., -100000), then after the first if it would still be negative. The check following the if statement will pass and the for loop would read one element before the start of input_dims.data (when i = 0).

A read operation accesses a memory location beyond the intended buffer boundary. Typical impact: sensitive data disclosure or crash.

CVE-2021-37685 has a CVSS score of 5.5 (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 (2.3.4, 2.4.3, 2.5.1); upgrading removes the vulnerable code path.

Kodem intelligence

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We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257.

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-2021-37685? CVE-2021-37685 is a medium-severity out-of-bounds read vulnerability in tensorflow (pip), affecting versions < 2.3.4. It is fixed in 2.3.4, 2.4.3, 2.5.1. A read operation accesses a memory location beyond the intended buffer boundary.
2. How severe is CVE-2021-37685? CVE-2021-37685 has a CVSS score of 5.5 (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-2021-37685?
   • 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-2021-37685? Yes. CVE-2021-37685 is fixed in 2.3.4, 2.4.3, 2.5.1. Upgrade to this version or later.
5. Is CVE-2021-37685 exploitable, and should I be worried? Whether CVE-2021-37685 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-2021-37685 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-2021-37685?
   • 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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