CVE-2021-37686

Infinite loop in TFLite

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Attribution

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

The strided slice implementation in TFLite has a logic bug which can allow an attacker to trigger an infinite loop. This arises from newly introduced support for ellipsis in axis definition:

  for (int i = 0; i < effective_dims;) {
    if ((1 << i) & op_context->params->ellipsis_mask) {
      // ...
      int ellipsis_end_idx =
          std::min(i + 1 + num_add_axis + op_context->input_dims - begin_count,
                   effective_dims);
      // ...
      for (; i < ellipsis_end_idx; ++i) {
        // ...
      }
      continue;
    }
    // ...
    ++i;
  }

An attacker can craft a model such that ellipsis_end_idx is smaller than i (e.g., always negative). In this case, the inner loop does not increase i and the continue statement causes execution to skip over the preincrement at the end of the outer loop.

CVE-2021-37686 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 (2.6.0rc2); upgrading removes the vulnerable code path.

Kodem intelligence

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We have patched the issue in GitHub commit dfa22b348b70bb89d6d6ec0ff53973bacb4f4695.

The fix will be included in TensorFlow 2.6.0. This is the only affected version.

1. What is CVE-2021-37686? CVE-2021-37686 is a medium-severity security vulnerability in tensorflow (pip), affecting versions >= 2.6.0rc0, < 2.6.0rc2. It is fixed in 2.6.0rc2.
2. How severe is CVE-2021-37686? CVE-2021-37686 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-2021-37686?
   • tensorflow (pip) (versions >= 2.6.0rc0, < 2.6.0rc2)
   • tensorflow-cpu (pip) (versions >= 2.6.0rc0, < 2.6.0rc2)
   • tensorflow-gpu (pip) (versions >= 2.6.0rc0, < 2.6.0rc2)
4. Is there a fix for CVE-2021-37686? Yes. CVE-2021-37686 is fixed in 2.6.0rc2. Upgrade to this version or later.
5. Is CVE-2021-37686 exploitable, and should I be worried? Whether CVE-2021-37686 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-2021-37686 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-2021-37686?
   • Upgrade tensorflow to 2.6.0rc2 or later
   • Upgrade tensorflow-cpu to 2.6.0rc2 or later
   • Upgrade tensorflow-gpu to 2.6.0rc2 or later

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