CVE-2020-15200

Segfault in Tensorflow

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Attribution

This vulnerability is a variant of GHSA-p5f8-gfw5-33w4

The RaggedCountSparseOutput implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the splits tensor generate a valid partitioning of the values tensor. Thus, the following code sets up conditions to cause a heap buffer overflow:

    auto per_batch_counts = BatchedMap<W>(num_batches);
    int batch_idx = 0;
    for (int idx = 0; idx < num_values; ++idx) {
      while (idx >= splits_values(batch_idx)) {
        batch_idx++;
      }
      const auto& value = values_values(idx);
      if (value >= 0 && (maxlength_ <= 0 || value < maxlength_)) {
        per_batch_counts[batch_idx - 1][value] = 1;
      }
    }

A BatchedMap is equivalent to a vector where each element is a hashmap. However, if the first element of splits_values is not 0, batch_idx will never be 1, hence there will be no hashmap at index 0 in per_batch_counts. Trying to access that in the user code results in a segmentation fault.

The application does not adequately validate input before processing it, allowing unexpected values to reach sensitive code paths. Typical impact: varies by context: data corruption, logic bypass, or denial of service.

CVE-2020-15200 has a CVSS score of 5.9 (High). The vector is network-reachable, 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 (2.3.1); upgrading removes the vulnerable code path.

Kodem intelligence

Already deployed Kodem?

We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.

We recommend users to upgrade to TensorFlow 2.3.1.

1. What is CVE-2020-15200? CVE-2020-15200 is a high-severity improper input validation vulnerability in tensorflow (pip), affecting versions = 2.3.0. It is fixed in 2.3.1. The application does not adequately validate input before processing it, allowing unexpected values to reach sensitive code paths.
2. How severe is CVE-2020-15200? CVE-2020-15200 has a CVSS score of 5.9 (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-2020-15200?
   • tensorflow (pip) (versions = 2.3.0)
   • tensorflow-cpu (pip) (versions = 2.3.0)
   • tensorflow-gpu (pip) (versions = 2.3.0)
4. Is there a fix for CVE-2020-15200? Yes. CVE-2020-15200 is fixed in 2.3.1. Upgrade to this version or later.
5. Is CVE-2020-15200 exploitable, and should I be worried? Whether CVE-2020-15200 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-2020-15200 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-2020-15200?
   • Upgrade tensorflow to 2.3.1 or later
   • Upgrade tensorflow-cpu to 2.3.1 or later
   • Upgrade tensorflow-gpu to 2.3.1 or later

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