CVE-2022-23577

Null-dereference in Tensorflow

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The implementation of GetInitOp is vulnerable to a crash caused by dereferencing a null pointer:

const auto& init_op_sig_it =
    meta_graph_def.signature_def().find(kSavedModelInitOpSignatureKey);
if (init_op_sig_it != sig_def_map.end()) {
  *init_op_name = init_op_sig_it->second.outputs()
                      .find(kSavedModelInitOpSignatureKey)
                      ->second.name();
  return Status::OK();
}

Here, we have a nested map and we assume that if the first .find succeeds then so would be the search in the internal map. However, the maps are built based on the SavedModel protobuf format and a malicious user can alter that on disk before loading to cause the second .find to return nullptr.

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

CVE-2022-23577 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.

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We have patched the issue in GitHub commit 4f38b1ac8e42727e18a2f0bde06d3bee8e77b250.

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-2022-23577? CVE-2022-23577 is a high-severity null pointer dereference vulnerability in tensorflow (pip), affecting versions < 2.5.3. It is fixed in 2.5.3, 2.6.3, 2.7.1. The application dereferences a null pointer, causing a crash.
2. How severe is CVE-2022-23577? CVE-2022-23577 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-2022-23577?
   • 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-2022-23577? Yes. CVE-2022-23577 is fixed in 2.5.3, 2.6.3, 2.7.1. Upgrade to this version or later.
5. Is CVE-2022-23577 exploitable, and should I be worried? Whether CVE-2022-23577 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-2022-23577 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-2022-23577?
   • 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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