CVE-2022-23589

Null pointer dereference in Grappler's IsConstant

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Under certain scenarios, Grappler component of TensorFlow can trigger a null pointer dereference. There are 2 places where this can occur, for the same malicious alteration of a SavedModel file (fixing the first one would trigger the same dereference in the second place):

First, during constant folding, the GraphDef might not have the required nodes for the binary operation:

  NodeDef* mul_left_child = node_map_->GetNode(node->input(0));
  NodeDef* mul_right_child = node_map_->GetNode(node->input(1));
  // One child must be constant, and the second must be Conv op.
  const bool left_child_is_constant = IsReallyConstant(*mul_left_child);
  const bool right_child_is_constant = IsReallyConstant(*mul_right_child);

If a node is missing, the correposning mul_*child would be null, and the dereference in the subsequent line would be incorrect.

We have a similar issue during IsIdentityConsumingSwitch:

  NodeDef* input_node = graph.GetNode(tensor_id.node());
  return IsSwitch(*input_node);

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

CVE-2022-23589 has a CVSS score of 6.5 (Medium). 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 commits 0a365c029e437be0349c31f8d4c9926b69fa3fa1 and 045deec1cbdebb27d817008ad5df94d96a08b1bf.

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-2022-23589? CVE-2022-23589 is a medium-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-2022-23589? CVE-2022-23589 has a CVSS score of 6.5 (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-2022-23589?
   • 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-2022-23589? Yes. CVE-2022-23589 is fixed in 2.5.3, 2.6.3, 2.7.1. Upgrade to this version or later.
5. Is CVE-2022-23589 exploitable, and should I be worried? Whether CVE-2022-23589 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-2022-23589 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-2022-23589?
   • 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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