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Uncontrolled Resource Consumption

CVE-2022-23591

High
7.5/10

Summary

Tensorflow is an Open Source Machine Learning Framework. The `GraphDef` format in TensorFlow does not allow self recursive functions. The runtime assumes that this invariant is satisfied. However, a `GraphDef` containing a fragment such as the following can be consumed when loading a `SavedModel`. This would result in a stack overflow during execution as resolving each `NodeDef` means resolving the function itself and its nodes. The fix will be included in TensorFlow 2.8.0rc0. 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.

  • LOW
  • NETWORK
  • NONE
  • UNCHANGED
  • NONE
  • NONE
  • NONE
  • HIGH

CWE-400 - Uncontrolled resource consumption

An uncontrolled resource allocation attack (also known as resource exhaustion attack) triggers unauthorized overconsumption of the limited resources in an application, such as memory, file system storage, database connection pool entries, and CPU. This may lead to denial of service for valid users and degradation of the application's functionality as well as that of the host operating system.

References

Advisory Timeline

  • Published