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Missing Release of Memory after Effective Lifetime

CVE-2022-23585

Medium
6.5/10

Summary

Tensorflow is an Open Source Machine Learning Framework. When decoding PNG images TensorFlow can produce a memory leak if the image is invalid. After calling `png::CommonInitDecode(..., &decode)`, the `decode` value contains allocated buffers which can only be freed by calling `png::CommonFreeDecode(&decode)`. However, several error case in the function implementation invoke the `OP_REQUIRES` macro which immediately terminates the execution of the function, without allowing for the memory free to occur. 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
  • LOW
  • NONE
  • HIGH

CWE-401 - Missing release of memory after effective lifetime (memory leak)

'Missing release of memory after effective lifetime (memory leak)' is a weakness that occurs when software doesn't effectively release allocated memory after it is used. If not addressed, this enables attackers to launch denial of service attacks (by crashing or hanging the program) or take advantage of other unexpected behavior resulting from low memory conditions.

References

Advisory Timeline

  • Published