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Improper Restriction of Operations within the Bounds of a Memory Buffer

CVE-2020-15196

High
9.9/10

Summary

In Tensorflow version 2.3.0rc0 before 2.3.1, the `SparseCountSparseOutput` and `RaggedCountSparseOutput` implementations don't validate that the `weights` tensor has the same shape as the data. The check exists for `DenseCountSparseOutput`, where both tensors are fully specified. In the sparse and ragged count weights are still accessed in parallel with the data. But, since there is no validation, a user passing fewer weights than the values for the tensors can generate a read from outside the bounds of the heap buffer allocated for the weights. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

  • LOW
  • NETWORK
  • HIGH
  • CHANGED
  • NONE
  • LOW
  • HIGH
  • HIGH

CWE-119 - Buffer Overflow

Buffer overflow attacks involve data transit and operations exceeding the restricted memory buffer, thereby corrupting or overwriting data in adjacent memory locations. Such overflow allows the attacker to run arbitrary code or manipulate the existing code to cause privilege escalation, data breach, denial of service, system crash and even complete system compromise. Given that languages such as C and C++ lack default safeguards against overwriting or accessing data in their memory, applications utilizing these languages are most susceptible to buffer overflows attacks.

References

Advisory Timeline

  • Published