title: Heap buffer overflow in Tensorflow
description: In Tensorflow version 2.3.0, 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.
problem types: {"CWE-119":"Improper Restriction of Operations within the Bounds of a Memory Buffer"}
affected:
{
product: tensorflow
vendor: tensorflow
versions:
status: affected, version: = 2.3.0
}
metrics:
cvssV3_1: [
attackComplexity: HIGH
attackVector: NETWORK
availabilityImpact: HIGH
baseScore: 8.5
baseSeverity: HIGH
confidentialityImpact: HIGH
integrityImpact: HIGH
privilegesRequired: LOW
scope: CHANGED
userInteraction: NONE
vectorString: CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:H/A:H
version: 3.1
]