CVE-2021-37670: Heap OOB in `UpperBound` and `LowerBound` in TensorFlow

Impact An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to tf.rawops.UpperBound:

python import tensorflow as tf tf.rawops.UpperBound( sortedinput=[1,2,3], values=tf.constant(value=[[0,0,0],[1,1,1],[2,2,2]],dtype=tf.int64), outtype=tf.int64) The implementation does not validate the rank of sortedinput argument:

cc void Compute(OpKernelContext ctx) override { const Tensor& sortedinputst = ctx->input(0); // ... OPREQUIRES(ctx, sortedinputst.dimsize(0) == valuest.dimsize(0), Status(error::INVALIDARGUMENT, "Leading dimsize of both tensors must match.")); // ... if (outputt->dtype() == DTINT32) { OPREQUIRES(ctx, FastBoundsCheck(sortedinputst.dimsize(1), ...)); // ... }

As we access the first two dimensions of sortedinputst tensor, it must have rank at least 2.

A similar issue occurs in tf.rawops.LowerBound.

Patches We have patched the issue in GitHub commit 42459e4273c2e47a3232cc16c4f4fff3b3a35c38. The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range. For more information Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.