| Package | Affected versions | Patched versions |
|---|---|---|
| tensorflow | ≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.3≥ 2.5.0, < 2.5.1< 2.3.4 | 2.3.42.4.32.5.1 |
| tensorflow-cpu | ≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.3≥ 2.5.0, < 2.5.1< 2.3.4 | 2.3.42.4.32.5.1 |
| tensorflow-gpu | ≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.3≥ 2.5.0, < 2.5.1< 2.3.4 | 2.3.42.4.32.5.1 |
| tensorflow | = 2.5.0= 2.6.0≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.3 | 2.3.42.4.3 |
An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to tf.raw_ops.UpperBound:
import tensorflow as tf
tf.raw_ops.UpperBound(
sorted_input=[1,2,3],
values=tf.constant(value=[[0,0,0],[1,1,1],[2,2,2]],dtype=tf.int64),
out_type=tf.int64)
The implementation does not validate the rank of sorted_input argument:
void Compute(OpKernelContext* ctx) override {
const Tensor& sorted_inputs_t = ctx->input(0);
// ...
OP_REQUIRES(ctx, sorted_inputs_t.dim_size(0) == values_t.dim_size(0),
Status(error::INVALID_ARGUMENT,
"Leading dim_size of both tensors must match."));
// ...
if (output_t->dtype() == DT_INT32) {
OP_REQUIRES(ctx,
FastBoundsCheck(sorted_inputs_t.dim_size(1), ...));
// ...
}
As we access the first two dimensions of sorted_inputs_t tensor, it must have rank at least 2.
A similar issue occurs in tf.raw_ops.LowerBound.
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.
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
This vulnerability has been reported by members of the Aivul Team from Qihoo 360.
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