| Package | Affected versions | Patched versions |
|---|---|---|
| tensorflow | ≥ 2.3.0, < 2.3.4≥ 2.4.0, < 2.4.3≥ 2.5.0, < 2.5.1< 2.4.3 | 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< 2.4.3 | 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< 2.4.3 | 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 |
The implementation of tf.raw_ops.QuantizeAndDequantizeV4Grad is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value.
import tensorflow as tf
tf.raw_ops.QuantizeAndDequantizeV4Grad(
gradients=[1.0,2.0],
input=[1.0,1.0],
input_min=[0.0],
input_max=[10.0],
axis=-100)
The implementation uses the axis value as the size argument to absl::InlinedVector constructor. But, the constructor uses an unsigned type for the argument, so the implicit conversion transforms the negative value to a large integer.
We have patched the issue in GitHub commit 96f364a1ca3009f98980021c4b32be5fdcca33a1.
The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, and TensorFlow 2.4.3, 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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