| Package | Affected versions | Patched versions |
|---|---|---|
| tensorflow | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2< 2.1.4 | 2.2.32.3.32.4.22.1.4 |
| tensorflow-cpu | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2< 2.1.4 | 2.2.32.3.32.4.22.1.4 |
| tensorflow-gpu | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2< 2.1.4 | 2.2.32.3.32.4.22.1.4 |
| tensorflow | ≥ 2.2.0, < 2.2.3≥ 2.3.0, < 2.3.3≥ 2.4.0, < 2.4.2< 2.1.4 | 2.2.32.3.32.4.22.1.4 |
The TFLite implementation of concatenation is vulnerable to an integer overflow issue:
for (int d = 0; d < t0->dims->size; ++d) {
if (d == axis) {
sum_axis += t->dims->data[axis];
} else {
TF_LITE_ENSURE_EQ(context, t->dims->data[d], t0->dims->data[d]);
}
}
An attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of int. TFLite uses int to represent tensor dimensions, whereas TF uses int64. Hence, valid TF models can trigger an integer overflow when converted to TFLite format.
We have patched the issue in GitHub commit 4253f96a58486ffe84b61c0415bb234a4632ee73.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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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