| Package | Affected versions | Patched versions |
|---|---|---|
| tensorflow | ≥ 2.6.0, < 2.6.3≥ 2.7.0, < 2.7.1< 2.5.3 | 2.6.32.7.12.5.3 |
| tensorflow-cpu | ≥ 2.6.0, < 2.6.3≥ 2.7.0, < 2.7.1< 2.5.3 | 2.6.32.7.12.5.3 |
| tensorflow-gpu | ≥ 2.6.0, < 2.6.3≥ 2.7.0, < 2.7.1< 2.5.3 | 2.6.32.7.12.5.3 |
| tensorflow | = 2.7.0≥ 2.6.0, ≤ 2.6.2≤ 2.5.2 |
The implementation of OpLevelCostEstimator::CalculateTensorSize is vulnerable to an integer overflow if an attacker can create an operation which would involve a tensor with large enough number of elements:
int64_t OpLevelCostEstimator::CalculateTensorSize(
const OpInfo::TensorProperties& tensor, bool* found_unknown_shapes) {
int64_t count = CalculateTensorElementCount(tensor, found_unknown_shapes);
int size = DataTypeSize(BaseType(tensor.dtype()));
VLOG(2) << "Count: " << count << " DataTypeSize: " << size;
return count * size;
}
Here, count and size can be large enough to cause count * size to overflow.
We have patched the issue in GitHub commit fcd18ce3101f245b083b30655c27b239dc72221e.
The fix will be included in TensorFlow 2.8.0. We will also cherrypick this commit on TensorFlow 2.7.1, TensorFlow 2.6.3, and TensorFlow 2.5.3, 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.
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