Tensorflow vulnerabilities
6 known vulnerabilities in Tensorflow from Google. 6 are rated high or critical. Each entry lists the affected versions and the release that fixes it, where the vendor has published one.
Latest first
- 7.5 High severity
Tensorflow: Out-of-bounds Write
Incorrect JSON input stringification in Google's Tensorflow serving versions up to 2.18.0 allows for potentially unbounded recursion leading to server crash.
- 8.8 High severity
Tensorflow: Out-of-bounds Write
Tensorflow is an Open Source Machine Learning Framework. The implementation of `SparseCountSparseOutput` is vulnerable to a heap overflow. 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,
- 8.1 High severity
Tensorflow: Out-of-bounds Read
Tensorflow is an Open Source Machine Learning Framework. The implementation of `FractionalAvgPoolGrad` does not consider cases where the input tensors are invalid allowing an attacker to read from outside of bounds of heap. The fix will be included in TensorFl
- 8.1 High severity
Tensorflow: Out-of-bounds Read
Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `ReverseSequence` does not fully validate the value of `batch_dim` and can result in a heap OOB read. There is a check to make sure the value of `batch_dim` does
- 8.8 High severity
Tensorflow: Integer Overflow or Wraparound
Tensorflow is an Open Source Machine Learning Framework. The implementation of shape inference for `Dequantize` is vulnerable to an integer overflow weakness. The `axis` argument can be `-1` (the default value for the optional argument) or any other positive v
- 8.8 High severity
Tensorflow: Out-of-bounds Read
Tensorflow is an Open Source Machine Learning Framework. The implementation of `Dequantize` does not fully validate the value of `axis` and can result in heap OOB accesses. The `axis` argument can be `-1` (the default value for the optional argument) or any ot