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PYSEC-2021-307
No affected components available
TensorFlow is an end-to-end open source platform for machine learning. In affected versions TFLite's expand_dims.cc contains a vulnerability which allows reading one element outside of bounds of heap allocated data. If axis is a large negative value (e.g., -100000), then after the first if it would still be negative. The check following the if statement will pass and the for loop would read one element before the start of input_dims.data (when i = 0). We have patched the issue in GitHub commit d94ffe08a65400f898241c0374e9edc6fa8ed257. 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.
Measures severity based on intrinsic characteristics of the vulnerability, independent of environment.
No exploitation activity has been observed at this time. Continue routine monitoring.
The exploit probability is very low. The vulnerability is unlikely to be exploited in the next 30 days.
We did not find any exploit available. Neither in GitHub repositories nor in the Exploit-Database.
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