CVE-2020-15209
A crafted TFLite model can force a node to have as input a tensor backed by a
nullptr
buffer. This can be achieved by changing a buffer index in the
flatbuffer serialization to convert a read-only tensor to a read-write one. The
runtime assumes that these buffers are written to before a possible read, hence
they are initialized with
nullptr
:
TfLiteTensorReset(type, name, ConvertArrayToTfLiteIntArray(rank, dims),
GetLegacyQuantization(quantization),
/*buffer=*/nullptr, required_bytes, allocation_type,
nullptr, is_variable, &tensor);
However, by changing the buffer index for a tensor and implicitly converting that tensor to be a read-write one, as there is nothing in the model that writes to it, we get a null pointer dereference.
TensorFlow 1.15.0, 1.15.1, 1.15.2, 1.15.3, 2.0.0, 2.0.1, 2.0.2, 2.1.0, 2.1.1, 2.2.0, 2.3.0.
We have patched the issue in 0b5662bc and will release patch releases for all versions between 1.15 and 2.3.
We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1.
A potential workaround would be to add a custom Verifier
to the model loading
code to ensure that no operator reuses tensors as both inputs and outputs. Care
should be taken to check all types of inputs (i.e., constant or variable tensors
as well as optional tensors).
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 discovered by members of the Aivul Team and is also discoverable through a variant analysis of another vulnerability reported by members of the Aivul Team from Qihoo 360.