CVE-2021-29614: Interpreter crash from `tf.io.decode_raw`

Impact The implementation of tf.io.decoderaw produces incorrect results and crashes the Python interpreter when combining fixedlength and wider datatypes.

python import tensorflow as tf

tf.io.decoderaw(tf.constant(["1","2","3","4"]), tf.uint16, fixedlength=4) The implementation of the padded version is buggy due to a confusion about pointer arithmetic rules.

First, the code computes the width of each output element by dividing the fixedlength value to the size of the type argument:

cc int width = fixedlength / sizeof(T);

The fixedlength argument is also used to determine the size needed for the output tensor:

cc TensorShape outshape = input.shape(); outshape.AddDim(width); Tensor outputtensor = nullptr; OPREQUIRESOK(context, context->allocateoutput("output", outshape, &outputtensor));

auto out = outputtensor->flatinnerdims<T>(); T outdata = out.data(); memset(outdata, 0, fixedlength flatin.size());

This is followed by reencoding code:

cc for (int64 i = 0; i < flatin.size(); ++i) { const T indata = reinterpretcast<const T>(flatin(i).data());

if (flatin(i).size() > fixedlength) { memcpy(outdata, indata, fixedlength); } else { memcpy(outdata, indata, flatin(i).size()); } outdata += fixedlength; }

The erroneous code is the last line above: it is moving the outdata pointer by fixedlength sizeof(T) bytes whereas it only copied at most fixedlength bytes from the input. This results in parts of the input not being decoded into the output.

Furthermore, because the pointer advance is far wider than desired, this quickly leads to writing to outside the bounds of the backing data. This OOB write leads to interpreter crash in the reproducer mentioned here, but more severe attacks can be mounted too, given that this gadget allows writing to periodically placed locations in memory.

Patches We have patched the issue in GitHub commit 698e01511f62a3c185754db78ebce0eee1f0184d.

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.

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