If the splits
argument of RaggedBincount
does not specify a valid SparseTensor
, then an attacker can trigger a heap buffer overflow:
import tensorflow as tf
tf.raw_ops.RaggedBincount(splits=[7,8], values= [5, 16, 51, 76, 29, 27, 54, 95],\
size= 59, weights= [0, 0, 0, 0, 0, 0, 0, 0],\
binary_output=False)
This will cause a read from outside the bounds of the splits
tensor buffer in the implementation of the RaggedBincount
op:
for (int idx = 0; idx < num_values; ++idx) {
while (idx >= splits(batch_idx)) {
batch_idx++;
}
...
if (bin < size) {
if (binary_output_) {
out(batch_idx - 1, bin) = T(1);
} else {
T value = (weights_size > 0) ? weights(idx) : T(1);
out(batch_idx - 1, bin) += value;
}
}
}
Before the for
loop, batch_idx
is set to 0. The attacker sets splits(0)
to be 7, hence the while
loop does not execute and batch_idx
remains 0. This then results in writing to out(-1, bin)
, which is before the heap allocated buffer for the output tensor.
We have patched the issue in GitHub commit eebb96c2830d48597d055d247c0e9aebaea94cd5.
The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3, as these are also affected.
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 reported by members of the Aivul Team from Qihoo 360.