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GHSA-8pmx-p244-g88h
No affected components available
Impact
The implementation of tf.io.decode_raw produces incorrect results and crashes the Python interpreter when combining fixed_length and wider datatypes.
import tensorflow as tf
tf.io.decode_raw(tf.constant(["1","2","3","4"]), tf.uint16, fixed_length=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 fixed_length value to the size of the type argument:
int width = fixed_length / sizeof(T);
The fixed_length argument is also used to determine the size needed for the output tensor:
TensorShape out_shape = input.shape();
out_shape.AddDim(width);
Tensor* output_tensor = nullptr;
OP_REQUIRES_OK(context, context->allocate_output("output", out_shape, &output_tensor));
auto out = output_tensor->flat_inner_dims<T>();
T* out_data = out.data();
memset(out_data, 0, fixed_length * flat_in.size());
This is followed by reencoding code:
for (int64 i = 0; i < flat_in.size(); ++i) {
const T* in_data = reinterpret_cast<const T*>(flat_in(i).data());
if (flat_in(i).size() > fixed_length) {
memcpy(out_data, in_data, fixed_length);
} else {
memcpy(out_data, in_data, flat_in(i).size());
}
out_data += fixed_length;
}
The erroneous code is the last line above: it is moving the out_data pointer by fixed_length * sizeof(T) bytes whereas it only copied at most fixed_length 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.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
The vulnerability requires local access to the device to be exploited. It is easy for an attacker to exploit this vulnerability. An attacker needs basic access or low-level privileges. No user interaction is needed for the attacker to exploit this vulnerability. The impact is confined to the system where the vulnerability exists. There is a high impact on the integrity of the data. There is a high impact on the availability of the system.
Exploitation attempts have been detected. Elevated vigilance and prompt remediation are advised.
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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