QID 982533
QID 982533: Python (pip) Security Update for tensorflow-gpu (GHSA-452g-f7fp-9jf7)
Security update has been released for tensorflow,tensorflow-cpu,tensorflow-gpu to fix the vulnerability.
Note: The preceding description block is extracted directly from the security advisory. Using automation, we have attempted to clean and format it as much as possible without introducing additional issues.
Calling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences.
There are multiple ways to reproduce this, listing a few examples here:
```python
import tensorflow as tf
import numpy as np
data = tf.random.truncated_normal(shape=1,mean=np.float32(20.8739),stddev=779.973,dtype=20,seed=64)
```
```python
import tensorflow as tf
import numpy as np
data =
tf.random.stateless_truncated_normal(shape=1,seed=[63,70],mean=np.float32(20.8739),stddev=779.973,dtype=20)
```
```python
import tensorflow as tf
import numpy as np
data = tf.one_hot(indices=[62,50],depth=136,on_value=np.int32(237),off_value=158,axis=856,dtype=20)
```
```python
import tensorflow as tf
import numpy as np
data = tf.range(start=np.int32(214),limit=660,delta=129,dtype=20)
```
```python
import tensorflow as tf
import numpy as np
data = tf.raw_ops.ResourceCountUpTo(resource=np.int32(30), limit=872, T=3)
```
```python
import tensorflow as tf
import numpy as np
writer_array = np.array([1,2],dtype=np.int32)
writer_tensor = tf.convert_to_tensor(writer_array,dtype=tf.resource)
```
All these examples and similar ones have the same behavior: the [conversion from Python array to C++ array](https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L113-L169) is vulnerable to a type confusion:
```cc
int pyarray_type = PyArray_TYPE(array);
PyArray_Descr* descr = PyArray_DESCR(array);
switch (pyarray_type) {
...
case NPY_VOID:
// Quantized types are currently represented as custom struct types.
// PyArray_TYPE returns NPY_VOID for structs, and we should look into
// descr to derive the actual type.
// Direct feeds of certain types of ResourceHandles are represented as a
// custom struct type.
return PyArrayDescr_to_TF_DataType(descr, out_tf_datatype);
...
}
```
For the tensor types involved in the above example, the `pyarray_type` is `NPY_VOID` but the `descr` field is such that `descr->field = NULL`. Then [`PyArrayDescr_to_TF_DataType`](https://github.com/tensorflow/tensorflow/blob/ff70c47a396ef1e3cb73c90513da4f5cb71bebba/tensorflow/python/lib/core/ndarray_tensor.cc#L72-L77) will trigger a null dereference:
```cc
Status PyArrayDescr_to_TF_DataType(PyArray_Descr* descr,
TF_DataType* out_tf_datatype) {
PyObject* key;
PyObject* value;
Py_ssize_t pos = 0;
if (PyDict_Next(descr->fields, &pos, &key, &value)) {
...
}
}
```
This is because the Python's `PyDict_Next` implementation would dereference the first argument.
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.
- GHSA-452g-f7fp-9jf7 -
github.com/advisories/GHSA-452g-f7fp-9jf7
CVEs related to QID 982533
| Advisory ID | Software | Component | Link |
|---|---|---|---|
| GHSA-452g-f7fp-9jf7 | tensorflow |
|
|
| GHSA-452g-f7fp-9jf7 | tensorflow-cpu |
|
|
| GHSA-452g-f7fp-9jf7 | tensorflow-gpu |
|