Skip to content
TensorFlowGHSA-452g-f7fp-9jf7

Type confusion during tensor casts lead to dereferencing null pointers

Low2.5CVE-2021-29513 · Published May 21, 2021 · updated Jul 8, 2026

### Impact 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:...

GitHub advisory

Affected versions

PackageAffectedFixed in
tensorflow
PyPI
< 2.1.42.1.4
>= 2.2.0, < 2.2.32.2.3
>= 2.3.0, < 2.3.32.3.3
>= 2.4.0, < 2.4.22.4.2
Details and references

### Impact 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. ### Patches We have patched the issue in GitHub commit [030af767d357d1b4088c4a25c72cb3906abac489](https://github.com/tensorflow/tensorflow/commit/030af767d357d1b4088c4a25c72cb3906abac489). 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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) for more information regarding the security model and how to contact us with issues and questions. ### Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360 as well as Ye Zhang and Yakun Zhang of Baidu X-Team.

CVSS 3.1
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Severity from
GitHub (reviewed advisory)
Weakness
CWE-476
Also known as
BIT-tensorflow-2021-29513, CVE-2021-29513, PYSEC-2021-150, PYSEC-2021-441, PYSEC-2021-639

More TensorFlow advisories

All TensorFlow
Advisory
Segfault in `tf.raw_ops.SparseCountSparseOutput`
Low2.5May 21, 2021
Crash in `tf.transpose` with complex inputs
Low2.5May 21, 2021
Crash in `tf.strings.substr` due to `CHECK`-fail
Low2.5May 21, 2021
Null dereference in Grappler's `TrySimplify`
Low2.5May 21, 2021
Stack overflow in `ParseAttrValue` with nested tensors
Low2.5May 21, 2021
Interpreter crash from `tf.io.decode_raw`
Medium7.1May 21, 2021

Critical advisories by email

Wednesdays: the week’s critical and high advisories in the AI and data stack, with the fixed versions. Only in weeks that have some.

Double opt-in. Unsubscribe any time.