CVE-2021-29513: Tensorflow Type Confusion During Tensor Casts Vulnerability
LOW
Advisory Identifier:
GHSA-452g-f7fp-9jf7
Advisory Summary
CVE-2021-29513 is a LOW (CVSS 2.5) type confusion during tensor casts vulnerability (CWE-476: NULL Pointer Dereference) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves type confusion during tensor casts lead to dereferencing null pointers. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.
CVE-2021-29513 at a Glance
CVE Identifier
CVE-2021-29513
GHSA Identifier
GHSA-452g-f7fp-9jf7
Severity
LOW
CVSS v3.1 Base Score
2.5
CWE Classification
CWE-476: NULL Pointer Dereference
Affected Product
Tensorflow
Package Ecosystem
PyPI
Published Date
Last Updated
Security Impact
CVSS: 2.5
The security impact metrics below are parsed directly from the CVSS v3.1 vector string (CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L).
Attack Vector
Local (AV:L)
Attack Complexity
High (AC:H)
Privileges Required
Low (PR:L)
User Interaction
None (UI:N)
Scope
Unchanged (S:U)
Confidentiality
None (C:N)
Integrity
None (I:N)
Availability
Low (A:L)
How to Fix CVE-2021-29513
To remediate this vulnerability, upgrade all affected Tensorflow dependencies to a patched release. Both direct and transitive dependencies should be checked, as frameworks or security libraries may pull in vulnerable versions transitively.
tensorflow
fixed: 2.1.4
pip install tensorflow>=2.1.4
tensorflow
fixed: 2.2.3
pip install tensorflow>=2.2.3
tensorflow
fixed: 2.3.3
pip install tensorflow>=2.3.3
tensorflow
fixed: 2.4.2
pip install tensorflow>=2.4.2
tensorflow-cpu
fixed: 2.1.4
pip install tensorflow-cpu>=2.1.4
tensorflow-cpu
fixed: 2.2.3
pip install tensorflow-cpu>=2.2.3
tensorflow-cpu
fixed: 2.3.3
pip install tensorflow-cpu>=2.3.3
tensorflow-cpu
fixed: 2.4.2
pip install tensorflow-cpu>=2.4.2
tensorflow-gpu
fixed: 2.1.4
pip install tensorflow-gpu>=2.1.4
tensorflow-gpu
fixed: 2.2.3
pip install tensorflow-gpu>=2.2.3
tensorflow-gpu
fixed: 2.3.3
pip install tensorflow-gpu>=2.3.3
tensorflow-gpu
fixed: 2.4.2
pip install tensorflow-gpu>=2.4.2
Verify Direct and Transitive Dependencies
Run the following command in your project directory to verify that all resolved dependencies use the patched release:
pip show tensorflowHow to Check if You Are Affected
To determine whether your Python environment is affected by CVE-2021-29513, inspect your installed packages using pip:
pip show tensorflowAnalyze the terminal output to locate any version of Tensorflow in your dependency tree. If any resolved version matches the vulnerable ranges listed in the table below (for example, versions earlier than 2.1.4), your application is affected.
Note: The pip show tensorflow command checks currently installed packages in your active Python environment.
Technical Details
Vulnerability Mechanism & Technical Analysis
This vulnerability is classified under CWE-476: NULL Pointer Dereference, where the product dereferences a pointer that it expects to be valid but is null. In Tensorflow, software state validation or input boundaries are circumvented during processing. Review the official source advisory statement below for specific affected components, classes, and reproduction details.
Source Advisory Statement
### 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.
CVE-2021-29513 Affected Tensorflow Versions
12 component(s)
Fixed Versions:
2.1.42.2.32.3.32.4.2
| Product / Package | Ecosystem | Affected Versions | Fixed Version |
|---|---|---|---|
|
tensorflow
|
PyPI |
< 2.1.4
|
2.1.4
|
|
tensorflow
|
PyPI |
≥ 2.2.0
< 2.2.3
|
2.2.3
|
|
tensorflow
|
PyPI |
≥ 2.3.0
< 2.3.3
|
2.3.3
|
|
tensorflow
|
PyPI |
≥ 2.4.0
< 2.4.2
|
2.4.2
|
|
tensorflow-cpu
|
PyPI |
< 2.1.4
|
2.1.4
|
|
tensorflow-cpu
|
PyPI |
≥ 2.2.0
< 2.2.3
|
2.2.3
|
|
tensorflow-cpu
|
PyPI |
≥ 2.3.0
< 2.3.3
|
2.3.3
|
|
tensorflow-cpu
|
PyPI |
≥ 2.4.0
< 2.4.2
|
2.4.2
|
|
tensorflow-gpu
|
PyPI |
< 2.1.4
|
2.1.4
|
|
tensorflow-gpu
|
PyPI |
≥ 2.2.0
< 2.2.3
|
2.2.3
|
|
tensorflow-gpu
|
PyPI |
≥ 2.3.0
< 2.3.3
|
2.3.3
|
|
tensorflow-gpu
|
PyPI |
≥ 2.4.0
< 2.4.2
|
2.4.2
|
References
GitHub Advisory / Wiki
ADVISORY
https://github.com/tensorflow/tensorflow/security/advisories/GHSA-452g-f7fp-9jf7
NVD — CVE-2021-29513
NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-29513
Security Fix Commit
FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/030af767d357d1b4088c4a25c72cb3906abac489
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-441.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-639.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-150.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/tensorflow/tensorflow
Frequently Asked Questions
What is CVE-2021-29513?
CVE-2021-29513 is a security vulnerability in Tensorflow involving Type confusion during tensor casts lead to dereferencing null pointers under CWE-476: NULL Pointer Dereference.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2021-29513?
Upgrading to 2.1.4, 2.2.3, 2.3.3, 2.4.2 (or the latest patched release for your respective branch) resolves the vulnerability.
Is tensorflow affected?
Yes, tensorflow is affected in releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade your dependency to 2.1.4, 2.2.3, 2.3.3, 2.4.2 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.1.4, 2.2.3, 2.3.3, 2.4.2. Upgrade your dependency to 2.1.4, 2.2.3, 2.3.3, 2.4.2 or later.
What is the CVSS score of CVE-2021-29513?
CVE-2021-29513 has a CVSS v3.1 base score of 2.5 (LOW) with vector string CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L.
What CWE is associated with CVE-2021-29513?
CVE-2021-29513 is classified under CWE-476: NULL Pointer Dereference (The product dereferences a pointer that it expects to be valid but is NULL.).
How can I check PyPI dependencies for CVE-2021-29513?
Run the dependency tree command: 'pip show tensorflow'. This command lists direct and transitive dependencies in your build configuration so you can verify if a vulnerable version is resolved.
Machine-readable OSV JSON
OSV v1.5 Schema
Normalized schema output
{
"schema_version" : "1.4.0",
"id" : "GHSA-452g-f7fp-9jf7",
"modified" : "2024-10-30T23:13:43Z",
"published" : "2021-05-21T14:20:46Z",
"aliases" : [ "CVE-2021-29513" ],
"summary" : "Type confusion during tensor casts lead to dereferencing null pointers",
"details" : "### Impact\nCalling TF operations with tensors of non-numeric types when the operations expect numeric tensors result in null pointer dereferences.\n\nThere are multiple ways to reproduce this, listing a few examples here:\n\n```python\nimport tensorflow as tf\nimport numpy as np\ndata = tf.random.truncated_normal(shape=1,mean=np.float32(20.8739),stddev=779.973,dtype=20,seed=64)\n```\n\n```python\nimport tensorflow as tf\nimport numpy as np\ndata =\ntf.random.stateless_truncated_normal(shape=1,seed=[63,70],mean=np.float32(20.8739),stddev=779.973,dtype=20)\n```\n\n```python\nimport tensorflow as tf\nimport numpy as np\ndata = tf.one_hot(indices=[62,50],depth=136,on_value=np.int32(237),off_value=158,axis=856,dtype=20)\n```\n\n```python\nimport tensorflow as tf\nimport numpy as np\ndata = tf.range(start=np.int32(214),limit=660,delta=129,dtype=20)\n```\n\n```python\nimport tensorflow as tf\nimport numpy as np\ndata = tf.raw_ops.ResourceCountUpTo(resource=np.int32(30), limit=872, T=3)\n```\n\n```python\nimport tensorflow as tf\nimport numpy as np\n\nwriter_array = np.array([1,2],dtype=np.int32)\nwriter_tensor = tf.convert_to_tensor(writer_array,dtype=tf.resource)\n```\n\nAll 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:\n\n```cc\n int pyarray_type = PyArray_TYPE(array);\n PyArray_Descr* descr = PyArray_DESCR(array);\n switch (pyarray_type) {\n ...\n case NPY_VOID:\n // Quantized types are currently represented as custom struct types.\n // PyArray_TYPE returns NPY_VOID for structs, and we should look into\n // descr to derive the actual type.\n // Direct feeds of certain types of ResourceHandles are represented as a\n // custom struct type.\n return PyArrayDescr_to_TF_DataType(descr, out_tf_datatype);\n ...\n }\n```\n\nFor 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:\n\n```cc\nStatus PyArrayDescr_to_TF_DataType(PyArray_Descr* descr,\n TF_DataType* out_tf_datatype) {\n PyObject* key;\n PyObject* value;\n Py_ssize_t pos = 0;\n if (PyDict_Next(descr->fields, &pos, &key, &value)) {\n ...\n }\n}\n```\n\nThis is because the Python's `PyDict_Next` implementation would dereference the first argument.\n\n### Patches\nWe have patched the issue in GitHub commit [030af767d357d1b4088c4a25c72cb3906abac489](https://github.com/tensorflow/tensorflow/commit/030af767d357d1b4088c4a25c72cb3906abac489).\n\nThe 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.\n\n### For more information\nPlease 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.\n\n### Attribution\nThis 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.",
"severity" : [ {
"type" : "CVSS_V3",
"score" : "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L"
}, {
"type" : "CVSS_V4",
"score" : "CVSS:4.0/AV:L/AC:L/AT:P/PR:L/UI:N/VC:N/VI:N/VA:L/SC:N/SI:N/SA:N"
} ],
"affected" : [ {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "2.1.4"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
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"events" : [ {
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}, {
"fixed" : "2.2.3"
} ]
} ]
}, {
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"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
"fixed" : "2.3.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
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"events" : [ {
"introduced" : "2.4.0"
}, {
"fixed" : "2.4.2"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
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}, {
"fixed" : "2.1.4"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.2.0"
}, {
"fixed" : "2.2.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
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}, {
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},
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"events" : [ {
"introduced" : "2.4.0"
}, {
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} ]
} ]
}, {
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},
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"type" : "ECOSYSTEM",
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}, {
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} ]
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}, {
"package" : {
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},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.2.0"
}, {
"fixed" : "2.2.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
"fixed" : "2.3.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
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},
"ranges" : [ {
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"events" : [ {
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}, {
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} ]
} ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-452g-f7fp-9jf7"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29513"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/030af767d357d1b4088c4a25c72cb3906abac489"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-441.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-639.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-150.yaml"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/tensorflow/tensorflow"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-476" ],
"severity" : "LOW",
"github_reviewed" : true,
"github_reviewed_at" : "2021-05-18T23:42:09Z",
"nvd_published_at" : "2021-05-14T20:15:00Z"
}
}