CVE-2021-29530: Tensorflow Invalid Validation Vulnerability
LOW
Advisory Identifier:
GHSA-xcwj-wfcm-m23c
Advisory Summary
CVE-2021-29530 is a LOW (CVSS 2.5) invalid validation vulnerability (CWE-476: NULL Pointer Dereference) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves invalid validation in `sparsematrixsparsecholesky`. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.
CVE-2021-29530 at a Glance
CVE Identifier
CVE-2021-29530
GHSA Identifier
GHSA-xcwj-wfcm-m23c
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-2021-29530
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-2021-29530, 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
An attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`:
```python
import tensorflow as tf
import numpy as np
from tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops
indices_array = np.array([[0, 0]])
value_array = np.array([-10.0], dtype=np.float32)
dense_shape = [1, 1]
st = tf.SparseTensor(indices_array, value_array, dense_shape)
input = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(
st.indices, st.values, st.dense_shape)
permutation = tf.constant([], shape=[1, 0], dtype=tf.int32)
tf.raw_ops.SparseMatrixSparseCholesky(input=input, permutation=permutation, type=tf.float32)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments:
```cc
void Compute(OpKernelContext* ctx) final {
...
const Tensor& input_permutation_indices = ctx->input(1);
...
ValidateInputs(ctx, *input_matrix, input_permutation_indices, &batch_size, &num_rows);
...
}
void ValidateInputs(OpKernelContext* ctx,
const CSRSparseMatrix& sparse_matrix,
const Tensor& permutation_indices, int* batch_size,
int64* num_rows) {
OP_REQUIRES(ctx, sparse_matrix.dtype() == DataTypeToEnum<T>::value, ...)
...
}
```
Although `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since [`OP_REQUIRES`](https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function.
```cc
#define OP_REQUIRES(CTX, EXP, STATUS) \
do { \
if (!TF_PREDICT_TRUE(EXP)) { \
CheckNotInComputeAsync((CTX), "OP_REQUIRES_ASYNC"); \
(CTX)->CtxFailure(__FILE__, __LINE__, (STATUS)); \
return; \
} \
} while (0)
```
Thus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`.
### Patches
We have patched the issue in GitHub commit [e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd](https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd).
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 Ying Wang and Yakun Zhang of Baidu X-Team.
CVE-2021-29530 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-xcwj-wfcm-m23c
NVD — CVE-2021-29530
NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-29530
Security Fix Commit
FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-458.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-656.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-167.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/tensorflow/tensorflow
Frequently Asked Questions
What is CVE-2021-29530?
CVE-2021-29530 is a security vulnerability in Tensorflow involving Invalid validation in `SparseMatrixSparseCholesky` 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-2021-29530?
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-2021-29530?
CVE-2021-29530 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-2021-29530?
CVE-2021-29530 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-2021-29530?
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-xcwj-wfcm-m23c",
"modified" : "2024-10-30T23:19:46Z",
"published" : "2021-05-21T14:22:09Z",
"aliases" : [ "CVE-2021-29530" ],
"summary" : "Invalid validation in `SparseMatrixSparseCholesky`",
"details" : "### Impact\nAn attacker can trigger a null pointer dereference by providing an invalid `permutation` to `tf.raw_ops.SparseMatrixSparseCholesky`:\n\n```python\nimport tensorflow as tf\nimport numpy as np\nfrom tensorflow.python.ops.linalg.sparse import sparse_csr_matrix_ops\n\nindices_array = np.array([[0, 0]])\nvalue_array = np.array([-10.0], dtype=np.float32)\ndense_shape = [1, 1]\nst = tf.SparseTensor(indices_array, value_array, dense_shape)\n\ninput = sparse_csr_matrix_ops.sparse_tensor_to_csr_sparse_matrix(\n st.indices, st.values, st.dense_shape)\n\npermutation = tf.constant([], shape=[1, 0], dtype=tf.int32)\n \ntf.raw_ops.SparseMatrixSparseCholesky(input=input, permutation=permutation, type=tf.float32)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/kernels/sparse/sparse_cholesky_op.cc#L85-L86) fails to properly validate the input arguments: \n \n```cc \nvoid Compute(OpKernelContext* ctx) final {\n ...\n const Tensor& input_permutation_indices = ctx->input(1);\n ...\n ValidateInputs(ctx, *input_matrix, input_permutation_indices, &batch_size, &num_rows);\n ...\n}\n\nvoid ValidateInputs(OpKernelContext* ctx,\n const CSRSparseMatrix& sparse_matrix,\n const Tensor& permutation_indices, int* batch_size,\n int64* num_rows) {\n OP_REQUIRES(ctx, sparse_matrix.dtype() == DataTypeToEnum<T>::value, ...)\n ...\n}\n```\nAlthough `ValidateInputs` is called and there are checks in the body of this function, the code proceeds to the next line in `ValidateInputs` since [`OP_REQUIRES`](https://github.com/tensorflow/tensorflow/blob/080f1d9e257589f78b3ffb75debf584168aa6062/tensorflow/core/framework/op_requires.h#L41-L48) is a macro that only exits the current function.\n\n```cc\n#define OP_REQUIRES(CTX, EXP, STATUS) \\\n do { \\\n if (!TF_PREDICT_TRUE(EXP)) { \\\n CheckNotInComputeAsync((CTX), \"OP_REQUIRES_ASYNC\"); \\\n (CTX)->CtxFailure(__FILE__, __LINE__, (STATUS)); \\\n return; \\\n } \\\n } while (0)\n```\n\nThus, the first validation condition that fails in `ValidateInputs` will cause an early return from that function. However, the caller will continue execution from the next line. The fix is to either explicitly check `context->status()` or to convert `ValidateInputs` to return a `Status`.\n\n### Patches\nWe have patched the issue in GitHub commit [e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd](https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd).\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 Ying Wang 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" : [ {
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} ]
} ]
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"name" : "tensorflow"
},
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},
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"name" : "tensorflow"
},
"ranges" : [ {
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"introduced" : "2.4.0"
}, {
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} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
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"type" : "ECOSYSTEM",
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}, {
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} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
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} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
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} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.4.0"
}, {
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} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
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} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.2.0"
}, {
"fixed" : "2.2.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
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"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
"fixed" : "2.3.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.4.0"
}, {
"fixed" : "2.4.2"
} ]
} ]
} ],
"references" : [ {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xcwj-wfcm-m23c"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29530"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/e6a7c7cc18c3aaad1ae0872cb0a959f5c923d2bd"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-458.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-656.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-167.yaml"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/tensorflow/tensorflow"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-476" ],
"severity" : "LOW",
"github_reviewed" : true,
"github_reviewed_at" : "2021-05-18T23:03:26Z",
"nvd_published_at" : "2021-05-14T20:15:00Z"
}
}