CVE-2021-29521: Tensorflow Segfault Vulnerability

LOW
Advisory Identifier: GHSA-hr84-fqvp-48mm
CVSS 2.5
CWE-131
Published
Advisory Summary

CVE-2021-29521 is a LOW (CVSS 2.5) segfault vulnerability (CWE-131: Incorrect Calculation of Buffer Size) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves segfault in sparsecountsparseoutput. Remediation is available by upgrading affected packages to version 2.3.3, 2.4.2.

CVE-2021-29521 at a Glance

CVE Identifier CVE-2021-29521
GHSA Identifier GHSA-hr84-fqvp-48mm
Severity LOW
CVSS v3.1 Base Score 2.5
CWE Classification CWE-131: Incorrect Calculation of Buffer Size
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-2021-29521

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: 2.3.3
pip install tensorflow>=2.3.3
tensorflow fixed: 2.4.2
pip install tensorflow>=2.4.2
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: 2.3.3
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 tensorflow

How to Check if You Are Affected

To determine whether your Python environment is affected by CVE-2021-29521, inspect your installed packages using pip:

pip show tensorflow

Analyze 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 2.3.3), 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-131: Incorrect Calculation of Buffer Size, where the product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow. 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 Specifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken. ```python import tensorflow as tf indices = tf.constant([], shape=[0, 0], dtype=tf.int64) values = tf.constant([], shape=[0, 0], dtype=tf.int64) dense_shape = tf.constant([-100, -100, -100], shape=[3], dtype=tf.int64) weights = tf.constant([], shape=[0, 0], dtype=tf.int64) tf.raw_ops.SparseCountSparseOutput(indices=indices, values=values, dense_shape=dense_shape, weights=weights, minlength=79, maxlength=96, binary_output=False) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., [`std::vector<absl::flat_hash_map<int64,T>>`](https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure. ```cc bool is_1d = shape.NumElements() == 1; int num_batches = is_1d ? 1 : shape.flat<int64>()(0); ... auto per_batch_counts = BatchedMap<W>(num_batches); ``` If the `shape` tensor has more than one element, `num_batches` is the first value in `shape`. Ensuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue. ### Patches We have patched the issue in GitHub commit [c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5](https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5). The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3. ### 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 Yakun Zhang and Ying Wang of Baidu X-Team.

CVE-2021-29521 Affected Tensorflow Versions

6 component(s)
Fixed Versions:
2.3.32.4.2
Product / Package Ecosystem Affected Versions Fixed Version
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
≥ 2.3.0
< 2.3.3
2.3.3
tensorflow-cpu
PyPI
≥ 2.4.0
< 2.4.2
2.4.2
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

Frequently Asked Questions

What is CVE-2021-29521?
CVE-2021-29521 is a security vulnerability in Tensorflow involving Segfault in SparseCountSparseOutput under CWE-131: Incorrect Calculation of Buffer Size.
Which Tensorflow versions are affected?
Affected versions include releases prior to 2.3.3, 2.4.2 across affected packages: tensorflow, tensorflow-cpu, tensorflow-gpu.
What version fixes CVE-2021-29521?
Upgrading to 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 2.3.3, 2.4.2. Upgrade your dependency to 2.3.3, 2.4.2 or later.
Is tensorflow-cpu affected?
Yes, tensorflow-cpu is affected in releases prior to 2.3.3, 2.4.2. Upgrade your dependency to 2.3.3, 2.4.2 or later.
What is the CVSS score of CVE-2021-29521?
CVE-2021-29521 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-2021-29521?
CVE-2021-29521 is classified under CWE-131: Incorrect Calculation of Buffer Size (The product does not correctly calculate the size to be used when allocating a buffer, which could lead to a buffer overflow.).
How can I check PyPI dependencies for CVE-2021-29521?
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-hr84-fqvp-48mm",
  "modified" : "2024-10-28T21:21:03Z",
  "published" : "2021-05-21T14:21:16Z",
  "aliases" : [ "CVE-2021-29521" ],
  "summary" : "Segfault in SparseCountSparseOutput",
  "details" : "### Impact\nSpecifying a negative dense shape in `tf.raw_ops.SparseCountSparseOutput` results in a segmentation fault being thrown out from the standard library as `std::vector` invariants are broken.\n\n```python\nimport tensorflow as tf\n\nindices = tf.constant([], shape=[0, 0], dtype=tf.int64)\nvalues = tf.constant([], shape=[0, 0], dtype=tf.int64)\ndense_shape = tf.constant([-100, -100, -100], shape=[3], dtype=tf.int64)\nweights = tf.constant([], shape=[0, 0], dtype=tf.int64)\n\ntf.raw_ops.SparseCountSparseOutput(indices=indices, values=values, dense_shape=dense_shape, weights=weights, minlength=79, maxlength=96, binary_output=False)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L199-L213) assumes the first element of the dense shape is always positive and uses it to initialize a `BatchedMap<T>` (i.e., [`std::vector<absl::flat_hash_map<int64,T>>`](https://github.com/tensorflow/tensorflow/blob/8f7b60ee8c0206a2c99802e3a4d1bb55d2bc0624/tensorflow/core/kernels/count_ops.cc#L27)) data structure.\n\n```cc\n  bool is_1d = shape.NumElements() == 1;\n  int num_batches = is_1d ? 1 : shape.flat<int64>()(0);\n  ...\n  auto per_batch_counts = BatchedMap<W>(num_batches); \n```\n\nIf the `shape` tensor has more than one element, `num_batches` is the first value in `shape`.\n                       \nEnsuring that the `dense_shape` argument is a valid tensor shape (that is, all elements are non-negative) solves this issue.\n\n### Patches\nWe have patched the issue in GitHub commit [c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5](https://github.com/tensorflow/tensorflow/commit/c57c0b9f3a4f8684f3489dd9a9ec627ad8b599f5).\n\nThe fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2 and TensorFlow 2.3.3.\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 Yakun Zhang and Ying Wang of Baidu X-Team.",
  "severity" : [ {
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    "score" : "CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L"
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  }, {
    "type" : "ADVISORY",
    "url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29521"
  }, {
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  "database_specific" : {
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}