CVE-2021-29529: Tensorflow Heap Buffer Overflow Caused By Vulnerability
LOW
Advisory Identifier:
GHSA-jfp7-4j67-8r3q
Advisory Summary
CVE-2021-29529 is a LOW (CVSS 2.5) heap buffer overflow caused by vulnerability (CWE-131: Incorrect Calculation of Buffer Size) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves heap buffer overflow caused by rounding. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.
CVE-2021-29529 at a Glance
CVE Identifier
CVE-2021-29529
GHSA Identifier
GHSA-jfp7-4j67-8r3q
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-2021-29529
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-2021-29529, 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-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
An attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements:
```python
import tensorflow as tf
l = [256, 328, 361, 17, 361, 361, 361, 361, 361, 361, 361, 361, 361, 361, 384]
images = tf.constant(l, shape=[1, 1, 15, 1], dtype=tf.qint32)
size = tf.constant([12, 6], shape=[2], dtype=tf.int32)
min = tf.constant(80.22522735595703)
max = tf.constant(80.39215850830078)
tf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max,
align_corners=True, half_pixel_centers=True)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value:
```cc
const float in_f = std::floor(in);
interpolation->lower[i] = std::max(static_cast<int64>(in_f), static_cast<int64>(0));
interpolation->upper[i] = std::min(static_cast<int64>(std::ceil(in)), in_size - 1);
```
For some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, [in the interpolation code](https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow:
```cc
template <int RESOLUTION, typename T, typename T_SCALE, typename T_CALC>
inline void OutputLerpForChannels(const InterpolationCache<T_SCALE>& xs,
const int64 x, const T_SCALE ys_ilerp,
const int channels, const float min,
const float max, const T* ys_input_lower_ptr,
const T* ys_input_upper_ptr,
T* output_y_ptr) {
const int64 xs_lower = xs.lower[x];
...
for (int c = 0; c < channels; ++c) {
const T top_left = ys_input_lower_ptr[xs_lower + c];
...
}
}
```
For the other cases where `interpolation->upper[i]` is smaller than `interpolation->lower[i]`, we can set them to be equal without affecting the output.
### Patches
We have patched the issue in GitHub commit [f851613f8f0fb0c838d160ced13c134f778e3ce7](https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7).
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-2021-29529 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-jfp7-4j67-8r3q
NVD — CVE-2021-29529
NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-29529
Security Fix Commit
FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-457.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-655.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-166.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/tensorflow/tensorflow
Frequently Asked Questions
What is CVE-2021-29529?
CVE-2021-29529 is a security vulnerability in Tensorflow involving Heap buffer overflow caused by rounding under CWE-131: Incorrect Calculation of Buffer Size.
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-2021-29529?
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-2021-29529?
CVE-2021-29529 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-2021-29529?
CVE-2021-29529 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-2021-29529?
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-jfp7-4j67-8r3q",
"modified" : "2024-10-30T23:19:12Z",
"published" : "2021-05-21T14:22:05Z",
"aliases" : [ "CVE-2021-29529" ],
"summary" : "Heap buffer overflow caused by rounding",
"details" : "### Impact\nAn attacker can trigger a heap buffer overflow in `tf.raw_ops.QuantizedResizeBilinear` by manipulating input values so that float rounding results in off-by-one error in accessing image elements:\n\n```python\nimport tensorflow as tf\n\nl = [256, 328, 361, 17, 361, 361, 361, 361, 361, 361, 361, 361, 361, 361, 384]\nimages = tf.constant(l, shape=[1, 1, 15, 1], dtype=tf.qint32)\nsize = tf.constant([12, 6], shape=[2], dtype=tf.int32)\nmin = tf.constant(80.22522735595703)\nmax = tf.constant(80.39215850830078)\n\ntf.raw_ops.QuantizedResizeBilinear(images=images, size=size, min=min, max=max,\n align_corners=True, half_pixel_centers=True)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L62-L66) computes two integers (representing the upper and lower bounds for interpolation) by ceiling and flooring a floating point value:\n\n```cc\nconst float in_f = std::floor(in);\ninterpolation->lower[i] = std::max(static_cast<int64>(in_f), static_cast<int64>(0));\ninterpolation->upper[i] = std::min(static_cast<int64>(std::ceil(in)), in_size - 1);\n```\n \nFor some values of `in`, `interpolation->upper[i]` might be smaller than `interpolation->lower[i]`. This is an issue if `interpolation->upper[i]` is capped at `in_size-1` as it means that `interpolation->lower[i]` points outside of the image. Then, [in the interpolation code](https://github.com/tensorflow/tensorflow/blob/44b7f486c0143f68b56c34e2d01e146ee445134a/tensorflow/core/kernels/quantized_resize_bilinear_op.cc#L245-L264), this would result in heap buffer overflow:\n\n```cc\ntemplate <int RESOLUTION, typename T, typename T_SCALE, typename T_CALC>\ninline void OutputLerpForChannels(const InterpolationCache<T_SCALE>& xs,\n const int64 x, const T_SCALE ys_ilerp,\n const int channels, const float min,\n const float max, const T* ys_input_lower_ptr,\n const T* ys_input_upper_ptr,\n T* output_y_ptr) {\n const int64 xs_lower = xs.lower[x];\n ...\n for (int c = 0; c < channels; ++c) {\n const T top_left = ys_input_lower_ptr[xs_lower + c];\n ...\n }\n}\n```\n\nFor the other cases where `interpolation->upper[i]` is smaller than `interpolation->lower[i]`, we can set them to be equal without affecting the output.\n\n### Patches\nWe have patched the issue in GitHub commit [f851613f8f0fb0c838d160ced13c134f778e3ce7](https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7).\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"
} ],
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},
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} ]
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}, {
"package" : {
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},
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}, {
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},
"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",
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},
"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",
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},
"ranges" : [ {
"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-jfp7-4j67-8r3q"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29529"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/f851613f8f0fb0c838d160ced13c134f778e3ce7"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-457.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-655.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-166.yaml"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/tensorflow/tensorflow"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-131", "CWE-193" ],
"severity" : "LOW",
"github_reviewed" : true,
"github_reviewed_at" : "2021-05-18T23:06:25Z",
"nvd_published_at" : "2021-05-14T20:15:00Z"
}
}