CVE-2021-37646: Bad alloc in `StringNGrams` caused by integer conversion in TensorFlow
First published: Thu Aug 12 2021(Updated: )
### Impact The implementation of `tf.raw_ops.StringNGrams` is vulnerable to an integer overflow issue caused by converting a signed integer value to an unsigned one and then allocating memory based on this value. ```python import tensorflow as tf tf.raw_ops.StringNGrams( data=['',''], data_splits=[0,2], separator=' '*100, ngram_widths=[-80,0,0,-60], left_pad=' ', right_pad=' ', pad_width=100, preserve_short_sequences=False) ``` The [implementation](https://github.com/tensorflow/tensorflow/blob/8d72537c6abf5a44103b57b9c2e22c14f5f49698/tensorflow/core/kernels/string_ngrams_op.cc#L184) calls `reserve` on a `tstring` with a value that sometimes can be negative if user supplies negative `ngram_widths`. The `reserve` method calls `TF_TString_Reserve` which has an `unsigned long` argument for the size of the buffer. Hence, the implicit conversion transforms the negative value to a large integer. ### Patches We have patched the issue in GitHub commit [c283e542a3f422420cfdb332414543b62fc4e4a5](https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5). The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, 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 members of the Aivul Team from Qihoo 360.
Credit: security-advisories@github.com security-advisories@github.com
| Affected Software | Affected Version | How to fix |
|---|---|---|
| Google TensorFlow | >=2.3.0<2.3.4 | |
| Google TensorFlow | >=2.4.0<2.4.3 | |
| Google TensorFlow | =2.5.0 | |
| Google TensorFlow | =2.6.0-rc0 | |
| Google TensorFlow | =2.6.0-rc1 | |
| Google TensorFlow | =2.6.0-rc2 | |
| pip/tensorflow-gpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-gpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-gpu | <2.3.4 | 2.3.4 |
| pip/tensorflow-cpu | =2.5.0 | 2.5.1 |
| pip/tensorflow-cpu | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow-cpu | <2.3.4 | 2.3.4 |
| pip/tensorflow | =2.5.0 | 2.5.1 |
| pip/tensorflow | >=2.4.0<2.4.3 | 2.4.3 |
| pip/tensorflow | <2.3.4 | 2.3.4 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/c283e542a3f422420cfdb332414543b62fc4e4a5
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6jh-7gv5-28vg
- https://nvd.nist.gov/vuln/detail/CVE-2021-37646
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-559.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-757.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-268.yaml
- https://github.com/advisories/GHSA-h6jh-7gv5-28vg (Advisory)
Frequently Asked Questions
What is the severity of CVE-2021-37646?
CVE-2021-37646 has been assigned a high severity rating due to its potential to lead to integer overflow and memory corruption.
How do I fix CVE-2021-37646?
You can fix CVE-2021-37646 by upgrading to TensorFlow version 2.5.1 or later, or TensorFlow GPU version 2.4.3.
Which versions of TensorFlow are affected by CVE-2021-37646?
CVE-2021-37646 affects TensorFlow versions 2.3.0 to 2.3.4, 2.4.0 to 2.4.3, 2.5.0, and pre-release versions of 2.6.0.
What impact does CVE-2021-37646 have on applications using TensorFlow?
CVE-2021-37646 could result in application crashes or arbitrary code execution due to memory allocation issues.
Is there a CVE-2021-37646 workaround available?
There are no specific workarounds for CVE-2021-37646 other than applying the recommended updates.
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