CVE-2020-15202: Integer truncation in Shard API usage
First published: Fri Sep 25 2020(Updated: )
### Impact The `Shard` API in TensorFlow expects the last argument to be a function taking two `int64` (i.e., `long long`) arguments: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/util/work_sharder.h#L59-L60 However, there are several places in TensorFlow where a lambda taking `int` or `int32` arguments is being used: https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L204-L205 https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/random_op.cc#L317-L318 In these cases, if the amount of work to be parallelized is large enough, integer truncation occurs. Depending on how the two arguments of the lambda are used, this can result in segfaults, read/write outside of heap allocated arrays, stack overflows, or data corruption. ### Patches We have patched the issue in 27b417360cbd671ef55915e4bb6bb06af8b8a832 and ca8c013b5e97b1373b3bb1c97ea655e69f31a575. We will release patch releases for all versions between 1.15 and 2.3. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. ### 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 |
|---|---|---|
| pip/tensorflow-gpu | =2.3.0 | 2.3.1 |
| pip/tensorflow-gpu | =2.2.0 | 2.2.1 |
| pip/tensorflow-gpu | >=2.1.0<2.1.2 | 2.1.2 |
| pip/tensorflow-gpu | >=2.0.0<2.0.3 | 2.0.3 |
| pip/tensorflow-gpu | <1.15.4 | 1.15.4 |
| pip/tensorflow-cpu | =2.3.0 | 2.3.1 |
| pip/tensorflow-cpu | =2.2.0 | 2.2.1 |
| pip/tensorflow-cpu | >=2.1.0<2.1.2 | 2.1.2 |
| pip/tensorflow-cpu | >=2.0.0<2.0.3 | 2.0.3 |
| pip/tensorflow-cpu | <1.15.4 | 1.15.4 |
| pip/tensorflow | =2.3.0 | 2.3.1 |
| pip/tensorflow | =2.2.0 | 2.2.1 |
| pip/tensorflow | >=2.1.0<2.1.2 | 2.1.2 |
| pip/tensorflow | >=2.0.0<2.0.3 | 2.0.3 |
| pip/tensorflow | <1.15.4 | 1.15.4 |
| TensorFlow Keras | <1.15.4 | |
| TensorFlow Keras | >=2.0.0<2.0.3 | |
| TensorFlow Keras | >=2.1.0<2.1.2 | |
| TensorFlow Keras | >=2.2.0<2.2.1 | |
| TensorFlow Keras | >=2.3.0<2.3.1 | |
| SUSE Linux | =15.2 |
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Reference Links
- http://lists.opensuse.org/opensuse-security-announce/2020-10/msg00065.html
- https://github.com/tensorflow/tensorflow/commit/27b417360cbd671ef55915e4bb6bb06af8b8a832
- https://github.com/tensorflow/tensorflow/commit/ca8c013b5e97b1373b3bb1c97ea655e69f31a575
- https://github.com/tensorflow/tensorflow/releases/tag/v2.3.1
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-h6fg-mjxg-hqq4
- https://nvd.nist.gov/vuln/detail/CVE-2020-15202
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-282.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-317.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-125.yaml
- https://github.com/advisories/GHSA-h6fg-mjxg-hqq4 (Advisory)
Frequently Asked Questions
What is the severity of CVE-2020-15202?
CVE-2020-15202 is classified as a high-severity vulnerability in the TensorFlow Shard API.
How do I fix CVE-2020-15202?
To mitigate CVE-2020-15202, update TensorFlow to versions 2.3.1, 2.2.1, 2.1.2, 2.0.3, or 1.15.4.
Which versions of TensorFlow are affected by CVE-2020-15202?
CVE-2020-15202 affects TensorFlow versions 2.3.0, 2.2.0, 2.1.0, 2.0.0 and all versions leading up to 1.15.4.
Who is impacted by CVE-2020-15202?
Developers utilizing affected TensorFlow versions may be exposed to vulnerabilities when using the Shard API.
What functionalities does the vulnerability CVE-2020-15202 impact?
CVE-2020-15202 impacts the behavior of the Shard API where an incorrect function argument type could lead to execution issues.
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