CVE-2021-29601: Integer overflow in TFLite concatentation
First published: Fri May 14 2021(Updated: )
### Impact The TFLite implementation of concatenation is [vulnerable to an integer overflow issue](https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76): ```cc for (int d = 0; d < t0->dims->size; ++d) { if (d == axis) { sum_axis += t->dims->data[axis]; } else { TF_LITE_ENSURE_EQ(context, t->dims->data[d], t0->dims->data[d]); } } ``` An attacker can craft a model such that the dimensions of one of the concatenation input overflow the values of `int`. TFLite uses `int` to represent tensor dimensions, whereas TF uses `int64`. Hence, valid TF models can trigger an integer overflow when converted to TFLite format. ### Patches We have patched the issue in GitHub commit [4253f96a58486ffe84b61c0415bb234a4632ee73](https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73). 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 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 | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-gpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-gpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-gpu | <2.1.4 | 2.1.4 |
| pip/tensorflow-cpu | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow-cpu | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow-cpu | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow-cpu | <2.1.4 | 2.1.4 |
| pip/tensorflow | >=2.4.0<2.4.2 | 2.4.2 |
| pip/tensorflow | >=2.3.0<2.3.3 | 2.3.3 |
| pip/tensorflow | >=2.2.0<2.2.3 | 2.2.3 |
| pip/tensorflow | <2.1.4 | 2.1.4 |
| TensorFlow Keras | <2.1.4 | |
| TensorFlow Keras | >=2.2.0<2.2.3 | |
| TensorFlow Keras | >=2.3.0<2.3.3 | |
| TensorFlow Keras | >=2.4.0<2.4.2 |
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Reference Links
- https://github.com/tensorflow/tensorflow/commit/4253f96a58486ffe84b61c0415bb234a4632ee73
- https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9c84-4hx6-xmm4
- https://nvd.nist.gov/vuln/detail/CVE-2021-29601
- https://github.com/tensorflow/tensorflow/blob/7b7352a724b690b11bfaae2cd54bc3907daf6285/tensorflow/lite/kernels/concatenation.cc#L70-L76
- https://github.com/advisories/GHSA-9c84-4hx6-xmm4 (Advisory)
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-529.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-727.yaml
- https://github.com/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-238.yaml
Frequently Asked Questions
What is the severity of CVE-2021-29601?
CVE-2021-29601 has a severity rating that could lead to integer overflow vulnerabilities, potentially compromising application stability.
How do I fix CVE-2021-29601?
To fix CVE-2021-29601, upgrade TensorFlow to version 2.4.2 or later.
Which versions of TensorFlow are affected by CVE-2021-29601?
CVE-2021-29601 affects TensorFlow versions prior to 2.4.2 including 2.1.4, 2.2.0 to 2.2.3, 2.3.0 to 2.3.3, and all versions before 2.4.0.
What type of vulnerability is CVE-2021-29601?
CVE-2021-29601 is identified as an integer overflow vulnerability in the TensorFlow Lite concatenation implementation.
Is CVE-2021-29601 present in both CPU and GPU versions of TensorFlow?
Yes, CVE-2021-29601 is present in both the CPU and GPU versions of TensorFlow.
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