CVE-2021-29601: Integer overflow in TFLite concatentation

Published May 14, 2021
·
Updated

Impact The TFLite implementation of concatenation is vulnerable to an integer overflow issue:

cc for (int d = 0; d < t0->dims->size; ++d) { if (d == axis) { sumaxis += t->dims->data[axis]; } else { TFLITEENSUREEQ(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.

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 members of the Aivul Team from Qihoo 360.

Other sources

TensorFlow is an end-to-end open source platform for machine learning. 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). 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. 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.

Affected Software

16 affected componentsFixes available
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
Google TensorFlow<2.1.4
Google TensorFlow>=2.2.0<2.2.3
Google TensorFlow>=2.3.0<2.3.3
Google TensorFlow>=2.4.0<2.4.2

Event History

May 14, 2021
CVE Published
via MITRE·07:21 PM
Data Sourced
via MITRE·07:21 PM
DescriptionSeverityWeakness
May 21, 2021
Advisory Published
via GitHub·02:28 PM

Frequently Asked Questions

1

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.

2

How do I fix CVE-2021-29601?

To fix CVE-2021-29601, upgrade TensorFlow to version 2.4.2 or later.

3

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.

4

What type of vulnerability is CVE-2021-29601?

CVE-2021-29601 is identified as an integer overflow vulnerability in the TensorFlow Lite concatenation implementation.

5

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.

Contact

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