GitHub Advisoryadvisory · PyPI

GHSA-3w67-q784-6w7c Division by zero in TFLite's implementation of `GatherNd`

LOW Published 2021-05-21 Modified 2024-10-31 Official page ↗
Aliases
CVE-2021-29589
Ecosystem
PyPI
Packages
tensorflow, tensorflow-cpu, tensorflow-gpu
Severity
Low
CVSS vector
CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L
Weaknesses
CWE-369
Reviewed
2021-05-18

Affected packages

• PyPI tensorflow: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-cpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-cpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-cpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3 • PyPI tensorflow-cpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-gpu: < 2.1.4 → fixed in 2.1.4 • PyPI tensorflow-gpu: >= 2.2.0, < 2.2.3 → fixed in 2.2.3 • PyPI tensorflow-gpu: >= 2.4.0, < 2.4.2 → fixed in 2.4.2 • PyPI tensorflow-gpu: >= 2.3.0, < 2.3.3 → fixed in 2.3.3

Description

Impact The reference implementation of the `GatherNd` TFLite operator is vulnerable to a division by zero error: ```cc ret.dims_to_count[i] = remain_flat_size / params_shape.Dims(i); ``` An attacker can craft a model such that `params` input would be an empty tensor. In turn, `params_shape.Dims(.)` would be zero, in at least one dimension. Patches We have patched the issue in GitHub commit 8e45822aa0b9f5df4b4c64f221e64dc930a70a9d. 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.

Related

Alias · CVE (1)

  • CVE-2021-29589 TensorFlow is an end-to-end open source platform for machine learning. vulnerability

Weakness · CWE (1)

References