CVE-2021-37680 json
Division by zero in TFLite in TensorFlow
published 2021-08-12 5 y ago modified 2026-09-08 2 mo ago also BIT-tensorflow-2021-37680 , EUVD-2021-0406 , GHSA-cfpj-3q4c-jhvr , PYSEC-2021-302 , PYSEC-2021-593 , PYSEC-2021-791
- cvss
- 5.5
- version 3.1, CNA
- cisa kev
- Not listed
- not in CISA's catalog
- epss
- 0.2%
- EPSS 0.2% chance of exploitation in 30 days, percentile 4%
- public exploits
- 0
- none known
Summary
TensorFlow is an end-to-end open source platform for machine learning. In affected versions the implementation of fully connected layers in TFLite is [vulnerable to a division by zero error](https://github.com/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.cc#L226). We have patched the issue in GitHub commit 718721986aa137691ee23f03638867151f74935f. 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.
Patch / fix
- pypi tensorflow 2.3.4, 2.4.3, 2.5.1
- pypi tensorflow-cpu 2.3.4, 2.4.3, 2.5.1
- pypi tensorflow-gpu 2.3.4, 2.4.3, 2.5.1
- bitnami tensorflow 2.3.4, 2.4.3, 2.5.1
- google tensorflow 2.3.4, 2.4.3
- tensorflow2 Package Hub 15 SP3, openSUSE Leap 15.3 2.6.0-bp153.2.3.1
- bazel3.7 Package Hub 15 SP3, openSUSE Leap 15.3 3.7.2-bp153.4.1
- tensorflow2-lite Package Hub 15 SP3, openSUSE Leap 15.3 2.6.0-bp153.2.3.1
- bazel-skylib1.0.3 Package Hub 15 SP3, openSUSE Leap 15.3 1.0.3-bp153.2.1
- tensorflow2_2_6_0-gnu-hpc Package Hub 15 SP3, openSUSE Leap 15.3 2.6.0-bp153.2.3.1
- tensorflow2_2_6_0-gnu-openmpi2-hpc Package Hub 15 SP3, openSUSE Leap 15.3 2.6.0-bp153.2.3.1
opensuse advisories openSUSE-SU-2022:10014-1
- tensorflow Arch Linux 2.5.1-1
arch linux advisories AVG-2292
Exploit prediction
0.15% chance of exploitation in the next 30 days, higher than 4% of all scored CVEs (FIRST EPSS, 2026-11-08).
CVSS scores
| assigned by | version | score | vector |
|---|---|---|---|
| CNA | 3.1 | 5.5 | CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H |
| GitHub | 3.1 | 5.5 | CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H |
| NVD | 3.1 | 5.5 | CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H |
| NVD | 2.0 | 2.1 | AV:L/AC:L/Au:N/C:N/I:N/A:P |
| OSV | 3.1 | 5.5 | CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:N/I:N/A:H |
Affected packages
| ecosystem | package | affected | fixed in |
|---|---|---|---|
| pypi | tensorflow | <2.3.4 >=2.4.0, <2.4.3 >=2.5.0, <2.5.1 >=2.3.0, <2.3.4 |
2.3.4 2.4.3 2.5.1 |
| pypi | tensorflow-cpu | <2.3.4 >=2.4.0, <2.4.3 >=2.5.0, <2.5.1 >=2.3.0, <2.3.4 |
2.3.4 2.4.3 2.5.1 |
| pypi | tensorflow-gpu | <2.3.4 >=2.4.0, <2.4.3 >=2.5.0, <2.5.1 >=2.3.0, <2.3.4 |
2.3.4 2.4.3 2.5.1 |
| bitnami | tensorflow | >=2.3.0, <2.3.4 >=2.4.0, <2.4.3 >=2.5.0, <2.5.1 |
2.3.4 2.4.3 2.5.1 |
Affected products
- tensorflow tensorflow
Classification
- Weakness
-
- CWE-369 Divide By Zero
References
-
github.com
/tensorflow/tensorflow/commit/718721986aa137691ee23f03638867151f74935f
web, patch, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/security/advisories/GHSA-cfpj-3q4c-jhvr
web, third-party-advisory, advisory
-
nvd.nist.gov
/vuln/detail/CVE-2021-37680
advisory, web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-593.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-791.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-302.yaml
web
-
github.com
/tensorflow/tensorflow
package
-
github.com
/tensorflow/tensorflow/blob/460e000de3a83278fb00b61a16d161b1964f15f4/tensorflow/lite/kernels/fully_connected.c
web
Sources
- CVE List CVE-2021-37680
- GitHub Advisory GHSA-cfpj-3q4c-jhvr
- NVD CVE-2021-37680
- OSV BIT-tensorflow-2021-37680
- OSV PYSEC-2021-302
- OSV PYSEC-2021-593
- OSV PYSEC-2021-791
Fencer Vulns keeps one entry per vulnerability: the CVE id wins, then GHSA, and records that alias each other are merged, except that two CVEs always keep separate entries. The same data is available as JSON.