CVE-2021-29573 json
Division by 0 in `MaxPoolGradWithArgmax`
published 5y ago modified 2mo ago also BIT-tensorflow-2021-29573 , EUVD-2021-0314 , GHSA-9vpm-rcf4-9wqw , PYSEC-2021-210 , PYSEC-2021-501 , PYSEC-2021-699
- cvss
- 2.5
- 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 8%
- public exploits
- 0
- none known
Summary
TensorFlow is an end-to-end open source platform for machine learning. The implementation of `tf.raw_ops.MaxPoolGradWithArgmax` is vulnerable to a division by 0. The implementation(https://github.com/tensorflow/tensorflow/blob/279bab6efa22752a2827621b7edb56a730233bd8/tensorflow/core/kernels/maxpooling_op.cc#L1033-L1034) fails to validate that the batch dimension of the tensor is non-zero, before dividing by this quantity. 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.
Patch / fix
- pypi tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2
- pypi tensorflow-cpu 2.1.4, 2.2.3, 2.3.3, 2.4.2
- pypi tensorflow-gpu 2.1.4, 2.2.3, 2.3.3, 2.4.2
- bitnami tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2
- google tensorflow 2.1.4, 2.2.3, 2.3.3, 2.4.2
- tensorflow Arch Linux 2.5.0-1
arch linux advisories AVG-1962
Is CVE-2021-29573 in your dependencies? Fencer shows which of your repositories are affected as soon as an advisory lands, and opens the fix as a pull request.
Check with FencerExploit prediction
0.19% chance of exploitation in the next 30 days, higher than 8% of all scored CVEs (FIRST EPSS, 2026-11-09).
Exploits and checks
- github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-9vpm-rcf4-9wqw referenced PoC, tagged "exploit" by a source, unverified
CVSS scores
| assigned by | version | score | vector |
|---|---|---|---|
| CNA | 3.1 | 2.5 | CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L |
| GitHub | 3.1 | 2.5 | CVSS:3.1/AV:L/AC:H/PR:L/UI:N/S:U/C:N/I:N/A:L |
| 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 | <2.1.4 >=2.2.0, <2.2.3 >=2.3.0, <2.3.3 >=2.4.0, <2.4.2 |
2.1.4 2.2.3 2.3.3 2.4.2 |
| pypi | tensorflow-cpu | <2.1.4 >=2.2.0, <2.2.3 >=2.3.0, <2.3.3 >=2.4.0, <2.4.2 |
2.1.4 2.2.3 2.3.3 2.4.2 |
| pypi | tensorflow-gpu | <2.1.4 >=2.2.0, <2.2.3 >=2.3.0, <2.3.3 >=2.4.0, <2.4.2 |
2.1.4 2.2.3 2.3.3 2.4.2 |
| bitnami | tensorflow | <2.1.4 >=2.2.0, <2.2.3 >=2.3.0, <2.3.3 >=2.4.0, <2.4.2 |
2.1.4 2.2.3 2.3.3 2.4.2 |
Affected products
- tensorflow tensorflow
Classification
- Weakness
-
- CWE-369 Divide By Zero
References
-
github.com
/tensorflow/tensorflow/commit/376c352a37ce5a68b721406dc7e77ac4b6cf483d
web, patch, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/security/advisories/GHSA-9vpm-rcf4-9wqw
web, exploit, patch, third-party-advisory, advisory
-
nvd.nist.gov
/vuln/detail/CVE-2021-29573
advisory, web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-501.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-699.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-210.yaml
web
-
github.com
/tensorflow/tensorflow
package
Sources
- CVE List CVE-2021-29573
- GitHub Advisory GHSA-9vpm-rcf4-9wqw
- NVD CVE-2021-29573
- OSV BIT-tensorflow-2021-29573
- OSV PYSEC-2021-210
- OSV PYSEC-2021-501
- OSV PYSEC-2021-699
Fencer Intel 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.