CVE-2021-29540 json
Heap buffer overflow in `Conv2DBackpropFilter`
published 2021-05-14 5 y ago modified 2026-09-08 2 mo ago also BIT-tensorflow-2021-29540 , EUVD-2021-0281 , GHSA-xgc3-m89p-vr3x , PYSEC-2021-177 , PYSEC-2021-468 , PYSEC-2021-666
- 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 12%
- public exploits
- 0
- none known
Summary
TensorFlow is an end-to-end open source platform for machine learning. An attacker can cause a heap buffer overflow to occur in `Conv2DBackpropFilter`. This is because the implementation(https://github.com/tensorflow/tensorflow/blob/1b0296c3b8dd9bd948f924aa8cd62f87dbb7c3da/tensorflow/core/kernels/conv_grad_filter_ops.cc#L495-L497) computes the size of the filter tensor but does not validate that it matches the number of elements in `filter_sizes`. Later, when reading/writing to this buffer, code uses the value computed here, instead of the number of elements in the tensor. 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, 2.2.0rc0, 2.2.3, 2.3.0rc0, 2.3.3, 2.3.4, 2.4.2, 2.4.3
- pypi tensorflow-cpu 2.1.4, 2.2.0rc0, 2.2.3, 2.3.0rc0, 2.3.3, 2.3.4, 2.4.2, 2.4.3
- pypi tensorflow-gpu 2.1.4, 2.2.0rc0, 2.2.3, 2.3.0rc0, 2.3.3, 2.3.4, 2.4.2, 2.4.3
- 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
Exploit prediction
0.21% chance of exploitation in the next 30 days, higher than 12% of all scored CVEs (FIRST EPSS, 2026-11-08).
Exploits and checks
- github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-xgc3-m89p-vr3x 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 | 7.8 | CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H |
| NVD | 2.0 | 4.6 | AV:L/AC:L/Au:N/C:P/I:P/A:P |
| OSV | 3.1 | 7.8 | CVSS:3.1/AV:L/AC:L/PR:L/UI:N/S:U/C:H/I:H/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 <2.2.0rc0 >=2.2.0, <2.3.0rc0 >=2.3.0, <2.3.4 >=2.4.0, <2.4.3 |
2.1.4 2.2.3 2.3.3 2.4.2 2.2.0rc0 2.3.0rc0 2.3.4 2.4.3 |
| pypi | tensorflow-cpu | <2.1.4 >=2.2.0, <2.2.3 >=2.3.0, <2.3.3 >=2.4.0, <2.4.2 <2.2.0rc0 >=2.2.0, <2.3.0rc0 >=2.3.0, <2.3.4 >=2.4.0, <2.4.3 |
2.1.4 2.2.3 2.3.3 2.4.2 2.2.0rc0 2.3.0rc0 2.3.4 2.4.3 |
| pypi | tensorflow-gpu | <2.1.4 >=2.2.0, <2.2.3 >=2.3.0, <2.3.3 >=2.4.0, <2.4.2 <2.2.0rc0 >=2.2.0, <2.3.0rc0 >=2.3.0, <2.3.4 >=2.4.0, <2.4.3 |
2.1.4 2.2.3 2.3.3 2.4.2 2.2.0rc0 2.3.0rc0 2.3.4 2.4.3 |
| 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
References
-
github.com
/tensorflow/tensorflow/commit/c570e2ecfc822941335ad48f6e10df4e21f11c96
web, patch, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/security/advisories/GHSA-xgc3-m89p-vr3x
web, exploit, third-party-advisory, advisory
-
nvd.nist.gov
/vuln/detail/CVE-2021-29540
advisory, web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2021-468.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2021-666.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2021-177.yaml
web
-
github.com
/tensorflow/tensorflow
package
Sources
- CVE List CVE-2021-29540
- GitHub Advisory GHSA-xgc3-m89p-vr3x
- NVD CVE-2021-29540
- OSV BIT-tensorflow-2021-29540
- OSV PYSEC-2021-177
- OSV PYSEC-2021-468
- OSV PYSEC-2021-666
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.