CVE-2020-15266 json
Undefined behavior in Tensorflow
published 2020-10-21 6 y ago modified 2026-09-08 2 mo ago also BIT-tensorflow-2020-15266 , EUVD-2020-0212 , GHSA-xwhf-g6j5-j5gc , PYSEC-2020-139 , PYSEC-2020-296 , PYSEC-2020-331
- cvss
- 3.7
- version 3.1, CNA
- cisa kev
- Not listed
- not in CISA's catalog
- epss
- 0.9%
- EPSS 0.9% chance of exploitation in 30 days, percentile 61%
- public exploits
- 0
- none known
Summary
In Tensorflow before version 2.4.0, when the `boxes` argument of `tf.image.crop_and_resize` has a very large value, the CPU kernel implementation receives it as a C++ `nan` floating point value. Attempting to operate on this is undefined behavior which later produces a segmentation fault. The issue is patched in eccb7ec454e6617738554a255d77f08e60ee0808 and TensorFlow 2.4.0 will be released containing the patch. TensorFlow nightly packages after this commit will also have the issue resolved.
Patch / fix
- pypi tensorflow 2.4.0
- pypi tensorflow-cpu 2.4.0
- pypi tensorflow-gpu 2.4.0
- bitnami tensorflow 2.4.0
- google tensorflow 2.4.0
- tensorflow Arch Linux 2.4.0rc4-1
arch linux advisories AVG-1350
Exploit prediction
0.94% chance of exploitation in the next 30 days, higher than 61% of all scored CVEs (FIRST EPSS, 2026-11-08).
Exploits and checks
- github.com https://github.com/tensorflow/tensorflow/issues/42129 referenced PoC, tagged "exploit" by a source, unverified
CVSS scores
| assigned by | version | score | vector |
|---|---|---|---|
| CNA | 3.1 | 3.7 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:L |
| GitHub | 3.1 | 3.7 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:L |
| NVD | 3.1 | 7.5 | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H |
| NVD | 2.0 | 5.0 | AV:N/AC:L/Au:N/C:N/I:N/A:P |
| OSV | 3.1 | 7.5 | CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H |
Affected packages
| ecosystem | package | affected | fixed in |
|---|---|---|---|
| pypi | tensorflow | <2.4.0 |
2.4.0 |
| pypi | tensorflow-cpu | <2.4.0 |
2.4.0 |
| pypi | tensorflow-gpu | <2.4.0 |
2.4.0 |
| bitnami | tensorflow | <2.4.0 |
2.4.0 |
Affected products
- tensorflow tensorflow
Classification
- Weakness
-
- CWE-119 Improper Restriction of Operations within the Bounds of a Memory Buffer
References
-
github.com
/tensorflow/tensorflow/pull/42143/commits/3ade2efec2e90c6237de32a19680caaa3ebc2845
web, patch, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/security/advisories/GHSA-xwhf-g6j5-j5gc
web, patch, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/issues/42129
web, exploit, patch, third-party-advisory, advisory, report
-
nvd.nist.gov
/vuln/detail/CVE-2020-15266
advisory, web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-296.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-331.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-139.yaml
web
-
github.com
/tensorflow/tensorflow
package
Sources
- CVE List CVE-2020-15266
- GitHub Advisory GHSA-xwhf-g6j5-j5gc
- NVD CVE-2020-15266
- OSV BIT-tensorflow-2020-15266
- OSV PYSEC-2020-139
- OSV PYSEC-2020-296
- OSV PYSEC-2020-331
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.