CVE-2020-15198 json
Heap buffer overflow in Tensorflow
published 2020-09-25 6 y ago modified 2026-09-10 1 mo ago also BIT-tensorflow-2020-15198 , EUVD-2020-0194 , GHSA-jc87-6vpp-7ff3 , PYSEC-2020-121 , PYSEC-2020-278 , PYSEC-2020-313
- cvss
- 5.4
- version 3.1, CNA
- cisa kev
- Not listed
- not in CISA's catalog
- epss
- 0.5%
- EPSS 0.5% chance of exploitation in 30 days, percentile 45%
- public exploits
- 0
- none known
Summary
In Tensorflow before version 2.3.1, the `SparseCountSparseOutput` implementation does not validate that the input arguments form a valid sparse tensor. In particular, there is no validation that the `indices` tensor has the same shape as the `values` one. The values in these tensors are always accessed in parallel. Thus, a shape mismatch can result in accesses outside the bounds of heap allocated buffers. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.
Patch / fix
- pypi tensorflow 2.3.1
- pypi tensorflow-cpu 2.3.1
- pypi tensorflow-gpu 2.3.1
- bitnami tensorflow 2.3.1
- google tensorflow 2.3.1
Exploit prediction
0.54% chance of exploitation in the next 30 days, higher than 45% of all scored CVEs (FIRST EPSS, 2026-11-08).
Exploits and checks
- github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3 referenced PoC, tagged "exploit" by a source, unverified
CVSS scores
| assigned by | version | score | vector |
|---|---|---|---|
| CNA | 3.1 | 5.4 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:N |
| GitHub | 3.1 | 5.4 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:N |
| NVD | 3.1 | 5.4 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:N |
| NVD | 2.0 | 5.8 | AV:N/AC:M/Au:N/C:P/I:P/A:N |
| OSV | 3.1 | 5.4 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:C/C:L/I:L/A:N |
Affected packages
| ecosystem | package | affected | fixed in |
|---|---|---|---|
| pypi | tensorflow | >=2.3.0, <2.3.1 <2.3.1 |
2.3.1 |
| pypi | tensorflow-cpu | >=2.3.0, <2.3.1 <2.3.1 |
2.3.1 |
| pypi | tensorflow-gpu | >=2.3.0, <2.3.1 <2.3.1 |
2.3.1 |
| bitnami | tensorflow | >=2.3.0, <2.3.1 |
2.3.1 |
Affected products
- tensorflow tensorflow
Classification
References
-
github.com
/tensorflow/tensorflow/commit/3cbb917b4714766030b28eba9fb41bb97ce9ee02
web, patch, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/security/advisories/GHSA-jc87-6vpp-7ff3
web, exploit, third-party-advisory, advisory
-
github.com
/tensorflow/tensorflow/releases/tag/v2.3.1
web, third-party-advisory, advisory
-
nvd.nist.gov
/vuln/detail/CVE-2020-15198
advisory, web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-278.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-313.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-121.yaml
web
-
github.com
/tensorflow/tensorflow
package
Sources
- CVE List CVE-2020-15198
- GitHub Advisory GHSA-jc87-6vpp-7ff3
- NVD CVE-2020-15198
- OSV BIT-tensorflow-2020-15198
- OSV PYSEC-2020-121
- OSV PYSEC-2020-278
- OSV PYSEC-2020-313
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.