CVE-2020-15197 json
Denial of Service in Tensorflow
published 2020-09-25 6 y ago modified 2026-09-08 2 mo ago also BIT-tensorflow-2020-15197 , EUVD-2020-0193 , GHSA-qc53-44cj-vfvx , PYSEC-2020-120 , PYSEC-2020-277 , PYSEC-2020-312
- cvss
- 6.3
- version 3.1, CNA
- cisa kev
- Not listed
- not in CISA's catalog
- epss
- 0.7%
- EPSS 0.7% chance of exploitation in 30 days, percentile 54%
- 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 rank 2. This tensor must be a matrix because code assumes its elements are accessed as elements of a matrix. However, malicious users can pass in tensors of different rank, resulting in a `CHECK` assertion failure and a crash. This can be used to cause denial of service in serving installations, if users are allowed to control the components of the input sparse tensor. 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
Exploit prediction
0.73% chance of exploitation in the next 30 days, higher than 54% of all scored CVEs (FIRST EPSS, 2026-11-08).
Exploits and checks
- github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-qc53-44cj-vfvx referenced PoC, tagged "exploit" by a source, unverified
CVSS scores
| assigned by | version | score | vector |
|---|---|---|---|
| CNA | 3.1 | 6.3 | CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H |
| GitHub | 3.1 | 6.3 | CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H |
| NVD | 3.1 | 6.3 | CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H |
| NVD | 2.0 | 3.5 | AV:N/AC:M/Au:S/C:N/I:N/A:P |
| OSV | 3.1 | 6.3 | CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:N/I:N/A:H |
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-qc53-44cj-vfvx
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-2020-15197
advisory, web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-277.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-312.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-120.yaml
web
-
github.com
/tensorflow/tensorflow
package
Sources
- CVE List CVE-2020-15197
- GitHub Advisory GHSA-qc53-44cj-vfvx
- NVD CVE-2020-15197
- OSV BIT-tensorflow-2020-15197
- OSV PYSEC-2020-120
- OSV PYSEC-2020-277
- OSV PYSEC-2020-312
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.