CVE-2020-15201 json
Heap buffer overflow in Tensorflow
published 6y ago modified 2mo ago also BIT-tensorflow-2020-15201 , EUVD-2020-0197 , GHSA-p5f8-gfw5-33w4 , PYSEC-2020-124 , PYSEC-2020-281 , PYSEC-2020-316
- cvss
- 4.8
- version 3.1, CNA
- cisa kev
- Not listed
- not in CISA's catalog
- epss
- 0.6%
- EPSS 0.6% chance of exploitation in 30 days, percentile 47%
- public exploits
- 0
- none known
Summary
In Tensorflow before version 2.3.1, the `RaggedCountSparseOutput` implementation does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the values in the `splits` tensor generate a valid partitioning of the `values` tensor. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. 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
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Check with FencerExploit prediction
0.57% chance of exploitation in the next 30 days, higher than 47% of all scored CVEs (FIRST EPSS, 2026-11-09).
Exploits and checks
- github.com https://github.com/tensorflow/tensorflow/security/advisories/GHSA-p5f8-gfw5-33w4 referenced PoC, tagged "exploit" by a source, unverified
CVSS scores
| assigned by | version | score | vector |
|---|---|---|---|
| CNA | 3.1 | 4.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N |
| GitHub | 3.1 | 4.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N |
| NVD | 3.1 | 4.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:L/I:L/A:N |
| NVD | 2.0 | 6.8 | AV:N/AC:M/Au:N/C:P/I:P/A:P |
| OSV | 3.1 | 4.8 | CVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/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-p5f8-gfw5-33w4
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-2020-15201
advisory, web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-cpu/PYSEC-2020-281.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow-gpu/PYSEC-2020-316.yaml
web
-
github.com
/pypa/advisory-database/tree/main/vulns/tensorflow/PYSEC-2020-124.yaml
web
-
github.com
/tensorflow/tensorflow
package
Sources
- CVE List CVE-2020-15201
- GitHub Advisory GHSA-p5f8-gfw5-33w4
- NVD CVE-2020-15201
- OSV BIT-tensorflow-2020-15201
- OSV PYSEC-2020-124
- OSV PYSEC-2020-281
- OSV PYSEC-2020-316
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.