CVE-2020-15199 is a medium-severity (CVSS 5.9) Improper Input Validation vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. A fix is available for tensorflow — see the affected versions and patch details below.
Denial of Service in Tensorflow
EPSS Exploitation Probability
Probability of exploitation in the next 30 days, from FIRST.org EPSS.
How urgent is this, really
CVE-2020-15199 by exploitation likelihood (EPSS) against impact (CVSS). Outside the shaded patch-first corner.
Where this sits among everything scored
Of 385,738 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Counts from FIRST.org, log-scaled.
Real-World Exposure
tensorflow🐍tensorflow-cpu🐍tensorflow-gpuReal-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.
Description
Impact
The RaggedCountSparseOutput does not validate that the input arguments form a valid ragged tensor. In particular, there is no validation that the splits tensor has the minimum required number of elements. Code uses this quantity to initialize a different data structure:
https://github.com/tensorflow/tensorflow/blob/0e68f4d3295eb0281a517c3662f6698992b7b2cf/tensorflow/core/kernels/count_ops.cc#L241-L244
Since BatchedMap is equivalent to a vector, it needs to have at least one element to not be nullptr. If user passes a splits tensor that is empty or has exactly one element, we get a SIGABRT signal raised by the operating system.
Patches
We have patched the issue in 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and will release a patch release.
We recommend users to upgrade to TensorFlow 2.3.1.
For more information
Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.
Attribution
This vulnerability is a variant of GHSA-p5f8-gfw5-33w4
Affected Packages
| Ecosystem | Package | Vulnerable range | Fix |
|---|---|---|---|
| 🐍PyPI | tensorflow | ≥ 2.3.0&&< 2.3.1 | 2.3.1pip install --upgrade 'tensorflow==2.3.1' |
| 🐍PyPI | tensorflow-cpu | ≥ 2.3.0&&< 2.3.1 | 2.3.1pip install --upgrade 'tensorflow-cpu==2.3.1' |
| 🐍PyPI | tensorflow-gpu | ≥ 2.3.0&&< 2.3.1 | 2.3.1pip install --upgrade 'tensorflow-gpu==2.3.1' |
Affected Products
tensorflowgoogleResearch use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.
Detection & mitigation playbook
Open-source dependencyDetect
Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow, including transitive dependencies — a direct dependency you never call can still pull in a vulnerable version.
Fix
Update tensorflow to 2.3.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2020-15199 is resolved across your whole dependency graph.
Workarounds
If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.
Frequently Asked Questions
Is CVE-2020-15199 in your dependencies?
Find it across PyPI, including transitive dependencies.