Denial of Service in Tensorflow

Exploitation

No confirmed exploitation recorded. Patch Next marks exploitation only from CISA KEV.

CVSS 4.0

8.2 · CVSS:4.0/AV:N/AC:L/AT:P/PR:N/UI:N/VC:N/VI:N/VA:H/SC:N/SI:N/SA:N · score source

EPSS

• CVE-2020-15199: 0.81% estimated exploitation activity in the next 30 days; percentile 57.01 (FIRST EPSS, 2026-11-03). This is a model estimate, not proof of exploitation or customer exposure.

As published by the source; long descriptions are shortened.

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/countops.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…

Published by the source

25 Sep 2020, 18:28 UTC

Last modified by the source

8 Jul 2026, 06:29 UTC

Source last checked

26 Oct 2026, 13:25 UTC

1. Revision 3 · amended · 26 Oct 2026

   Amended from ghsa record GHSA-x5cp-9pcf-pp3h

2. Revision 2 · amended · 25 Oct 2026

   Reviewed OSV alias/range/CVSS4 correction; package source scopes and original publication preserved. No customer finding resolved.

3. Revision 1 · created · 24 Oct 2026

   Published from osv record GHSA-x5cp-9pcf-pp3h

Preview the exact text before you copy it. Commands are read-only suggestions; review them before running anything.

# Patch Next advisory brief: CVE-2020-15199

Advisory: Denial of Service in Tensorflow
IDs: CVE-2020-15199, GHSA-x5cp-9pcf-pp3h
Record: https://patchnext.com/advisories/cve-2020-15199 (revision 3, source checked 26 Oct 2026, 13:25 UTC)
Inventory: none attached. This brief comes from a public page and does not say whether any deployment is affected.
Severity: High (CVSS 8.2)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: tensorflow 2.3.1; tensorflow-gpu 2.3.1; tensorflow-cpu 2.3.1. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI tensorflow: affected >= 2.3.0, <= 2.3.0; fixed in 2.3.1
- PyPI tensorflow-gpu: affected >= 2.3.0, <= 2.3.0; fixed in 2.3.1
- PyPI tensorflow-cpu: affected >= 2.3.0, <= 2.3.0; fixed in 2.3.1

## Suggested inspection (read-only)
- `python -m pip show tensorflow`: prints the installed version. Run with the environment that serves production active.
- `python -m pip show tensorflow-gpu`: prints the installed version. Run with the environment that serves production active.
- `python -m pip show tensorflow-cpu`: prints the installed version. Run with the environment that serves production active.

## Supported action
Upgrade each affected component to a fixed version on its release line. For a transitive dependency, upgrade the parent package or add an override or constraint, then regenerate the lockfile. Redeploy every environment that ran an affected version.

## Required regression checks
- Run the test suite and a production build.
- Exercise the features that depend on tensorflow, tensorflow-gpu, tensorflow-cpu.
- Repeat the inspection and confirm that only fixed versions remain installed.

## Rollback considerations
- Keep the previous lockfile and deployment artifact. Rolling back reinstates the vulnerable version, so apply any documented mitigation while you fix forward.

## Unresolved questions
- Which deployments run an affected version?
- Exploitation status can change. Check again before lowering the priority.
- Mark the work resolved only after the fixed version is verified in production.

## Sources
- Verified: https://osv.dev/vulnerability/GHSA-x5cp-9pcf-pp3h (OSV record GHSA-x5cp-9pcf-pp3h lists affected ranges: PyPI tensorflow >=2.3.0 =2.3.0 =2.3.0 <2.3.1 (fixed))
- Verified: https://github.com/advisories/GHSA-x5cp-9pcf-pp3h (GitHub-reviewed advisory GHSA-x5cp-9pcf-pp3h lists affected ranges: PyPI tensorflow >=2.3.0 =2.3.0 =2.3.0 <=2.3.0)

Commands are suggestions: review each one before running it.