Denial of Service in Tensorflow

Exploitation

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

CVSS 4.0

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

EPSS

• CVE-2020-15206: 0.96% estimated exploitation activity in the next 30 days; percentile 61.47 (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 Changing the TensorFlow's SavedModel protocol buffer and altering the name of required keys results in segfaults and data corruption while loading the model. This can cause a denial of service in products using tensorflow-serving or other inference-as-a-service installments. We have added fixes to this in f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d (both going into TensorFlow 2.2.0 and 2.3.0 but not yet backported to earlier versions). However, this was not enough, as #41097 reports a different failure mode. Patches We have patched the issue in adf095206f25471e864a8e63a0f1caef53a0e3a6 and will release patch releases for all versions between 1.15 and 2.3. Patch releases for versions between 1.15 and 2.1 will also contain cherry-picks of f760f88b4267d981e13f4b302c437ae800445968 and fcfef195637c6e365577829c4d67681695956e7d. We recommend users to upgrade to TensorFlow 1.15.4, 2.0.3, 2.1.2, 2.2.1, or 2.3.1. For more information Please…

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-w5gh-2wr2-pm6g

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-w5gh-2wr2-pm6g

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# Patch Next advisory brief: CVE-2020-15206

Advisory: Denial of Service in Tensorflow
IDs: CVE-2020-15206, GHSA-w5gh-2wr2-pm6g
Record: https://patchnext.com/advisories/cve-2020-15206 (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 7.1)
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, 2.2.1, 2.1.2, 2.0.3, 1.15.4; tensorflow-gpu 2.3.1, 2.2.1, 2.1.2, 2.0.3, 1.15.4; tensorflow-cpu 2.3.1, 2.2.1, 2.1.2, 2.0.3, 1.15.4. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI tensorflow: affected < 1.15.4; >= 2.0.0, < 2.0.3; >= 2.1.0, < 2.1.2; >= 2.2.0, <= 2.2.0; >= 2.3.0, <= 2.3.0; fixed in 2.0.3, 2.1.2, 1.15.4, 2.3.1, 2.2.1
- PyPI tensorflow-gpu: affected < 1.15.4; >= 2.0.0, < 2.0.3; >= 2.1.0, < 2.1.2; >= 2.2.0, <= 2.2.0; >= 2.3.0, <= 2.3.0; fixed in 2.0.3, 2.1.2, 1.15.4, 2.3.1, 2.2.1
- PyPI tensorflow-cpu: affected < 1.15.4; >= 2.0.0, < 2.0.3; >= 2.1.0, < 2.1.2; >= 2.2.0, <= 2.2.0; >= 2.3.0, <= 2.3.0; fixed in 2.0.3, 2.1.2, 1.15.4, 2.3.1, 2.2.1

## 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-w5gh-2wr2-pm6g (OSV record GHSA-w5gh-2wr2-pm6g lists affected ranges: PyPI tensorflow >=0 =2.0.0 =2.1.0 =2.2.0 =2.3.0 =0 =2.0.0 =2.1.0 =2.2.0 =2.3.0 =0 =2.0.0 =2.1.0 =2.2.0 =2.3.0 <2.3.1 (fixed))
- Verified: https://github.com/advisories/GHSA-w5gh-2wr2-pm6g (GitHub-reviewed advisory GHSA-w5gh-2wr2-pm6g lists affected ranges: PyPI tensorflow >=0 =2.0.0 =2.1.0 =2.2.0 =2.3.0 =0 =2.0.0 =2.1.0 =2.2.0 =2.3.0 =0 =2.0.0 =2.1.0 =2.2.0 =2.3.0 <=2.3.0)

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