Segfault 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-2020-15200: 0.85% estimated exploitation activity in the next 30 days; percentile 58.27 (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 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. Thus, the following code sets up conditions to cause a heap buffer overflow: A BatchedMap is equivalent to a vector where each element is a hashmap. However, if the first element of splitsvalues is not 0, batchidx will never be 1, hence there will be no hashmap at index 0 in perbatchcounts. Trying to access that in the user code results in a segmentation fault. 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

Published by the source

25 Sep 2020, 18:28 UTC

Last modified by the source

8 Jul 2026, 06:28 UTC

Source last checked

26 Oct 2026, 13:25 UTC

1. Revision 3 · amended · 26 Oct 2026

   Amended from ghsa record GHSA-x7rp-74x2-mjf3

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-x7rp-74x2-mjf3

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# Patch Next advisory brief: CVE-2020-15200

Advisory: Segfault in Tensorflow
IDs: CVE-2020-15200, GHSA-x7rp-74x2-mjf3
Record: https://patchnext.com/advisories/cve-2020-15200 (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-x7rp-74x2-mjf3 (OSV record GHSA-x7rp-74x2-mjf3 lists affected ranges: PyPI tensorflow >=2.3.0 =2.3.0 =2.3.0 <2.3.1 (fixed))
- Verified: https://github.com/advisories/GHSA-x7rp-74x2-mjf3 (GitHub-reviewed advisory GHSA-x7rp-74x2-mjf3 lists affected ranges: PyPI tensorflow >=2.3.0 =2.3.0 =2.3.0 <=2.3.0)

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