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Medium, CVSS 6.5amended

Data leak in Tensorflow

CVE-2020-15205GHSA-g7p5-5759-qv46

What to do

Vendor fix available. Upgrade each affected package to the fixed version on its release line (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), redeploy, then verify what is actually installed.

This page has no inventory attached, so it cannot say whether you are affected. A vendor fix is not proof that your deployment is fixed.

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Affected packages

  • 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

Sources

Severity and exploitation
Exploitation
No confirmed exploitation recorded. Patch Next marks exploitation only from CISA KEV.
CVSS 4.0
6.5 · CVSS:4.0/AV:N/AC:L/AT:N/PR:N/UI:P/VC:N/VI:N/VA:N/SC:H/SI:H/SA:H · score source
EPSS
  • CVE-2020-15205: 1.03% estimated exploitation activity in the next 30 days; percentile 63.72 (FIRST EPSS, 2026-11-03). This is a model estimate, not proof of exploitation or customer exposure.
Source description

As published by the source; long descriptions are shortened.

Impact The datasplits argument of tf.rawops.StringNGrams lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory All the binary strings after ee ff are contents from the memory stack. Since these can contain return addresses, this data leak can be used to defeat ASLR. Patches We have patched the issue in 0462de5b544ed4731aa2fb23946ac22c01856b80 and will release patch releases for all versions between 1.15 and 2.3. 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 consult our security guide for more information regarding the security model and how to contact us with issues and questions. Attribution This vulnerability has been reported by members of the Aivul Team from Qihoo 360.

Dates and revisions
Published by the source
Last modified by the source
Source last checked
  1. Revision 3 · amended ·

    Amended from ghsa record GHSA-g7p5-5759-qv46

  2. Revision 2 · amended ·

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

  3. Revision 1 · created ·

    Published from osv record GHSA-g7p5-5759-qv46

Agent brief

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# Patch Next advisory brief: CVE-2020-15205

Advisory: Data leak in Tensorflow
IDs: CVE-2020-15205, GHSA-g7p5-5759-qv46
Record: https://patchnext.com/advisories/cve-2020-15205 (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: Medium (CVSS 6.5)
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-g7p5-5759-qv46 (OSV record GHSA-g7p5-5759-qv46 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-g7p5-5759-qv46 (GitHub-reviewed advisory GHSA-g7p5-5759-qv46 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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