Authenticated Local Privilege Escalation vulnerability in Intel Optimization for Tensorflow

Exploitation

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

CVSS

5.5 · CVSS:3.1/AV:L/AC:H/PR:L/UI:R/S:U/C:L/I:H/A:L

EPSS

• CVE-2023-27506: 0.16% estimated exploitation activity in the next 30 days; percentile 5.25 (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.

Improper buffer restrictions in the Intel(R) Optimization for Tensorflow software before version 2.12 may allow an authenticated user to potentially enable escalation of privilege via local access.

Published by the source

11 Aug 2023, 03:30 UTC

Last modified by the source

13 Jul 2026, 16:43 UTC

Source last checked

26 Oct 2026, 13:12 UTC

1. Revision 2 · amended · 26 Oct 2026

   Amended from ghsa record GHSA-m2f8-v8q4-3m59

2. Revision 1 · created · 24 Oct 2026

   Published from osv record GHSA-m2f8-v8q4-3m59

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# Patch Next advisory brief: CVE-2023-27506

Advisory: Authenticated Local Privilege Escalation vulnerability in Intel Optimization for Tensorflow
IDs: CVE-2023-27506, GHSA-m2f8-v8q4-3m59
Record: https://patchnext.com/advisories/cve-2023-27506 (revision 2, source checked 26 Oct 2026, 13:12 UTC)
Inventory: none attached. This brief comes from a public page and does not say whether any deployment is affected.
Severity: Medium (CVSS 5.5)
Exploitation: no confirmed exploitation recorded. Patch Next confirms exploitation only from CISA KEV.
Vendor response: Vendor fix available; fixed versions by package: tensorflow-intel 2.12; intel-tensorflow-avx512 2.12; intel-tensorflow 2.12. A vendor fix does not mean a deployment is fixed.

## Affected components
- PyPI tensorflow-intel: affected < 2.12; fixed in 2.12
- PyPI intel-tensorflow-avx512: affected < 2.12; fixed in 2.12
- PyPI intel-tensorflow: affected < 2.12; fixed in 2.12

## Suggested inspection (read-only)
- `python -m pip show tensorflow-intel`: prints the installed version. Run with the environment that serves production active.
- `python -m pip show intel-tensorflow-avx512`: prints the installed version. Run with the environment that serves production active.
- `python -m pip show intel-tensorflow`: 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-intel, intel-tensorflow-avx512, intel-tensorflow.
- 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-m2f8-v8q4-3m59 (OSV record GHSA-m2f8-v8q4-3m59 lists affected ranges: PyPI intel-tensorflow >=0 =0 =0 <2.12 (fixed))
- Verified: https://github.com/advisories/GHSA-m2f8-v8q4-3m59 (GitHub-reviewed advisory GHSA-m2f8-v8q4-3m59 lists affected ranges: PyPI intel-tensorflow >=0 =0 =0 <2.12)

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