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Not in CISA KEV
HIGH severity

CVE-2020-15193

HIGHFix: tensorflow/tensorflow@22e07fb

CVE-2020-15193 is a high-severity (CVSS 7.1) CWE-908 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether CVE-2020-15193 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Memory corruption in Tensorflow

Also known asBIT-tensorflow-2020-15193GHSA-rjjg-hgv6-h69vPYSEC-2020-116PYSEC-2020-273PYSEC-2020-308
Published
Sep 25, 2020
Updated
Aug 7, 2026
Affected
6 pkgs
Patched
6 / 6
Exploits
1 known
Exploitation data as of Aug 7, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Real-World Exposure

6 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu

Real-time download stats are indexed for npm and PyPI packages. This vulnerability affects PyPI packages — download data is not available via public APIs for these ecosystems.

Description

In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of dlpack.to_dlpack can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a reinterpret_cast Since the PyObject is a Python object, not a TensorFlow Tensor, the cast to EagerTensor fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in TensorFlow versions 2.2.1, or 2.3.1.

Affected Packages

6 total 6 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflow2.2.0&&< 2.2.12.2.1
🐍PyPItensorflow2.3.0&&< 2.3.12.3.1
🐍PyPItensorflow-cpu2.2.0&&< 2.2.12.2.1
🐍PyPItensorflow-cpu2.3.0&&< 2.3.12.3.1
🐍PyPItensorflow-gpu2.2.0&&< 2.2.12.2.1
🐍PyPItensorflow-gpu2.3.0&&< 2.3.12.3.1
Exploits & PoCs
1

Research use only. For defensive security, authorized penetration testing, and academic research only. Never execute exploit code against systems without explicit written authorization.

Detection & mitigation playbook

Open-source dependency
  1. Detect

    Scan your dependency tree (package-lock.json, pnpm-lock.yaml, requirements.txt, go.sum, etc.) for tensorflow. O3's reachability analysis confirms whether the vulnerable code path is actually invoked in your application, so you act on real exposure instead of every transitive match.

  2. Fix

    Update tensorflow to 2.2.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2020-15193 is resolved across your whole dependency graph.

  3. Workarounds

    If you can't upgrade right away: gate or disable the affected feature, validate untrusted input at the boundary, and avoid passing attacker-controlled data into the vulnerable path. O3's runtime protection blocks exploitation in production as an interim safeguard until the upgrade lands.

  4. How O3 protects you

    O3 pinpoints whether CVE-2020-15193 is reachable in your code and exactly where to fix it, then blocks exploitation in production at runtime until the patched version is deployed.

Tailored to CVE-2020-15193. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

In Tensorflow before versions 2.2.1 and 2.3.1, the implementation of `dlpack.to_dlpack` can be made to use uninitialized memory resulting in further memory corruption. This is because the pybind11 glue code assumes that the argument is a tensor. However, there is nothing stopping users from passing in a Python object instead of a tensor. The uninitialized memory address is due to a `reinterpret_cast` Since the `PyObject` is a Python object, not a TensorFlow Tensor, the cast to `EagerTensor` fails. The issue is patched in commit 22e07fb204386768e5bcbea563641ea11f96ceb8 and is released in Tensor
O3 Security · Impact-Aware SCA

Is CVE-2020-15193 in your dependencies?

O3 detects CVE-2020-15193 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.