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MEDIUM severity

CVE-2020-15201

MEDIUMFix: tensorflow/tensorflow@3cbb917

CVE-2020-15201 is a medium-severity (CVSS 4.8) Improper Input Validation vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether CVE-2020-15201 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Heap buffer overflow in Tensorflow

Also known asBIT-tensorflow-2020-15201GHSA-p5f8-gfw5-33w4PYSEC-2020-124PYSEC-2020-281PYSEC-2020-316
Published
Sep 25, 2020
Updated
Aug 7, 2026
Affected
3 pkgs
Patched
3 / 3
Exploits
1 known
Exploitation data as of Aug 7, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Real-World Exposure

3 pkgs affected
🐍tensorflow🐍tensorflow-cpu🐍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 version 2.3.1, 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. Hence, the code is prone to heap buffer overflow. If split_values does not end with a value at least num_values then the while loop condition will trigger a read outside of the bounds of split_values once batch_idx grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and is released in TensorFlow version 2.3.1.

Affected Packages

3 total 3 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflow2.3.0&&< 2.3.12.3.1
🐍PyPItensorflow-cpu2.3.0&&< 2.3.12.3.1
🐍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 2.3.1 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms CVE-2020-15201 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-2020-15201 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-2020-15201. 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 version 2.3.1, 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. Hence, the code is prone to heap buffer overflow. If `split_values` does not end with a value at least `num_values` then the `while` loop condition will trigger a read outside of the bounds of `split_values` once `batch_idx` grows too large. The issue is patched in commit 3cbb917b4714766030b28eba9fb41bb97ce9ee02 and
O3 Security · Impact-Aware SCA

Is CVE-2020-15201 in your dependencies?

O3 detects CVE-2020-15201 across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.