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HIGH severity

GHSA-r4c4-5fpq-56wg

HIGHFix: tensorflow/tensorflow@e84c975

GHSA-r4c4-5fpq-56wg is a high-severity (CVSS 7.3) Out-of-bounds Read vulnerability in tensorflow. O3 Security confirms whether GHSA-r4c4-5fpq-56wg is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Heap OOB in boosted trees

Also known asBIT-tensorflow-2021-37664CVE-2021-37664PYSEC-2021-286PYSEC-2021-577PYSEC-2021-775
Published
Aug 25, 2021
Updated
Jul 8, 2026
Affected
9 pkgs
Patched
9 / 9
Exploits
None indexed
Exploitation data as of Jul 31, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

EPSS Exploitation Probability

via FIRST.org ↗
0.2%probability of exploitation in next 30 days
Lower Risk0.00%
Lower risk than most CVEs6th percentile — riskier than 6% of all scored CVEsHighest risk
0.00%0.22%0.44%0.67%0.0%0.2%Feb 26May 26Jul 26

EPSS (Exploit Prediction Scoring System) is a daily probability model maintained by FIRST.org. It estimates the likelihood a CVE will be exploited in production environments within the next 30 days, derived from real-world threat intelligence signals.

How urgent is this, really

GHSA-r4c4-5fpq-56wg plotted by exploitation likelihood (EPSS) against impact (CVSS). The shaded corner — EPSS 50%+ and CVSS 7.0+ — is where this CVE doesn't sit, though severity or exploitability alone can still warrant action.

Where this sits among everything scored

Of 365,017 CVEs with a current EPSS score, this one falls in the < 10% band (highlighted). Real counts from FIRST.org, not a sample — log-scaled since the landscape is heavily right-skewed.

Real-World Exposure

9 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-gpu🐍tensorflow-gpu+1 more

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

Impact

An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to BoostedTreesSparseCalculateBestFeatureSplit:

import tensorflow as tf

tf.raw_ops.BoostedTreesSparseCalculateBestFeatureSplit(
  node_id_range=[0,10],
  stats_summary_indices=[[1, 2, 3, 0x1000000]],
  stats_summary_values=[1.0],
  stats_summary_shape=[1,1,1,1],
  l1=l2=[1.0],
  tree_complexity=[0.5],
  min_node_weight=[1.0],
  logits_dimension=3,
  split_type='inequality')                                                                                                                                                                                                                                                                

The implementation needs to validate that each value in stats_summary_indices is in range.

Patches

We have patched the issue in GitHub commit e84c975313e8e8e38bb2ea118196369c45c51378.

The fix will be included in TensorFlow 2.6.0. We will also cherrypick this commit on TensorFlow 2.5.1, TensorFlow 2.4.3, and TensorFlow 2.3.4, as these are also affected and still in supported range.

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.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.3.4
🐍PyPItensorflow2.4.0&&< 2.4.32.4.3
🐍PyPItensorflow2.5.0&&< 2.5.12.5.1
🐍PyPItensorflow-cpuall versions2.3.4
🐍PyPItensorflow-cpu2.4.0&&< 2.4.32.4.3
🐍PyPItensorflow-cpu2.5.0&&< 2.5.12.5.1

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 2.3.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-r4c4-5fpq-56wg 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 GHSA-r4c4-5fpq-56wg 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 GHSA-r4c4-5fpq-56wg. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact An attacker can read from outside of bounds of heap allocated data by sending specially crafted illegal arguments to `BoostedTreesSparseCalculateBestFeatureSplit`: ```python import tensorflow as tf tf.raw_ops.BoostedTreesSparseCalculateBestFeatureSplit( node_id_range=[0,10], stats_summary_indices=[[1, 2, 3, 0x1000000]], stats_summary_values=[1.0], stats_summary_shape=[1,1,1,1], l1=l2=[1.0], tree_complexity=[0.5], min_node_weight=[1.0], logits_dimension=3, split_type='inequality')
O3 Security · Impact-Aware SCA

Is GHSA-r4c4-5fpq-56wg in your dependencies?

O3 detects GHSA-r4c4-5fpq-56wg across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.

GHSA-r4c4-5fpq-56wg: Heap OOB in boosted… | O3 Security