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CRITICAL severity

GHSA-g7p5-5759-qv46

CRITICALFix: tensorflow/tensorflow@0462de5

GHSA-g7p5-5759-qv46 is a critical-severity (CVSS 9) Buffer Overflow vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-g7p5-5759-qv46 is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Data leak in Tensorflow

Also known asBIT-tensorflow-2020-15205CVE-2020-15205PYSEC-2020-128PYSEC-2020-285PYSEC-2020-320
Published
Sep 25, 2020
Updated
Jul 8, 2026
Affected
15 pkgs
Patched
15 / 15
Exploits
1 known
Exploitation data as of Jul 8, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Real-World Exposure

15 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+7 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

The data_splits argument of tf.raw_ops.StringNGrams lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory

>>> tf.raw_ops.StringNGrams(data=["aa", "bb", "cc", "dd", "ee", "ff"], data_splits=[0,8], separator=" ", ngram_widths=[3], left_pad="", right_pad="", pad_width=0, preserve_short_sequences=False)
StringNGrams(ngrams=<tf.Tensor: shape=(6,), dtype=string, numpy=
array([b'aa bb cc', b'bb cc dd', b'cc dd ee', b'dd ee ff',
       b'ee ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00',
       b'ff \xf4j\xa7q\x7f\x00\x00q\x00\x00\x00\x00\x00\x00\x00\xd8\x9b~\xa8q\x7f\x00 \x9b~\xa8q\x7f\x00\x00p\xf5j\xa7q\x7f\x00\x00H\xf8j\xa7q\x7f\x00\x00\xf0\xf3\xf7\x85q\x7f\x00\x00`}\xa6\x00\x00\x00\x00\x00`~\xa6\x00\x00\x00\x00\x00\xb0~\xeb\x9bq\x7f\x00'],...

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.

Affected Packages

15 total 15 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions1.15.4
🐍PyPItensorflow2.0.0&&< 2.0.32.0.3
🐍PyPItensorflow2.1.0&&< 2.1.22.1.2
🐍PyPItensorflow2.2.0&&< 2.2.12.2.1
🐍PyPItensorflow2.3.0&&< 2.3.12.3.1
🐍PyPItensorflow-cpuall versions1.15.4
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 1.15.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-g7p5-5759-qv46 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-g7p5-5759-qv46 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-g7p5-5759-qv46. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact The `data_splits` argument of [`tf.raw_ops.StringNGrams`](https://www.tensorflow.org/api_docs/python/tf/raw_ops/StringNGrams) lacks validation. This allows a user to pass values that can cause heap overflow errors and even leak contents of memory ```python >>> tf.raw_ops.StringNGrams(data=["aa", "bb", "cc", "dd", "ee", "ff"], data_splits=[0,8], separator=" ", ngram_widths=[3], left_pad="", right_pad="", pad_width=0, preserve_short_sequences=False) StringNGrams(ngrams=<tf.Tensor: shape=(6,), dtype=string, numpy= array([b'aa bb cc', b'bb cc dd', b'cc dd ee', b'dd ee ff', b'ee f
O3 Security · Impact-Aware SCA

Is GHSA-g7p5-5759-qv46 in your dependencies?

O3 detects GHSA-g7p5-5759-qv46 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-g7p5-5759-qv46: Data leak in… | O3 Security