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LOW severity

GHSA-x83m-p7pv-ch8v

LOWFix: tensorflow/tensorflow@744009c

GHSA-x83m-p7pv-ch8v is a low-severity (CVSS 2.5) CWE-369 vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-x83m-p7pv-ch8v is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

Division by 0 in `QuantizedAdd`

Also known asBIT-tensorflow-2021-29549CVE-2021-29549PYSEC-2021-186PYSEC-2021-477PYSEC-2021-675
Published
May 21, 2021
Updated
Jul 8, 2026
Affected
12 pkgs
Patched
12 / 12
Exploits
1 known
Exploitation data as of Jul 8, 2026 · OSV.dev, NVD, FIRST.org (EPSS)

Real-World Exposure

12 pkgs affected
🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu🐍tensorflow-cpu+4 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 cause a runtime division by zero error and denial of service in tf.raw_ops.QuantizedAdd:

import tensorflow as tf

x = tf.constant([68, 228], shape=[2, 1], dtype=tf.quint8)
y = tf.constant([], shape=[2, 0], dtype=tf.quint8)

min_x = tf.constant(10.723421015884028)
max_x = tf.constant(15.19578006631113)
min_y = tf.constant(-5.539003866682977)
max_y = tf.constant(42.18819949559947)

tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y)

This is because the implementation computes a modulo operation without validating that the divisor is not zero.

void VectorTensorAddition(const T* vector_data, float min_vector,
                          float max_vector, int64 vector_num_elements,
                          const T* tensor_data, float min_tensor,
                          float max_tensor, int64 tensor_num_elements,
                          float output_min, float output_max, Toutput* output) {
  for (int i = 0; i < tensor_num_elements; ++i) {
    const int64 vector_i = i % vector_num_elements;
    ...
  }
}

Since vector_num_elements is determined based on input shapes, a user can trigger scenarios where this quantity is 0.

Patches

We have patched the issue in GitHub commit 744009c9e5cc5d0447f0dc39d055f917e1fd9e16.

The fix will be included in TensorFlow 2.5.0. We will also cherrypick this commit on TensorFlow 2.4.2, TensorFlow 2.3.3, TensorFlow 2.2.3 and TensorFlow 2.1.4, 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 Yakun Zhang and Ying Wang of Baidu X-Team.

Affected Packages

12 total 12 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.1.4
🐍PyPItensorflow2.2.0&&< 2.2.32.2.3
🐍PyPItensorflow2.3.0&&< 2.3.32.3.3
🐍PyPItensorflow2.4.0&&< 2.4.22.4.2
🐍PyPItensorflow-cpuall versions2.1.4
🐍PyPItensorflow-cpu2.2.0&&< 2.2.32.2.3
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 2.1.4 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-x83m-p7pv-ch8v 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-x83m-p7pv-ch8v 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-x83m-p7pv-ch8v. 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 cause a runtime division by zero error and denial of service in `tf.raw_ops.QuantizedAdd`: ```python import tensorflow as tf x = tf.constant([68, 228], shape=[2, 1], dtype=tf.quint8) y = tf.constant([], shape=[2, 0], dtype=tf.quint8) min_x = tf.constant(10.723421015884028) max_x = tf.constant(15.19578006631113) min_y = tf.constant(-5.539003866682977) max_y = tf.constant(42.18819949559947) tf.raw_ops.QuantizedAdd(x=x, y=y, min_x=min_x, max_x=max_x, min_y=min_y, max_y=max_y) ``` This is because the [implementation](https://github.com/tensorflow/tensorflow/blob/6f2
O3 Security · Impact-Aware SCA

Is GHSA-x83m-p7pv-ch8v in your dependencies?

O3 detects GHSA-x83m-p7pv-ch8v across PyPI dependencies and uses function-level reachability to confirm whether the vulnerable code path is actually reachable — not just present. No false positives.