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GHSA-79h2-q768-fpxr

MEDIUM

GHSA-79h2-q768-fpxr is a medium-severity (CVSS 5.9) Improper Input Validation vulnerability in tensorflow. 1 public exploit reference exists, so weaponization risk is real. O3 Security confirms whether GHSA-79h2-q768-fpxr is actually reachable in your code before you act, and blocks exploitation at runtime until you patch.

TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions

Also known asBIT-tensorflow-2022-36027CVE-2022-36027PYSEC-2026-3128PYSEC-2026-3291PYSEC-2026-965
Published
Sep 16, 2022
Updated
Jul 13, 2026
Affected
9 pkgs
Patched
9 / 9
Exploits
1 known

Blast Radius

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

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Description

Impact

When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process.

import tensorflow as tf

class QuantConv2DTransposed(tf.keras.layers.Layer):
    def build(self, input_shape):
        self.kernel = self.add_weight("kernel", [3, 3, input_shape[-1], 24])

    def call(self, inputs):
        filters = tf.quantization.fake_quant_with_min_max_vars_per_channel(
            self.kernel, -3.0 * tf.ones([24]), 3.0 * tf.ones([24]), narrow_range=True
        )
        filters = tf.transpose(filters, (0, 1, 3, 2))
        return tf.nn.conv2d_transpose(inputs, filters, [*inputs.shape[:-1], 24], 1)

inp = tf.keras.Input(shape=(6, 8, 48), batch_size=1)
x = tf.quantization.fake_quant_with_min_max_vars(inp, -3.0, 3.0, narrow_range=True)
x = QuantConv2DTransposed()(x)
x = tf.quantization.fake_quant_with_min_max_vars(x, -3.0, 3.0, narrow_range=True)

model = tf.keras.Model(inp, x)

model.save("/tmp/testing")
converter = tf.lite.TFLiteConverter.from_saved_model("/tmp/testing")
converter.optimizations = [tf.lite.Optimize.DEFAULT]

# terminated by signal SIGSEGV (Address boundary error)
tflite_model = converter.convert()

Patches

We have patched the issue in GitHub commit aa0b852a4588cea4d36b74feb05d93055540b450.

The fix will be included in TensorFlow 2.10.0. We will also cherrypick this commit on TensorFlow 2.9.1, TensorFlow 2.8.1, and TensorFlow 2.7.2, 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 Lukas Geiger via Github issue.

Affected Packages

9 total 9 fixed
EcosystemPackageVulnerable rangeFix
🐍PyPItensorflowall versions2.7.2
🐍PyPItensorflow≥ 2.8.0&&< 2.8.12.8.1
🐍PyPItensorflow≥ 2.9.0&&< 2.9.12.9.1
🐍PyPItensorflow-cpuall versions2.7.2
🐍PyPItensorflow-cpu≥ 2.8.0&&< 2.8.12.8.1
🐍PyPItensorflow-cpu≥ 2.9.0&&< 2.9.12.9.1
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.

TensorFlow is an open source platform for machine learning. When convert…

github.comNVDSep 2022

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 2.7.2 or later, then make sure no transitive (indirect) dependency still pins the vulnerable range — O3 confirms GHSA-79h2-q768-fpxr 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-79h2-q768-fpxr 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-79h2-q768-fpxr. Runtime protection reduces exposure until a permanent patch is applied and verified — it complements patching, it doesn't replace it.

Frequently Asked Questions

### Impact When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. ```python import tensorflow as tf class QuantConv2DTransposed(tf.keras.layers.Layer): def build(self, input_shape): self.kernel = self.add_weight("kernel", [3, 3, input_shape[-1], 24]) def call(self, inputs): filters = tf.quantization.fake_quant_with_min_max_vars_per_channel( self.kernel, -3.0 * tf.ones([24]), 3.0 * tf.ones([24]), narrow_range=True ) filters = tf.transpose(filters, (0, 1, 3, 2))
O3 Security · Impact-Aware SCA

Is GHSA-79h2-q768-fpxr in your dependencies?

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