CVE-2022-36027

highpriority · track · 30EPSS 0.008 · 55 %ileEUUS
grimoire priorityP2 · Scheduled
Exploitable?
—
no known exploitation
Likely / severe?
0.77% (55th) EPSS
CVSS 7.5 · high
Is it mine?
19 products
Check your exposure
Who says so?
4 sources
cnnvd · enisa_euvd · osv · nvd

EPSS · scored 2026-10-27

TensorFlow is an open source platform for machine learning. When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. 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. There are no known workarounds for this issue.

01

cvss

Technical severity, 0–10. One row per scoring source.

7.5
010

base score 0–10 · higher = more severe · hover a vector to decode⚠ sources disagree · medium–high · 5.9–7.5

v3.17.5highCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:Hprimary · NVD / NIST USexploitability 3.9 · impact 3.6
v3.15.9mediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:Hsecondary · ENISA EU
v3.15.9mediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:Hsecondary · security-advisories@github.comexploitability 2.2 · impact 3.6
v3.15.9mediumCVSS:3.1/AV:N/AC:H/PR:N/UI:N/S:U/C:N/I:N/A:Hsecondary · OSV
v3.16.9mediumCVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H/E:Usecondary · CVSS-BT (t0sche)
02

ssvc

A stakeholder decision — act, attend, or track — from exploitation and impact.

Exploitation: poc · Automatable: no · Technical Impact: partialcoordinatorcisa_ssvcUS
03

weaknesses

The CWE weakness classes this flaw belongs to.

CWE-20USCWE-20US
04

affected products (19)

Products and version ranges known to be affected.

05

advisories (5)

Vendor and ecosystem advisories describing this.

  • GHSA-79h2-q768-fpxr TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions2022-09-16ghsa · osvUS

    ### 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)) 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](https://github.com/tensorflow/tensorflow/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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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](https://github.com/tensorflow/tensorflow/issues/53767).

  • BIT-tensorflow-2022-36027Segfault TFLite converter on per-channel quantized transposed convolutions in TensorFlow2024-03-06vendor · osvUS

    TensorFlow is an open source platform for machine learning. When converting transposed convolutions using per-channel weight quantization the converter segfaults and crashes the Python process. 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. There are no known workarounds for this issue.

  • PYSEC-2026-3128 TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions2026-07-13vendor · osvUS

    ### 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)) 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](https://github.com/tensorflow/tensorflow/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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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](https://github.com/tensorflow/tensorflow/issues/53767).

  • PYSEC-2026-3291 TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions2026-07-13vendor · osvUS

    ### 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)) 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](https://github.com/tensorflow/tensorflow/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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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](https://github.com/tensorflow/tensorflow/issues/53767).

  • PYSEC-2026-965 TensorFlow segfault TFLite converter on per-channel quantized transposed convolutions2026-07-07vendor · osvUS

    ### 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)) 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](https://github.com/tensorflow/tensorflow/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](https://github.com/tensorflow/tensorflow/blob/master/SECURITY.md) 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](https://github.com/tensorflow/tensorflow/issues/53767).

06

also known as (11)

Other names and identifiers for this entity.

07

sources

Which sources assert this record, and when.

cnnvdenisa_euvdosvnvdNVDCVE.orgMITRE
published 2022-09-16modified 2026-06-17
08

references (10)

External citations — patches, advisories, analysis.

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