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CVE-2022-41894

HIGH
Published 2022-11-18T00:00:00.000Z
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CVSS Score

V3.1
7.1
/10
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H
Base Score Metrics
Exploitability: N/A Impact: N/A

EPSS Score

v2025.03.14
0.002
probability
of exploitation in the wild

There is a 0.2% chance that this vulnerability will be exploited in the wild within the next 30 days.

Updated: 2025-06-25
Exploit Probability
Percentile: 0.451
Higher than 45.1% of all CVEs

Attack Vector Metrics

Attack Vector
NETWORK
Attack Complexity
HIGH
Privileges Required
LOW
User Interaction
REQUIRED
Scope
UNCHANGED

Impact Metrics

Confidentiality
HIGH
Integrity
HIGH
Availability
HIGH

Description

TensorFlow is an open source platform for machine learning. The reference kernel of the `CONV_3D_TRANSPOSE` TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter. We have patched the issue in GitHub commit 72c0bdcb25305b0b36842d746cc61d72658d2941. The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, as these are also affected and still in supported range.

Understanding This Vulnerability

This Common Vulnerabilities and Exposures (CVE) entry provides detailed information about a security vulnerability that has been publicly disclosed. CVEs are standardized identifiers assigned by MITRE Corporation to track and catalog security vulnerabilities across software and hardware products.

The severity rating (HIGH) indicates the potential impact of this vulnerability based on the CVSS (Common Vulnerability Scoring System) framework. Higher severity ratings typically indicate vulnerabilities that could lead to more significant security breaches if exploited. Security teams should prioritize remediation efforts based on severity, exploit availability, and the EPSS (Exploit Prediction Scoring System) score, which predicts the likelihood of exploitation in the wild.

If this vulnerability affects products or systems in your infrastructure, we recommend reviewing the affected products section, checking for available patches or updates from vendors, and implementing recommended workarounds or solutions until a permanent fix is available. Organizations should also monitor security advisories and threat intelligence feeds for updates about active exploitation of this vulnerability.

Available Exploits

No exploits available for this CVE.

Related News

No news articles found for this CVE.

Affected Products

References

EU Vulnerability Database

Monitored by ENISA for EU cybersecurity

EU Coordination

EU Coordinated

Exploitation Status

No Known Exploitation

ENISA Analysis

Malicious code in bioql (PyPI)

Affected Products (ENISA)

tensorflow
tensorflow

ENISA Scoring

CVSS Score (3.1)

7.1
/10
CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H

EPSS Score

0.240
probability

Data provided by ENISA EU Vulnerability Database. Last updated: October 3, 2025

GitHub Security Advisories

Community-driven vulnerability intelligence from GitHub

✓ GitHub Reviewed HIGH

Buffer overflow in `CONV_3D_TRANSPOSE` on TFLite

GHSA-h6q3-vv32-2cq5

Advisory Details

### Impact The reference kernel of the [`CONV_3D_TRANSPOSE`](https://github.com/tensorflow/tensorflow/blob/091e63f0ea33def7ecad661a5ac01dcafbafa90b/tensorflow/lite/kernels/internal/reference/conv3d_transpose.h#L121) TensorFlow Lite operator wrongly increments the data_ptr when adding the bias to the result. Instead of `data_ptr += num_channels;` it should be `data_ptr += output_num_channels;` as if the number of input channels is different than the number of output channels, the wrong result will be returned and a buffer overflow will occur if num_channels > output_num_channels. An attacker can craft a model with a specific number of input channels in a way similar to the attached example script. It is then possible to write specific values through the bias of the layer outside the bounds of the buffer. This attack only works if the reference kernel resolver is used in the interpreter (i.e. `experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF` is used). ```python import tensorflow as tf model = tf.keras.Sequential( [ tf.keras.layers.InputLayer(input_shape=(2, 2, 2, 1024), batch_size=1), tf.keras.layers.Conv3DTranspose( filters=8, kernel_size=(2, 2, 2), padding="same", data_format="channels_last", ), ] ) converter = tf.lite.TFLiteConverter.from_keras_model(model) tflite_model = converter.convert() interpreter = tf.lite.Interpreter( model_content=tflite_model, experimental_op_resolver_type=tf.lite.experimental.OpResolverType.BUILTIN_REF, ) interpreter.allocate_tensors() interpreter.set_tensor( interpreter.get_input_details()[0]["index"], tf.zeros(shape=[1, 2, 2, 2, 1024]) ) interpreter.invoke() ``` ### Patches We have patched the issue in GitHub commit [72c0bdcb25305b0b36842d746cc61d72658d2941](https://github.com/tensorflow/tensorflow/commit/72c0bdcb25305b0b36842d746cc61d72658d2941). The fix will be included in TensorFlow 2.11. We will also cherrypick this commit on TensorFlow 2.10.1, 2.9.3, and TensorFlow 2.8.4, 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 Thibaut Goetghebuer-Planchon, Arm Ltd.

Affected Packages

PyPI tensorflow
ECOSYSTEM: ≥0 <2.8.4
PyPI tensorflow
ECOSYSTEM: ≥2.9.0 <2.9.3
PyPI tensorflow
ECOSYSTEM: ≥2.10.0 <2.10.1

CVSS Scoring

CVSS Score

7.5

CVSS Vector

CVSS:3.1/AV:N/AC:H/PR:L/UI:R/S:U/C:H/I:H/A:H

Advisory provided by GitHub Security Advisory Database. Published: November 21, 2022, Modified: November 21, 2022

References

Published: 2022-11-18T00:00:00.000Z
Last Modified: 2025-04-22T16:04:55.192Z
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