6.5

CVSS Score

3.1

-

CVSS Score

Basic Information

CVE ID

CVE-2023-25661

GHSA ID

GHSA-fxgc-95xx-grvq

EPSS Score

0.31172%

CWE

CWE-20

Published

3/27/2023

Updated

9/1/2023

KEV Status

Technology

Python

Basic Information

CVE ID

GHSA ID

EPSS Score

CWE

Published

Updated

KEV Status

Technology

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CVE-2023-25661

CVE-2023-25661: TensorFlow Denial of Service vulnerability

Impact

A malicious invalid input crashes a tensorflow model (Check Failed) and can be used to trigger a denial of service attack. To minimize the bug, we built a simple single-layer TensorFlow model containing a Convolution3DTranspose layer, which works well with expected inputs and can be deployed in real-world systems. However, if we call the model with a malicious input which has a zero dimension, it gives Check Failed failure and crashes.

import tensorflow as tf

class MyModel(tf.keras.Model):
    def __init__(self):
        super().__init__()
        self.conv = tf.keras.layers.Convolution3DTranspose(2, [3,3,3], padding="same")
        
    def call(self, input):
        return self.conv(input)
model = MyModel() # Defines a valid model.

x = tf.random.uniform([1, 32, 32, 32, 3], minval=0, maxval=0, dtype=tf.float32) # This is a valid input.
output = model.predict(x)
print(output.shape) # (1, 32, 32, 32, 2)

x = tf.random.uniform([1, 32, 32, 0, 3], dtype=tf.float32) # This is an invalid input.
output = model(x) # crash

This Convolution3DTranspose layer is a very common API in modern neural networks. The ML models containing such vulnerable components could be deployed in ML applications or as cloud services. This failure could be potentially used to trigger a denial of service attack on ML cloud services.

Patches

We have patched the issue in

GitHub commit 948fe6369a5711d4b4568ea9bbf6015c6dfb77e2
GitHub commit 85db5d07db54b853484bfd358c3894d948c36baf.

The fix will be included in TensorFlow 2.12.0. We will also cherrypick this commit on TensorFlow 2.11.1

For more information

Please consult our security guide for more information regarding the security model and how to contact us with issues and questions.

(GitHub Advisory)

Miggo Vulnerability Database

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CVE-2023-25661

CVE-2023-25661:

6.5

CVSS Score

3.1

-

CVSS Score

Basic Information

CVE ID

CVE-2023-25661

GHSA ID

GHSA-fxgc-95xx-grvq

EPSS Score

0.31172%

CWE

CWE-20

Published

3/27/2023

Updated

9/1/2023

KEV Status

Technology

Python

Basic Information

CVE ID

GHSA ID

EPSS Score

CWE

Published

Updated

KEV Status

Technology

Technical Details

CVSS Vector

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

Package Name	Ecosystem	Vulnerable Versions	First Patched Version
tensorflow	pip	< 2.11.1	2.11.1
tensorflow-cpu	pip	< 2.11.1	2.11.1

Technical Details

Vulnerability Intelligence
Miggo AI

Root Cause Analysis

The vulnerability stems from improper input validation in the Conv3DTranspose gradient computation. The TensorFlow commit 948fe63 shows added validation checks in conv_grad_ops_3d.cc to handle zero-dimension inputs. The Keras test case addition in 85db5d0 verifies this fix at the API level. The root cause is in the Conv3DBackpropInputOp implementation where missing dimension checks allowed invalid inputs to crash the process. The patched code explicitly checks for zero dimensions and early returns, confirming this as the vulnerable function.

Vulnerable functions

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WAF Protection Rules

WAF Rule

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Reasoning

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6.5

-

Basic Information

Basic Information

Concerned about an active attack path?

CVE-2023-25661: TensorFlow Denial of Service vulnerability

Impact

Patches

For more information

CVE-2023-25661:

6.5

-

Basic Information

Basic Information

Technical Details

Technical Details

Vulnerability IntelligenceMiggo AI

Root Cause Analysis

Vulnerable functions

WAF Protection Rules

WAF Rule

Reasoning

Vulnerability IntelligenceMiggo AI

6.5

6.5

Vulnerability IntelligenceMiggo AI

Root Cause Analysis

Vulnerable functions

WAF Protection Rules

WAF Rule

Reasoning

CVE-2023-25661: TensorFlow Denial of Service vulnerability

Impact

Patches

For more information

Technical Details

Basic Information

Basic Information

Vulnerability Intelligence
Miggo AI

Vulnerability Intelligence
Miggo AI

Vulnerability Intelligence
Miggo AI