CVE-2026-108961
Received Received - Intake

Code Injection in PaddlePaddle PaddleX Preprocessor

Vulnerability report for CVE-2026-108961, including description, CVSS score, EPSS score, affected products, exploitability, helpful resources, and attack-flow context.

Publication date: 2026-10-13

Last updated on: 2026-10-13

Assigner: VulDB

Description

A flaw has been found in PaddlePaddle PaddleX up to 3.7.2. Affected by this vulnerability is the function NormalizeImage.__init__ of the file paddlex/inference/models/text_detection/processors.py of the component Preprocessor. This manipulation of the argument scale causes code injection. It is possible to initiate the attack remotely. The pull request to fix this issue awaits acceptance.

CVSS Scores

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Meta Information

Published
2026-10-13
Last Modified
2026-10-13
Generated
2026-10-13
AI Q&A
2026-10-13
EPSS Evaluated
N/A
NVD
EUVD

Affected Vendors & Products

Showing 3 associated CPEs
Vendor Product Version / Range
PaddlePaddle PaddleX 3.7.0
PaddlePaddle PaddleX 3.7.1
PaddlePaddle PaddleX 3.7.2

Helpful Resources

Exploitability

CWE
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KEV
KEV Icon
CWE ID Description
CWE-94 The product constructs all or part of a code segment using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the syntax or behavior of the intended code segment.
CWE-74 The product constructs all or part of a command, data structure, or record using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify how it is parsed or interpreted when it is sent to a downstream component.

Attack-Flow Graph

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Executive Summary

CVE-2026-108961 is an arbitrary code execution vulnerability in PaddlePaddle PaddleX up to version 3.7.2. It occurs in the NormalizeImage preprocessing step for text detection, where an attacker-controlled 'scale' string in the model directory's 'inference.yml' file is evaluated using Python's eval() function without proper validation. This allows malicious code execution during model loading.

Detection Guidance

Check for suspicious files named inference.yml in model directories, especially those containing malicious scale values like eval() calls or unexpected file writes. Inspect logs for unauthorized file creation or unusual model loading behavior.

Impact Analysis

An attacker can execute arbitrary code on your system by tricking you into loading a malicious model directory. This could lead to data theft, system compromise, or further network infiltration. The attack requires only a single modified line in 'inference.yml' and leaves no error trace, making detection difficult.

Compliance Impact

This vulnerability could lead to unauthorized data access or modification, violating GDPR's integrity and confidentiality principles or HIPAA's security requirements for protected health information. Organizations using affected PaddleX versions may face compliance violations if exploited.

Mitigation Strategies

Apply the official patch from pull request #5212 to replace eval() with a restricted numeric parser. Avoid using untrusted model directories and validate all third-party models before deployment.

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