CVE-2023-5541 in Moodle
Summary
by MITRE • 11/09/2023
The CSV grade import method contained an XSS risk for users importing the spreadsheet, if it contained unsafe content.
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Analysis
by VulDB Data Team • 09/06/2025
The vulnerability identified as CVE-2023-5541 represents a cross-site scripting weakness within CSV grade import functionality that affects users processing spreadsheet data. This issue arises when users attempt to import CSV files containing malicious content, creating a potential attack vector that could compromise user sessions and data integrity. The vulnerability specifically targets the import mechanism where user-supplied data from spreadsheets is processed without adequate sanitization or validation, allowing attackers to inject malicious scripts that execute within the context of other users' browsers.
The technical flaw stems from insufficient input validation and output encoding within the CSV import processing pipeline. When spreadsheet data is parsed and rendered in the user interface, the system fails to properly escape or sanitize special characters that could be interpreted as HTML or JavaScript code. This weakness allows attackers to embed malicious payloads within CSV files that contain grade data, which then executes when other users view or interact with the imported data. The vulnerability aligns with CWE-79, which specifically addresses cross-site scripting flaws, and demonstrates a classic case of inadequate data sanitization in web applications.
The operational impact of this vulnerability extends beyond simple script execution, potentially enabling attackers to perform session hijacking, data theft, or unauthorized actions within the application context. Users who import or view CSV files containing malicious content could unknowingly execute scripts that steal cookies, redirect traffic, or modify application behavior. This risk is particularly concerning in educational or enterprise environments where grade data import is a common administrative task, as it could allow attackers to gain unauthorized access to sensitive academic or business information. The vulnerability also maps to ATT&CK technique T1566.001, which covers social engineering through spearphishing with attachments, as malicious CSV files could be delivered as part of targeted attacks.
Mitigation strategies should focus on implementing comprehensive input validation and output encoding mechanisms within the CSV import processing workflow. The system must sanitize all user-supplied data from imported files, particularly when the data is rendered in web interfaces, by implementing proper HTML entity encoding and content security policies. Additionally, the application should employ strict file format validation and consider implementing a whitelist approach for acceptable CSV content. Security measures should include regular security testing of import functionality, implementation of secure coding practices for data handling, and user education regarding the risks of importing untrusted spreadsheet data. Organizations should also consider implementing web application firewalls and monitoring for suspicious import activities to detect potential exploitation attempts.