CVE-2017-3104 in RoboHelp
Summary
by MITRE
Adobe RoboHelp has a cross-site scripting (XSS) vulnerability. This affects versions before RH12.0.4.460 and RH2017 before RH2017.0.2.
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Analysis
by VulDB Data Team • 01/25/2021
Adobe RoboHelp contains a cross-site scripting vulnerability that allows remote attackers to inject malicious scripts into web pages viewed by users. This vulnerability stems from insufficient input validation and output encoding mechanisms within the application's web interface processing components. The flaw exists in versions prior to RH12.0.4.460 and RH2017 before RH2017.0.2, where user-supplied data is not properly sanitized before being rendered in web contexts. The vulnerability is categorized under CWE-79 as a classic cross-site scripting flaw, which occurs when an application incorporates untrusted data into web pages without proper validation or encoding. Attackers can exploit this weakness by crafting malicious input that gets executed in the victim's browser context, potentially leading to session hijacking, credential theft, or redirection to malicious sites. The vulnerability is particularly concerning because RoboHelp is commonly used for creating technical documentation and help systems, which often contain user-generated content that may be processed through vulnerable code paths. This issue aligns with ATT&CK technique T1059.002 for command and scripting interpreter, specifically targeting web-based scripting environments. The operational impact includes potential unauthorized access to sensitive documentation systems, data exfiltration through compromised user sessions, and possible escalation to broader system compromise if the help system is integrated with enterprise applications. Organizations using affected versions should immediately apply patches released by Adobe to mitigate the risk of exploitation. The vulnerability demonstrates the importance of input validation and output encoding in web applications, particularly in content management systems and documentation tools that process user input. Security practitioners should implement additional monitoring for suspicious user input patterns and consider network segmentation to limit potential lateral movement if exploitation occurs. The flaw underscores the necessity of regular security updates and vulnerability assessments for enterprise documentation tools that handle web-based interactions and user-generated content processing.