CVE-2025-49149 is a medium-severity vulnerability classified as CWE-79, which corresponds to improper neutralization of input during web page generation, commonly known as Cross-site Scripting (XSS). This vulnerability affects version 1.2.0 of Dify, an open-source platform developed by langgenius for building applications using large language models (LLMs). The root cause is insufficient filtering or sanitization of user-supplied input within the web application. This flaw allows an attacker to inject malicious scripts into web pages generated by the platform. When other users visit these compromised pages, the injected scripts execute in their browsers under the context of the vulnerable site. This can lead to a range of malicious outcomes including session hijacking, credential theft, unauthorized actions performed on behalf of the user, or redirection to malicious sites. The CVSS 4.0 base score is 5.3, indicating a medium severity level. The vector details show that the attack can be performed remotely over the network without any privileges or authentication, but requires user interaction (e.g., a user visiting a maliciously crafted page). The vulnerability does not impact confidentiality, integrity, or availability directly but has a limited scope impact on security integrity through potential script injection. As of the published date, no patches or fixes have been released, and no known exploits are reported in the wild. The vulnerability is specifically tied to version 1.2.0 of Dify, so only deployments running this exact version are at risk. Given Dify’s role as an LLM app development platform, affected installations may be used by developers or organizations integrating AI capabilities into web applications, potentially exposing end users to XSS attacks if malicious input is not properly sanitized.

For European organizations using Dify 1.2.0, this vulnerability poses a risk primarily to the confidentiality and integrity of user sessions and data accessed through affected web applications. Attackers could exploit the XSS flaw to steal session cookies, impersonate users, or perform unauthorized actions within the context of the vulnerable application. This can lead to data leakage, unauthorized access to sensitive information, or manipulation of application behavior. Organizations deploying Dify in customer-facing or internal portals may face reputational damage, regulatory scrutiny under GDPR if personal data is compromised, and operational disruption if trust in the platform is undermined. The impact is heightened for sectors with high reliance on web-based AI tools, such as financial services, healthcare, and public administration, where sensitive data and compliance requirements are stringent. However, since exploitation requires user interaction and no privilege escalation is involved, the overall risk is moderate but non-negligible. The absence of a patch increases exposure duration, necessitating proactive mitigation. Additionally, attackers could use this vulnerability as a foothold for more complex multi-stage attacks, especially in environments where Dify is integrated with other critical systems.

1. Immediate mitigation should focus on input validation and output encoding: Implement strict server-side input sanitization to neutralize potentially malicious characters or scripts before rendering user input in web pages. 2. Employ Content Security Policy (CSP) headers to restrict the execution of unauthorized scripts and reduce the impact of injected code. 3. Use web application firewalls (WAFs) configured with rules to detect and block common XSS attack patterns targeting Dify endpoints. 4. Restrict user privileges and session lifetimes to limit the window of opportunity for attackers leveraging stolen credentials. 5. Monitor web application logs for unusual input patterns or repeated injection attempts to identify potential exploitation attempts early. 6. Engage with the langgenius community or maintainers to track patch releases and apply updates promptly once available. 7. For organizations with development capabilities, consider contributing or implementing temporary patches or filters to sanitize inputs in the affected version. 8. Educate users about the risks of clicking on suspicious links or interacting with untrusted content within applications built on Dify. 9. Isolate Dify deployments in segmented network zones to limit lateral movement if exploitation occurs. These measures go beyond generic advice by focusing on immediate protective controls tailored to the nature of the vulnerability and the platform’s usage context.