CVE-2025-66481 is a critical vulnerability in DeepChat, an open-source AI chat platform supporting cloud models and large language models (LLMs). Versions 0.5.1 and earlier improperly neutralize script-related HTML tags embedded within Mermaid content, a diagramming and visualization syntax supported by the platform. The vulnerability stems from inadequate sanitization in the MermaidArtifact.vue component, where the patch intended to mitigate XSS attacks is bypassable. Attackers exploit unquoted HTML attributes combined with HTML entity encoding to evade the regex filters designed to strip dangerous attributes. This enables injection of malicious scripts that execute in the context of the victim's browser. More critically, because DeepChat is built on Electron, the malicious script can leverage the electron.ipcRenderer interface to achieve remote code execution on the victim’s machine, escalating the attack from a typical XSS to full system compromise. The vulnerability requires no privileges and only limited user interaction (e.g., viewing malicious content). The CVSS 3.1 score of 9.7 reflects the vulnerability’s network attack vector, low complexity, no privileges required, user interaction needed, and a scope change due to the RCE capability. No official patch or fix is available at the time of publication, increasing the urgency for defensive measures. Although no known exploits are reported in the wild yet, the potential impact is severe given the criticality and ease of exploitation.

For European organizations, the impact of CVE-2025-66481 is substantial. Exploitation can lead to full remote code execution on user machines, potentially compromising sensitive data, intellectual property, and internal systems. Organizations using DeepChat for AI-driven customer support, internal communications, or data analysis risk unauthorized access and control over endpoints. The vulnerability threatens confidentiality by exposing sensitive chat data and credentials, integrity by allowing malicious code injection and manipulation of chat content, and availability by enabling destructive payloads or ransomware deployment. The cross-site scripting nature combined with Electron’s IPC interface broadens the attack surface beyond typical web app boundaries, increasing risk to endpoint security. This is particularly concerning for sectors with high regulatory requirements such as finance, healthcare, and government agencies in Europe. The lack of an official patch means organizations must rely on immediate mitigations to prevent exploitation. Additionally, the vulnerability could be leveraged in targeted attacks or supply chain compromises, amplifying its impact across interconnected European digital ecosystems.

1. Immediately disable or restrict rendering of Mermaid content within DeepChat to prevent injection vectors. 2. Implement strict input validation and sanitization on all user-supplied content, especially focusing on unquoted HTML attributes and entity encoding. 3. Employ Content Security Policy (CSP) headers to limit script execution sources and reduce XSS impact. 4. Run DeepChat within isolated environments or sandboxes to contain potential RCE effects. 5. Monitor network and endpoint logs for suspicious activity related to electron.ipcRenderer usage or anomalous script execution. 6. Educate users to avoid interacting with untrusted or unexpected chat content until a patch is released. 7. Engage with the DeepChat community or vendor to track patch developments and apply updates promptly once available. 8. Consider alternative AI chat platforms with verified security postures if immediate risk reduction is critical. 9. Conduct penetration testing and code audits focusing on HTML sanitization and Electron IPC interfaces. 10. Use endpoint protection solutions capable of detecting and blocking malicious script execution and IPC abuse.