CVE-2025-67744 is a critical security vulnerability identified in the DeepChat platform, an open-source AI agent framework that integrates various models and tools. The vulnerability stems from two concurrent issues: an unsafe configuration in the Mermaid diagram rendering component and an exposed Electron IPC (Inter-Process Communication) renderer interface accessible from the DOM. Mermaid is used to generate diagrams from text descriptions, and in this case, it allows arbitrary JavaScript execution due to improper sanitization or unsafe configuration. The Electron IPC renderer exposure means that malicious scripts injected via Mermaid can escalate from a typical Cross-Site Scripting (XSS) attack to full Remote Code Execution (RCE) on the host system. This allows an attacker to execute arbitrary system commands remotely without requiring any privileges, though user interaction (such as opening a malicious diagram) is necessary. The vulnerability affects all DeepChat versions prior to 0.5.3, which contains the patch that secures the Mermaid configuration and restricts IPC exposure. The CVSS v3.1 score of 9.7 reflects the critical nature of this flaw, with high impact on confidentiality, integrity, and availability, and ease of exploitation over the network. While no exploits have been reported in the wild yet, the combination of open-source usage, AI platform popularity, and the severity of the flaw makes it a significant threat. Organizations using DeepChat should prioritize upgrading to version 0.5.3 and review their deployment configurations to ensure IPC interfaces are not unnecessarily exposed and that Mermaid inputs are sanitized or restricted. This vulnerability highlights the risks of integrating complex rendering components and IPC mechanisms without strict security controls in modern AI platforms.

For European organizations, the impact of CVE-2025-67744 can be severe. Successful exploitation results in full remote code execution, allowing attackers to take complete control over affected systems. This can lead to data breaches, unauthorized access to sensitive AI models and data, disruption of AI services, and potential lateral movement within networks. Given the increasing reliance on AI platforms like DeepChat for automation, research, and business intelligence, compromise could undermine operational integrity and confidentiality. The vulnerability's network accessibility and lack of required privileges make it highly exploitable, increasing risk especially in environments where DeepChat is exposed to external users or integrated into web-facing applications. The AI and tech sectors in Europe, which are rapidly growing and often leverage open-source tools, could face intellectual property theft, espionage, or sabotage. Additionally, critical infrastructure or research institutions using DeepChat might experience service outages or manipulation of AI-driven decisions. The absence of known exploits in the wild currently provides a window for proactive mitigation, but the critical severity demands immediate attention to prevent potential attacks.

1. Immediate upgrade of all DeepChat instances to version 0.5.3 or later, which contains the patch addressing the unsafe Mermaid configuration and IPC exposure. 2. Audit and restrict Electron IPC interfaces to ensure they are not exposed unnecessarily to the DOM or untrusted inputs. 3. Implement strict input validation and sanitization on Mermaid diagram data to prevent injection of malicious JavaScript code. 4. Employ network segmentation and firewall rules to limit access to DeepChat services, especially from untrusted networks. 5. Monitor logs and network traffic for unusual activity indicative of exploitation attempts, such as unexpected command executions or IPC calls. 6. Conduct security reviews of any custom integrations or plugins that interact with Mermaid or Electron IPC to ensure they do not introduce similar vulnerabilities. 7. Educate users about the risks of opening untrusted Mermaid diagrams or AI agent inputs that could trigger code execution. 8. Consider deploying runtime application self-protection (RASP) or endpoint detection and response (EDR) solutions to detect and block exploitation attempts in real time. 9. Maintain an incident response plan tailored to AI platform compromises, including containment and recovery procedures. These steps go beyond generic patching by addressing the root causes and operational security controls necessary to mitigate this complex vulnerability.