Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2025-67744 is a critical-severity Code Injection (CWE-94) vulnerability in Thinkinai Deepchat. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Command and Scripting Interpreter (T1059); ranked at the 43th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the Supply Chain and Deployment risk domain.
The strongest mitigations our analysis identified map to SA-11 (Developer Testing and Evaluation) and SI-10 (Information Input Validation) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2025-67744 is a high-severity vulnerability (CVSS 9.6) affecting DeepChat, an open-source artificial intelligence agent platform that unifies models, tools, and agents, in versions prior to 0.5.3. The flaw resides in the Mermaid diagram rendering component, which permits arbitrary JavaScript execution due to unsafe Mermaid configuration. This XSS issue escalates to full remote code execution (RCE) because of an exposed Electron IPC renderer interface accessible from the DOM, enabling attackers to run arbitrary system commands. It is classified under CWE-94 (Code Injection).
An attacker can exploit this vulnerability over the network with low complexity and no privileges required, though user interaction is needed to render a malicious Mermaid diagram within DeepChat. Successful exploitation changes the scope and grants high confidentiality, integrity, and availability impact, culminating in arbitrary command execution on the victim's system.
The GitHub security advisory (GHSA-w8w8-82pv-5rg9) and patch commit (b179d97921af04a0ae1ae68757338dd8b8cbefe7) confirm that upgrading to DeepChat version 0.5.3 resolves the issues by addressing the unsafe Mermaid configuration and exposed IPC interface.
This vulnerability is particularly relevant to AI/ML practitioners using DeepChat for agent development, as it highlights risks in rendering untrusted diagram content within Electron-based desktop applications. No real-world exploitation has been reported as of the CVE publication on 2025-12-16.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-203488
Vulnerability Data
DeepChat is an open-source artificial intelligence agent platform that unifies models, tools, and agents. Prior to version 0.5.3, a security vulnerability exists in the Mermaid diagram rendering component that allows arbitrary JavaScript execution. Due to the exposure of the Electron…
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IPC renderer to the DOM, this Cross-Site Scripting (XSS) flaw escalates to full Remote Code Execution (RCE), allowing an attacker to execute arbitrary system commands. Two concurrent issues, unsafe Mermaid configuration and an exposed IPC interface, cause this issue. Version 0.5.3 contains a patch.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Supply Chain and Deployment
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: artificial intelligence
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.3.1
Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation finds code paths that accept and execute externally influenced strings.
Input validation directly stops untrusted data from being used to construct executable code without neutralization.
Least privilege limits the damage an injected code fragment can perform once executed.
Requiring documented secure development standards and tools enforces use of safe code-generation APIs and escaping.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
PR.PS-06's SDLC practices directly target injection flaws via secure coding and testing (mostly), yet as a single broad outcome it leaves many code-generation specifics unaddressed (partial).
PR.DS-10 protects runtime data confidentiality/integrity but has no bearing on neutralizing externally influenced input during code generation, so neither direction shows any preventive effect.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Banning unapproved code samples and unauthenticated web services, combined with secure-coding standards and SAST, prevents the dynamic generation or inclusion of attacker-supplied code.
Controls that restrict unauthorized or malicious code from being introduced via external networks or removable media limit opportunities for an attacker to inject and execute arbitrary code.