Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:HSummary
CVE-2025-66481 is a critical-severity Cross-site Scripting (CWE-79) vulnerability in Thinkinai Deepchat. Its CVSS base score is 9.6 (Critical).
Operationally, exploitation aligns with the MITRE ATT&CK technique Browser Session Hijacking (T1185); ranked at the 44th 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 Other ATLAS/OWASP Terms 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-2025-66481 is a cross-site scripting (XSS) vulnerability affecting DeepChat, an open-source AI chat platform that supports cloud models and large language models (LLMs). Versions 0.5.1 and below are impacted due to improperly sanitized Mermaid content in the MermaidArtifact.vue component. A recent security patch for this component proves insufficient, as it can be bypassed using unquoted HTML attributes combined with HTML entity encoding, allowing attackers to evade the regex filter intended to strip dangerous attributes.
Remote unauthenticated attackers can exploit the vulnerability by tricking victims into interacting with maliciously crafted Mermaid content, requiring user interaction such as viewing or rendering the payload. Successful exploitation enables remote code execution (RCE) on the victim's machine via the Electron ipcRenderer interface. The issue carries a CVSS v3.1 base score of 9.6 (AV:N/AC:L/PR:N/UI:R/S:C/C:H/I:H/A:H) and is associated with CWE-79 (Improper Neutralization of Input During Web Page Generation), CWE-80 (Improper Neutralization of Script-Related HTML Tags), and CWE-94 (Improper Control of Generation of Code).
The GitHub security advisory (GHSA-h9f5-7hhf-fqm4) confirms there is no fix available at the time of publication, emphasizing the need for practitioners to avoid untrusted Mermaid content in affected DeepChat deployments until a patch is released.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-201843
Vulnerability Data
DeepChat is an open-source AI chat platform that supports cloud models and LLMs. Versions 0.5.1 and below are vulnerable to XSS attacks through improperly sanitized Mermaid content. The recent security patch for MermaidArtifact.vue is insufficient and can be bypassed using…
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unquoted HTML attributes combined with HTML entity encoding. Remote Code Execution is possible on the victim's machine via the electron.ipcRenderer interface, bypassing the regex filter intended to strip dangerous attributes. There is no fix at time of publication.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Other ATLAS/OWASP Terms
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, llms
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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Mitigating Controls (NIST 800-53 r5) AI
Developer testing and evaluation can discover missing or incorrect input neutralization through targeted web-application tests.
Input validation directly enforces neutralization of untrusted data before it reaches web output generation.
Output filtering can catch or sanitize unneutralized script content before it is served to users.
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.
Secure engineering principles include mandatory output encoding and neutralization of HTML metacharacters to stop injection at the source.
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.
Secure SDLC practices directly target introduction of XSS via coding standards/testing (mostly), yet the single broad outcome leaves many specific neutralization vectors unaddressed (partial).
Patching and EOL replacement can remediate known XSS instances in libraries or frameworks (partial) but do nothing to enforce input neutralization in application code (none).
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.
Secure-coding testing and automated code-analysis tools are applied to detect improper neutralization of script-related content during web-page generation.
Knowledge exchange on emerging attack techniques and patches reduces the likelihood that cross-site scripting flaws remain unaddressed in deployed applications.
Operational indicators of compromise for web-application attacks can be incorporated into WAF or input-filtering rules, lowering the likelihood that unsanitized data reaches the browser.
Requiring language-specific secure-coding standards and automated scanning during the SDLC catches missing output encoding or improper neutralization of untrusted data before the software reaches production.
Application security requirements explicitly call for neutralization of script-related HTML tags.
Secure-coding standards, SAST scans and removal of insecure code samples together eliminate the failure to neutralize script content that produces cross-site scripting flaws.