CVE-2025-51859
Published: 22 July 2025
Summary
CVE-2025-51859 is a medium-severity Cross-site Scripting (CWE-79) vulnerability. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 46.5% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog.
This vulnerability is AI-related — categorised as LLM Application Platforms; in the LLM/Generative AI Risks risk domain.
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-22327
Vulnerability details
Stored Cross-Site Scripting (XSS) vulnerability in Chaindesk thru 2025-05-26 in its agent chat component. An attacker can achieve arbitrary client-side script execution by crafting an AI agent whose system prompt instructs the underlying Large Language Model (LLM) to embed malicious…
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script payloads (e.g., SVG-based XSS) into its chat responses. When a user interacts with such a malicious agent or accesses a direct link to a conversation containing an XSS payload, the script executes in the user's browser. Successful exploitation can lead to the theft of sensitive information, such as JWT session tokens, potentially resulting in account hijacking.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- LLM/Generative AI Risks
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: ai, large language model, llm
Related Threats
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
Stored XSS via AI agent responses enables exploitation of public-facing web app (T1190), content injection (T1659), client-side JavaScript execution (T1059.007), and theft of web session tokens like JWT (T1539).
Affected Assets
Mitigating Controls
Likely Mitigating Controls AI
Per-CVE control mapping for this CVE has not run yet; the list below is derived from the weakness types (CWEs) cited in the NVD entry.
Penetration testing submits XSS payloads to web applications, detecting cross-site scripting flaws for subsequent remediation.
Validates web inputs to reject script-related content that could produce XSS.
Output validation against expected content can reject or sanitize script content in generated web pages, reducing XSS exploitability.