Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:L/A:NCVSS and EPSS are reproduced from their sources (NVD, FIRST EPSS). Risk Priority is our own derived reading, not an NVD score.
Summary
CVE-2024-11824 is a high-severity Cross-site Scripting (CWE-79) vulnerability in Langgenius Dify. Its CVSS base score is 7.6 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Protocol or Service Impersonation (T1001.003); ranked at the 40th 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 Privacy and Disclosure 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-2024-11824 is a stored cross-site scripting (XSS) vulnerability in the langgenius/dify application, specifically within its chat log functionality. The flaw affects the latest version prior to the patch and occurs because certain HTML tags, such as <input> and <form>, are not properly disallowed. This allows attackers to inject malicious HTML into chat logs via prompts. The vulnerability carries a CVSS v3.1 base score of 7.6 (AV:N/AC:L/PR:L/UI:R/S:C/C:H/I:L/A:N) and maps to CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting').
A low-privileged user (PR:L) can exploit the vulnerability by submitting prompts containing malicious HTML, which becomes stored in the chat log. When an administrator views the tainted log, the injected script executes in the admin's browser context due to insufficient sanitization, enabling the attacker to steal the admin's credentials or other sensitive information. Exploitation requires network access and user interaction from the victim (UI:R), but achieves high confidentiality impact with changed scope (S:C).
The issue is addressed in Dify version 0.12.1, with the fix implemented in commit 55edd5047e6fcbc9bb56a4ea055fcce090f3eb5d available at https://github.com/langgenius/dify/commit/55edd5047e6fcbc9bb56a4ea055fcce090f3eb5d. Security teams should prioritize upgrading to the patched version. Further details, including the original report, are provided in the Huntr bounty advisory at https://huntr.com/bounties/72387deb-6e64-48ed-a8c3-b50d22a0970f.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2025-7040
Vulnerability Data
A stored cross-site scripting (XSS) vulnerability exists in langgenius/dify version latest, specifically in the chat log functionality. The vulnerability arises because certain HTML tags like <input> and <form> are not disallowed, allowing an attacker to inject malicious HTML into the…
more
log via prompts. When an admin views the log containing the malicious HTML, the attacker could steal the admin's credentials or sensitive information. This issue is fixed in version 0.12.1.
- CWE(s)
AI Security AnalysisAI
- AI Category
- LLM Application Platforms
- Risk Domain
- Privacy and Disclosure
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: dify
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
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V1.1.2V1.3.2
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.
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).
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.
A.5.6 only promotes membership in groups to improve knowledge, receive alerts, and share information; it performs no action that stops XSS exploitation (or even the underlying neutralization defect) and is a pure dependency for other controls per the owner's labeling rule.