Raw vector
CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:N/A:HSummary
CVE-2024-6673 is a medium-severity CSRF (CWE-352) vulnerability in Lollms Lollms Web Ui. Its CVSS base score is 6.5 (Medium).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked at the 7th 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 AC-3 (Access Enforcement) and SC-23 (Session Authenticity) — see the control section below for these in your framework.
OWASP Top 10 for Web (2025)
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2024-47724
Vulnerability Data
A Cross-Site Request Forgery (CSRF) vulnerability exists in the `install_comfyui` endpoint of the `lollms_comfyui.py` file in the parisneo/lollms-webui repository, versions v9.9 to the latest. The endpoint uses the GET method without requiring a client ID, allowing an attacker to trick…
more
a victim into installing ComfyUI. If the victim's device does not have sufficient capacity, this can result in a crash.
- 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
- The vulnerability affects lollms-webui, a web platform for running LLMs locally with integrations like ComfyUI for generative AI workflows, best fitting the 'Other Platforms' category as it is neither a framework, library, nor specific to other listed subdomains.
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Control response
—
—
—
V3.3.2V3.5.1V10.2.1
Mitigating Controls (NIST 800-53 r5) AI
Access enforcement requires verifying that state-changing requests originate from the authenticated user rather than a forged cross-site source.
Protecting session authenticity prevents attackers from replaying or forging authenticated requests via the victim's browser.
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 require anti-CSRF controls such as tokens or SameSite attributes.
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.
By denying access to phishing or malicious sites, the control lowers the likelihood that a user will be tricked into submitting a forged request that performs an unintended action on another site.
Contextual intelligence about emerging CSRF toolkits can be translated into updated anti-CSRF token or same-site policy configurations across applications.