CVE-2026-28797: CWE-20: Improper Input Validation in infiniflow ragflow
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine. In versions 0.24.0 and prior, a Server-Side Template Injection (SSTI) vulnerability exists in RAGFlow's Agent workflow Text Processing (StringTransform) and Message components. These components use Python's jinja2.Template (unsandboxed) to render user-supplied templates, allowing any authenticated user to execute arbitrary operating system commands on the server. At time of publication, there are no publicly available patches.
AI Analysis
Technical Summary
RAGFlow versions 0.24.0 and earlier contain a Server-Side Template Injection vulnerability in the Agent workflow's Text Processing (StringTransform) and Message components. These components utilize Python's jinja2.Template without sandboxing to render templates provided by authenticated users. This improper input validation enables execution of arbitrary OS commands by authenticated users, posing a significant security risk. The vulnerability is tracked as CVE-2026-28797 with a CVSS 4.0 score of 8.7 (high severity). No vendor advisory or patches have been published yet.
Potential Impact
Exploitation of this vulnerability allows any authenticated user to execute arbitrary operating system commands on the server hosting RAGFlow. This can lead to full system compromise, data theft, or disruption of service. The high CVSS score reflects the ease of exploitation (no user interaction required beyond authentication) and the critical impact on confidentiality, integrity, and availability.
Mitigation Recommendations
At the time of this report, no official patches or fixes are available for this vulnerability. Users should monitor the vendor's official channels for updates and advisories. As a temporary measure, restrict access to authenticated users and consider isolating the affected components to limit potential damage. Avoid exposing RAGFlow services to untrusted networks until a fix is released.
CVE-2026-28797: CWE-20: Improper Input Validation in infiniflow ragflow
Description
RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine. In versions 0.24.0 and prior, a Server-Side Template Injection (SSTI) vulnerability exists in RAGFlow's Agent workflow Text Processing (StringTransform) and Message components. These components use Python's jinja2.Template (unsandboxed) to render user-supplied templates, allowing any authenticated user to execute arbitrary operating system commands on the server. At time of publication, there are no publicly available patches.
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
RAGFlow versions 0.24.0 and earlier contain a Server-Side Template Injection vulnerability in the Agent workflow's Text Processing (StringTransform) and Message components. These components utilize Python's jinja2.Template without sandboxing to render templates provided by authenticated users. This improper input validation enables execution of arbitrary OS commands by authenticated users, posing a significant security risk. The vulnerability is tracked as CVE-2026-28797 with a CVSS 4.0 score of 8.7 (high severity). No vendor advisory or patches have been published yet.
Potential Impact
Exploitation of this vulnerability allows any authenticated user to execute arbitrary operating system commands on the server hosting RAGFlow. This can lead to full system compromise, data theft, or disruption of service. The high CVSS score reflects the ease of exploitation (no user interaction required beyond authentication) and the critical impact on confidentiality, integrity, and availability.
Mitigation Recommendations
At the time of this report, no official patches or fixes are available for this vulnerability. Users should monitor the vendor's official channels for updates and advisories. As a temporary measure, restrict access to authenticated users and consider isolating the affected components to limit potential damage. Avoid exposing RAGFlow services to untrusted networks until a fix is released.
Technical Details
- Data Version
- 5.2
- Assigner Short Name
- GitHub_M
- Date Reserved
- 2026-03-03T14:25:19.245Z
- Cvss Version
- 4.0
- State
- PUBLISHED
- Remediation Level
- null
Threat ID: 69d038800a160ebd925f4637
Added to database: 4/3/2026, 10:00:32 PM
Last enriched: 4/11/2026, 9:20:56 AM
Last updated: 5/20/2026, 9:53:30 AM
Views: 109
Community Reviews
0 reviewsCrowdsource mitigation strategies, share intel context, and vote on the most helpful responses. Sign in to add your voice and help keep defenders ahead.
Want to contribute mitigation steps or threat intel context? Sign in or create an account to join the community discussion.
Actions
Updates to AI analysis require Pro Console access. Upgrade inside Console → Billing.
External Links
Need more coverage?
Upgrade to Pro Console for AI refresh and higher limits.
For incident response and remediation, OffSeq services can help resolve threats faster.
Latest Threats
Check if your credentials are on the dark web
Instant breach scanning across billions of leaked records. Free tier available.