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CVE-2025-68700VulnerabilityPublished Updated

CVE-2025-68700: infiniflow ragflow · OS Command Injection

Affects infiniflow ragflowSource CVE Database V5

0

Not known to be exploited · CVSS 8.6 High : upgrade to 0.23.0 soon.

Data as of 26 Oct 2026, 06:26 UTC

CVSS 4.0 base score
8.6High
How severe the flaw is on the 0–10 CVSS scale.
EPSS
0.7% chance of exploitation in 30 days – higher than 53% of CVEsExploit Prediction Scoring System: the likelihood attackers will use this flaw soon.
Patch
Available – fixed in 0.23.0
CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:H/VI:H/VA:H/SC:H/SI:H/SA:H/E:P

Exploitation evidence

  1. Disclosed: evidence found.

    CVE published 31 Dec 2025

  2. Detectable: no evidence on record.

    No public nuclei template indexed

  3. Weaponised: no evidence on record.

    No public exploit code indexed in Metasploit or Exploit-DB

  4. Exploited in the wild: no evidence on record.

    Not in CISA KEV and no source reports exploitation

  5. Observed by OffSeq Mirage: no evidence on record.

    No attack traffic recorded by OffSeq Mirage sensors

A missing step is not evidence of safety. Sources: cve.org (opens in a new tab), the Metasploit, Exploit-DB and nuclei-templates index as of 2026-10-26.

What to do

Upgrade infiniflow/ragflow to 0.23.0.

Affected software

ProductAffectedFixed in
GitHubinfiniflow/ragflowpkg:github/infiniflow/ragflowOther threatsAffected
  • <0.23.0
Fixed in
  • 0.23.0

Check your own hosts for these packages with threat-finder (opens in a new tab), our free open-source scanner.

AI-suggested

To mitigate CVE-2025-68700, European organizations should immediately upgrade all RAGFlow deployments to version 0.23.0 or later, which contains the official patch addressing the unsafe eval() usage and access control issues. In addition to patching, organizations should conduct a comprehensive audit of all RAGFlow endpoints to verify proper access control enforcement and correct any inverted permission logic. Implement strict input validation and sanitization on all user-supplied data, especially in components that execute or parse code. Employ network segmentation and least privilege principles to limit the exposure of RAGFlow servers. Monitor logs and system behavior for unusual command execution patterns indicative of exploitation attempts. Consider deploying runtime application self-protection (RASP) or endpoint detection and response (EDR) solutions to detect and block malicious command execution. Finally, establish incident response procedures tailored to potential RAGFlow compromises and ensure backups are current to enable recovery from any successful attacks.

Description

RAGFlow is an open-source RAG (Retrieval-Augmented Generation) engine. In versions prior to 0.23.0, a low-privileged authenticated user (normal login account) can execute arbitrary system commands on the server host process via the frontend Canvas CodeExec component, completely bypassing sandbox isolation. This occurs because untrusted data (stdout) is parsed using eval() with no filtering or sandboxing. The intended design was to "automatically convert string results into Python objects," but this effectively executes attacker-controlled code. Additional endpoints lack access control or contain inverted permission logic, significantly expanding the attack surface and enabling chained exploitation. Version 0.23.0 contains a patch for the issue.

References

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