CVE-2024-10131
Nov. 1, 2024, 5:12 p.m.
8.8
High
Description
The `add_llm` function in `llm_app.py` in infiniflow/ragflow version 0.11.0 contains a remote code execution (RCE) vulnerability. The function uses user-supplied input `req['llm_factory']` and `req['llm_name']` to dynamically instantiate classes from various model dictionaries. This approach allows an attacker to potentially execute arbitrary code due to the lack of comprehensive input validation or sanitization. An attacker could provide a malicious value for 'llm_factory' that, when used as an index to these model dictionaries, results in the execution of arbitrary code.
Product(s) Impacted
| Vendor | Product | Versions |
|---|---|---|
| Infiniflow |
|
|
Weaknesses
Common security weaknesses mapped to this vulnerability.
CWE-77
Improper Neutralization of Special Elements used in a Command ('Command Injection')
The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component.
*CPE(s)
Affected systems and software identified for this CVE.
| Type | Vendor | Product | Version | Update | Edition | Language | Software Edition | Target Software | Target Hardware | Other Information |
|---|---|---|---|---|---|---|---|---|---|---|
| a | infiniflow | ragflow | 0.11.0 | / | / | / | / | / | / | / |
Tags
CVSS Score
CVSS Data - 3.1
- Attack Vector: NETWORK
- Attack Complexity: LOW
- Privileges Required: LOW
- Scope: UNCHANGED
- Confidentiality Impact: HIGH
- Integrity Impact: HIGH
- Availability Impact: HIGH
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H
Timeline
Published: Oct. 19, 2024, 4:15 a.m.
Last Modified: Nov. 1, 2024, 5:12 p.m.
Last Modified: Nov. 1, 2024, 5:12 p.m.
Status : Analyzed
CVE has been recently published to the CVE List and has been received by the NVD.
More infoSource
security@huntr.dev
*Disclaimer: Some vulnerabilities do not have an associated CPE. To enhance the data, we use AI to infer CPEs based on CVE details. This is an automated process and might not always be accurate.