CVE-2024-6959

Nov. 3, 2024, 5:15 p.m.

7.1
High

Description

A vulnerability in parisneo/lollms-webui version 9.8 allows for a Denial of Service (DOS) attack when uploading an audio file. If an attacker appends a large number of characters to the end of a multipart boundary, the system will continuously process each character, rendering lollms-webui inaccessible. This issue is exacerbated by the lack of Cross-Site Request Forgery (CSRF) protection, enabling remote exploitation. The vulnerability leads to service disruption, resource exhaustion, and extended downtime.

Product(s) Impacted

Vendor Product Versions
Lollms
  • Lollms Web Ui
  • 9.8

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-352
Cross-Site Request Forgery (CSRF)
The web application does not, or can not, sufficiently verify whether a well-formed, valid, consistent request was intentionally provided by the user who submitted the request.
CWE-400
Uncontrolled Resource Consumption
The product does not properly control the allocation and maintenance of a limited resource, thereby enabling an actor to influence the amount of resources consumed, eventually leading to the exhaustion of available resources.

*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 lollms lollms_web_ui 9.8 / / / / / / /

CVSS Score

7.1 / 10

CVSS Data - 3.1

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Privileges Required: NONE
  • Scope: UNCHANGED
  • Confidentiality Impact: NONE
  • Integrity Impact: LOW
  • Availability Impact: HIGH
  • CVSS:3.1/AV:N/AC:L/PR:N/UI:R/S:U/C:N/I:L/A:H

    View Vector String

Timeline

Published: Oct. 13, 2024, 1:15 p.m.
Last Modified: Nov. 3, 2024, 5:15 p.m.

Status : Modified

CVE has been recently published to the CVE List and has been received by the NVD.

More info

Source

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.