CVE-2025-1451

March 20, 2025, 10:15 a.m.

7.5
High

Description

A vulnerability in parisneo/lollms-webui v13 arises from the server's handling of multipart boundaries in file uploads. The server does not limit or validate the length of the boundary or the characters appended to it, allowing an attacker to craft requests with excessively long boundaries, leading to resource exhaustion and eventual denial of service (DoS). Despite an attempted patch in commit 483431bb, which blocked hyphen characters from being appended to the multipart boundary, the fix is insufficient. The server remains vulnerable if other characters (e.g., '4', 'a') are used instead of hyphens. This allows attackers to exploit the vulnerability using different characters, causing resource exhaustion and service unavailability.

Product(s) Impacted

Vendor Product Versions
Parisneo
  • Lollms-webui
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

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 parisneo lollms-webui / / / / / / / /

CVSS Score

7.5 / 10

CVSS Data - 3.0

  • Attack Vector: NETWORK
  • Attack Complexity: LOW
  • Privileges Required: NONE
  • Scope: UNCHANGED
  • Confidentiality Impact: NONE
  • Integrity Impact: NONE
  • Availability Impact: HIGH
  • CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:N/A:H

    View Vector String

Timeline

Published: March 20, 2025, 10:15 a.m.
Last Modified: March 20, 2025, 10:15 a.m.

Status : Undergoing Analysis

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.