CVE-2024-47826

Nov. 8, 2024, 3:41 p.m.

6.1
Medium

Description

eLabFTW is an open source electronic lab notebook for research labs. A vulnerability in versions prior to 5.1.5 allows an attacker to inject arbitrary HTML tags in the pages: "experiments.php" (show mode), "database.php" (show mode) or "search.php". It works by providing HTML code in the extended search string, which will then be displayed back to the user in the error message. This means that injected HTML will appear in a red "alert/danger" box, and be part of an error message. Due to some other security measures, it is not possible to execute arbitrary javascript from this attack. As such, this attack is deemed low impact. Users should upgrade to at least version 5.1.5 to receive a patch. No known workarounds are available.

Product(s) Impacted

Vendor Product Versions
Elabftw
  • Elabftw
  • *

Weaknesses

Common security weaknesses mapped to this vulnerability.

CWE-79
Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting')
The product does not neutralize or incorrectly neutralizes user-controllable input before it is placed in output that is used as a web page that is served to other users.

*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 elabftw elabftw / / / / / / / /

CVSS Score

6.1 / 10

CVSS Data - 3.1

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

    View Vector String

Timeline

Published: Oct. 14, 2024, 6:15 p.m.
Last Modified: Nov. 8, 2024, 3:41 p.m.

Status : Analyzed

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

More info

Source

security-advisories@github.com

*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.