CVE-2026-43979

May 28, 2026, 8:16 p.m.

5.0
Medium

Description

Local Deep Research is an AI-powered research assistant for deep, iterative research. Prior to 1.6.0, PDFService._markdown_to_html() constructs an HTML document by interpolating user-controlled values — specifically title (sourced from research.title or research.query) and metadata key-value pairs — directly into an f-string without any HTML escaping. An authenticated attacker can craft a research query containing HTML special characters to inject arbitrary HTML tags into the document processed by WeasyPrint during PDF export. This injection can be chained to trigger a Server-Side Request Forgery (SSRF), bypassing the application's existing SSRF defenses in ssrf_validator.py. This vulnerability is fixed in 1.6.0.

Product(s) Impacted

Vendor Product Versions
Local Deep Research
  • Local Deep Research
  • <1.6.0

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 local_deep_research local_deep_research <1.6.0 / / / / / / /

CVSS Score

5.0 / 10

CVSS Data - 3.1

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

    View Vector String

Timeline

Published: May 28, 2026, 7:16 p.m.
Last Modified: May 28, 2026, 8:16 p.m.

Status : Received

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

More info

*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.