CVE-2026-43979: CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') in LearningCircuit local-deep-research
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.
AI Analysis
Technical Summary
Local Deep Research versions before 1.6.0 contain an XSS vulnerability in the PDFService._markdown_to_html() function. This function constructs HTML documents by embedding user-supplied values such as research titles and metadata into an f-string without HTML escaping. An authenticated attacker can exploit this to inject arbitrary HTML during PDF export, which is processed by WeasyPrint. The injected HTML can then be used to bypass SSRF defenses in ssrf_validator.py, enabling server-side request forgery. The vulnerability is addressed in version 1.6.0.
Potential Impact
The vulnerability allows an authenticated attacker to inject arbitrary HTML into documents generated during PDF export, leading to cross-site scripting. This injection can be chained to bypass existing SSRF protections, potentially allowing the attacker to make unauthorized server-side requests. The CVSS score is 5.0 (medium severity), indicating limited confidentiality impact and no integrity or availability impact.
Mitigation Recommendations
This vulnerability is fixed in LearningCircuit local-deep-research version 1.6.0. Users should upgrade to version 1.6.0 or later to remediate this issue. No official patch link is provided, but upgrading to the fixed version is the recommended action. Since this is not a cloud service, remediation depends on user action to update the software.
CVE-2026-43979: CWE-79: Improper Neutralization of Input During Web Page Generation ('Cross-site Scripting') in LearningCircuit local-deep-research
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.
CVSS v3.1
Score 5.0medium
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
Local Deep Research versions before 1.6.0 contain an XSS vulnerability in the PDFService._markdown_to_html() function. This function constructs HTML documents by embedding user-supplied values such as research titles and metadata into an f-string without HTML escaping. An authenticated attacker can exploit this to inject arbitrary HTML during PDF export, which is processed by WeasyPrint. The injected HTML can then be used to bypass SSRF defenses in ssrf_validator.py, enabling server-side request forgery. The vulnerability is addressed in version 1.6.0.
Potential Impact
The vulnerability allows an authenticated attacker to inject arbitrary HTML into documents generated during PDF export, leading to cross-site scripting. This injection can be chained to bypass existing SSRF protections, potentially allowing the attacker to make unauthorized server-side requests. The CVSS score is 5.0 (medium severity), indicating limited confidentiality impact and no integrity or availability impact.
Mitigation Recommendations
This vulnerability is fixed in LearningCircuit local-deep-research version 1.6.0. Users should upgrade to version 1.6.0 or later to remediate this issue. No official patch link is provided, but upgrading to the fixed version is the recommended action. Since this is not a cloud service, remediation depends on user action to update the software.
Technical Details
- Data Version
- 5.2
- Assigner Short Name
- GitHub_M
- Date Reserved
- 2026-05-04T20:24:31.916Z
- Cvss Version
- 3.1
- State
- PUBLISHED
- Remediation Level
- null
Threat ID: 6a188e05e29bf47b501d67c1
Added to database: 5/28/2026, 6:48:37 PM
Last enriched: 5/28/2026, 7:05:36 PM
Last updated: 5/29/2026, 11:01:32 AM
Views: 7
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