Threats affecting pkg:github/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.

CVE-2026-46526 is a Server-Side Request Forgery (SSRF) vulnerability in LearningCircuit's local-deep-research product prior to version 1.6.10. The issue arises from a logical flaw in the URL validation logic, where differences between the URL parsing library used for validation and the one used for sending requests allow attackers to bypass SSRF protections. This vulnerability has a medium severity rating with a CVSS score of 5. The vulnerability is fixed in version 1.6.10. No known exploits are reported in the wild, and no official patch advisory details are provided beyond the version fix.

Local Deep Research is an AI-powered research assistant for deep, iterative research. In versions from 1.3.0 to before 1.3.9, the download service (download_service.py) makes HTTP requests using raw requests.get() without utilizing the application's SSRF protection (safe_requests.py). This can allow attackers to access internal services and attempt to reach cloud provider metadata endpoints (AWS/GCP/Azure), as well as perform internal network reconnaissance, by submitting malicious URLs through the API, depending on the deployment and surrounding controls. This issue has been patched in version 1.3.9.