CVE-2026-43979

{"id": "CVE-2026-43979", "cveTags": [], "metrics": {"ssvcV203": [{"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0", "ssvcData": {"id": "CVE-2026-43979", "role": "CISA Coordinator", "options": [{"exploitation": "poc"}, {"automatable": "no"}, {"technicalImpact": "partial"}], "version": "2.0.3", "timestamp": "2026-05-28T19:33:32.262907Z"}}], "cvssMetricV31": [{"type": "Secondary", "source": "security-advisories@github.com", "cvssData": {"scope": "CHANGED", "version": "3.1", "baseScore": 5.0, "attackVector": "NETWORK", "baseSeverity": "MEDIUM", "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:L/I:N/A:N", "integrityImpact": "NONE", "userInteraction": "NONE", "attackComplexity": "LOW", "availabilityImpact": "NONE", "privilegesRequired": "LOW", "confidentialityImpact": "LOW"}, "impactScore": 1.4, "exploitabilityScore": 3.1}]}, "affected": [{"source": "security-advisories@github.com", "affectedData": [{"vendor": "LearningCircuit", "product": "local-deep-research", "versions": [{"status": "affected", "version": "< 1.6.0"}]}]}], "published": "2026-05-28T19:16:38.067", "references": [{"url": "https://github.com/LearningCircuit/local-deep-research/pull/3082", "source": "security-advisories@github.com"}, {"url": "https://github.com/LearningCircuit/local-deep-research/pull/3613", "source": "security-advisories@github.com"}, {"url": "https://github.com/LearningCircuit/local-deep-research/security/advisories/GHSA-fj2m-qvh9-jq4q", "source": "security-advisories@github.com"}, {"url": "https://github.com/LearningCircuit/local-deep-research/security/advisories/GHSA-fj2m-qvh9-jq4q", "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0"}], "vulnStatus": "Deferred", "weaknesses": [{"type": "Secondary", "source": "security-advisories@github.com", "description": [{"lang": "en", "value": "CWE-79"}, {"lang": "en", "value": "CWE-918"}]}], "descriptions": [{"lang": "en", "value": "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 \u2014 specifically title (sourced from research.title or research.query) and metadata key-value pairs \u2014 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."}], "lastModified": "2026-06-17T10:50:09.363", "sourceIdentifier": "security-advisories@github.com"}