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CVE-2026-43979

Estado: AplazadaMedia (5)—

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

Probabilidad de explotación (EPSS)

EPSS (Exploit Prediction Scoring System, de FIRST) estima la probabilidad de que una vulnerabilidad sea explotada en 30 días. Complementa a CVSS (impacto) y a CISA KEV (explotación confirmada).

Tecnologías afectadas (1)

⚠ Inferidas por IA a partir de la descripción — NVD aún no ha analizado esta CVE; no son CPE verificados.

Learningcircuit — Local Deep ResearchAI

CWE

Referencias

JSON original (NVD)

Mostrar
{
  "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,
          "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 — 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."
    }
  ],
  "lastModified": "2026-06-17T10:50:09.363",
  "sourceIdentifier": "security-advisories@github.com"
}