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vLLM vulnerable to Server-Side Request Forgery (SSRF) in `MediaConnector`

Type: advisorySources: OSV

vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.14.1, a Server-Side Request Forgery (SSRF) vulnerability exists in the `MediaConnector` class within the vLLM project's multimodal feature set. The load_from_url and load_from_url_async methods obtain and process media from URLs provided by users, using different Python parsing libraries when restricting the target host. These two parsing libraries have different interpretations of backslashes, which allows the host name restriction to be bypassed. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources. This vulnerability is particularly critical in containerized environments like `llm-d`, where a compromised vLLM pod could be used to scan the internal network, interact with other pods, and potentially cause denial of service or access sensitive data. For example, an attacker could make the vLLM pod send malicious requests to an internal `llm-d` management endpoint, leading to system instability by falsely reporting metrics like the KV cache state. Version 0.14.1 contains a patch for the issue.

Products
Not specified
Affected versions
Not specified
Fixed versions
Not specified
CVEs
CVE-2026-24779
Confirmed fixed CVEs
Not specified
Updated in this feed
3 Nov 2026, 02:19 UTC · Revision 1

Source evidence

vLLM vulnerable to Server-Side Request Forgery (SSRF) in `MediaConnector`

core-osv · Original source

Published 27 Jan 2026, 22:01 UTC

vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.14.1, a Server-Side Request Forgery (SSRF) vulnerability exists in the `MediaConnector` class within the vLLM project's multimodal feature set. The load_from_url and load_from_url_async methods obtain and process media from URLs provided by users, using different Python parsing libraries when restricting the target host. These two parsing libraries have different interpretations of backslashes, which allows the host name restriction to be bypassed. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources. This vulnerability is particularly critical in containerized environments like `llm-d`, where a compromised vLLM pod could be used to scan the internal network, interact with other pods, and potentially cause denial of service or access sensitive data. For example, an attacker could make the vLLM pod send malicious requests to an internal `llm-d` management endpoint, leading to system instability by falsely reporting metrics like the KV cache state. Version 0.14.1 contains a patch for the issue.

osv:CVE-2026-24779
Complete source record
{
  "id": "d45bb3a61fc9670350e36fba5191eef95da634329deade409ba9f8712dec0564",
  "url": "https://osv.dev/vulnerability/CVE-2026-24779",
  "method": "structured",
  "payload": {
    "kind": "advisory",
    "title": "vLLM vulnerable to Server-Side Request Forgery (SSRF) in `MediaConnector`",
    "summary": "vLLM is an inference and serving engine for large language models (LLMs). Prior to version 0.14.1, a Server-Side Request Forgery (SSRF) vulnerability exists in the `MediaConnector` class within the vLLM project's multimodal feature set. The load_from_url and load_from_url_async methods obtain and process media from URLs provided by users, using different Python parsing libraries when restricting the target host. These two parsing libraries have different interpretations of backslashes, which allows the host name restriction to be bypassed. This allows an attacker to coerce the vLLM server into making arbitrary requests to internal network resources. This vulnerability is particularly critical in containerized environments like `llm-d`, where a compromised vLLM pod could be used to scan the internal network, interact with other pods, and potentially cause denial of service or access sensitive data. For example, an attacker could make the vLLM pod send malicious requests to an internal `llm-d` management endpoint, leading to system instability by falsely reporting metrics like the KV cache state. Version 0.14.1 contains a patch for the issue.",
    "evidence": "osv:CVE-2026-24779",
    "products": [],
    "severity": "high",
    "structured": {
      "id": "CVE-2026-24779",
      "aliases": [
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        "GHSA-qh4c-xf7m-gxfc",
        "PYSEC-2026-2020"
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          "database_specific": {
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      ],
      "modified": "2026-10-28T02:30:25.576695022Z",
      "provider": "osv",
      "references": [
        "https://security.access.redhat.com/data/csaf/v2/vex/2026/cve-2026-24779.json",
        "https://access.redhat.com/errata/RHSA-2026:10184",
        "https://access.redhat.com/errata/RHSA-2026:19712",
        "https://access.redhat.com/errata/RHSA-2026:30087",
        "https://access.redhat.com/errata/RHSA-2026:30088",
        "https://access.redhat.com/errata/RHSA-2026:30089",
        "https://access.redhat.com/errata/RHSA-2026:3461",
        "https://access.redhat.com/errata/RHSA-2026:3462",
        "https://access.redhat.com/errata/RHSA-2026:3782",
        "https://access.redhat.com/errata/RHSA-2026:42644",
        "https://access.redhat.com/security/cve/CVE-2026-24779",
        "https://github.com/CVEProject/cvelistV5/tree/main/cves/2026/24xxx/CVE-2026-24779.json",
        "https://github.com/vllm-project/vllm/security/advisories/GHSA-qh4c-xf7m-gxfc",
        "https://nvd.nist.gov/vuln/detail/CVE-2026-24779",
        "https://bugzilla.redhat.com/show_bug.cgi?id=2433624",
        "https://github.com/vllm-project/vllm/commit/f46d576c54fb8aeec5fc70560e850bed38ef17d7",
        "https://github.com/vllm-project/vllm/pull/32746"
      ],
      "provider_metadata": {
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        "cna_assigner": "GitHub_M",
        "osv_generated_from": "https://github.com/CVEProject/cvelistV5/tree/main/cves/2026/24xxx/CVE-2026-24779.json"
      }
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    "related_cves": [
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    "security_relevant": true,
    "severity_evidence": "/severity/0/score"
  },
  "source_id": "core-osv",
  "document_id": 491854,
  "published_at": "2026-01-27T22:01:13.808+00:00"
}