GCVE Workshop - 22 September 2026 (14:00-18:00), Luxembourg Before The Vulnopticon Conference - Registration

CVE-2025-59953 (GCVE-0-2025-59953)

Vulnerability from cvelistv5 – Published: 2026-09-16 15:36 – Updated: 2026-09-16 15:36
VLAI
Title
LMdeploy has Remote Code Execution by Pickle Deserialization via zmq_rpc.call_and_response() in InterLM/lmdeploy
Summary
LMDeploy is a toolkit for compressing, deploying, and serving large language models. Starting in version 0.9.1 and prior to version 0.10.2, the LMdeploy implements an rpc server (AsyncRPCServer in zmq_rpc.py) for supporting the RPC communications. In its core functionality call_and_response(), I found it will directly use the pickles.loads() to deserialize the received messages without any sanitization, hence resulting in a remote code execution vulnerability by this RPC server. Version 0.10.2 contains a patch.
CWE
  • CWE-502 - Deserialization of Untrusted Data
References
Impacted products
Vendor Product Version CPE status
InternLM lmdeploy Affected: >= 0.9.1, < 0.10.2
guessed Create a notification for this product.
Show details on NVD website

{
  "containers": {
    "cna": {
      "affected": [
        {
          "product": "lmdeploy",
          "vendor": "InternLM",
          "versions": [
            {
              "status": "affected",
              "version": "\u003e= 0.9.1, \u003c 0.10.2"
            }
          ]
        }
      ],
      "descriptions": [
        {
          "lang": "en",
          "value": "LMDeploy is a toolkit for compressing, deploying, and serving large language models. Starting in version 0.9.1 and prior to version 0.10.2, the LMdeploy\u00a0implements an\u00a0rpc server (AsyncRPCServer in zmq_rpc.py)\u00a0for supporting the RPC communications. In its core functionality\u00a0call_and_response(), I found it will directly use the\u00a0pickles.loads()\u00a0to deserialize the received messages without any sanitization, hence resulting in a remote code execution vulnerability by this RPC server. Version 0.10.2 contains a patch."
        }
      ],
      "metrics": [
        {
          "cvssV3_1": {
            "attackComplexity": "LOW",
            "attackVector": "NETWORK",
            "availabilityImpact": "HIGH",
            "baseScore": 9.8,
            "baseSeverity": "CRITICAL",
            "confidentialityImpact": "HIGH",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "scope": "UNCHANGED",
            "userInteraction": "NONE",
            "vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
            "version": "3.1"
          }
        }
      ],
      "problemTypes": [
        {
          "descriptions": [
            {
              "cweId": "CWE-502",
              "description": "CWE-502: Deserialization of Untrusted Data",
              "lang": "en",
              "type": "CWE"
            }
          ]
        }
      ],
      "providerMetadata": {
        "dateUpdated": "2026-09-16T15:36:02.095Z",
        "orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
        "shortName": "GitHub_M"
      },
      "references": [
        {
          "name": "https://github.com/InternLM/lmdeploy/security/advisories/GHSA-5h8j-6crg-7rmw",
          "tags": [
            "x_refsource_CONFIRM"
          ],
          "url": "https://github.com/InternLM/lmdeploy/security/advisories/GHSA-5h8j-6crg-7rmw"
        },
        {
          "name": "https://github.com/InternLM/lmdeploy/releases/tag/v0.10.2",
          "tags": [
            "x_refsource_MISC"
          ],
          "url": "https://github.com/InternLM/lmdeploy/releases/tag/v0.10.2"
        }
      ],
      "source": {
        "advisory": "GHSA-5h8j-6crg-7rmw",
        "discovery": "UNKNOWN"
      },
      "title": "LMdeploy has Remote Code Execution by Pickle Deserialization via zmq_rpc.call_and_response() in InterLM/lmdeploy"
    }
  },
  "cveMetadata": {
    "assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
    "assignerShortName": "GitHub_M",
    "cveId": "CVE-2025-59953",
    "datePublished": "2026-09-16T15:36:02.095Z",
    "dateReserved": "2025-09-23T14:33:49.506Z",
    "dateUpdated": "2026-09-16T15:36:02.095Z",
    "state": "PUBLISHED"
  },
  "dataType": "CVE_RECORD",
  "dataVersion": "5.2",
  "vulnerability-lookup:meta": {
    "nvd": "{\"cve\":{\"id\":\"CVE-2025-59953\",\"sourceIdentifier\":\"security-advisories@github.com\",\"published\":\"2026-09-16T16:17:03.010\",\"lastModified\":\"2026-09-16T16:17:03.010\",\"vulnStatus\":\"Received\",\"cveTags\":[],\"descriptions\":[{\"lang\":\"en\",\"value\":\"LMDeploy is a toolkit for compressing, deploying, and serving large language models. Starting in version 0.9.1 and prior to version 0.10.2, the LMdeploy\u00a0implements an\u00a0rpc server (AsyncRPCServer in zmq_rpc.py)\u00a0for supporting the RPC communications. In its core functionality\u00a0call_and_response(), I found it will directly use the\u00a0pickles.loads()\u00a0to deserialize the received messages without any sanitization, hence resulting in a remote code execution vulnerability by this RPC server. Version 0.10.2 contains a patch.\"}],\"affected\":[{\"source\":\"security-advisories@github.com\",\"affectedData\":[{\"vendor\":\"InternLM\",\"product\":\"lmdeploy\",\"versions\":[{\"version\":\"\u003e= 0.9.1, \u003c 0.10.2\",\"status\":\"affected\"}]}]}],\"metrics\":{\"cvssMetricV31\":[{\"source\":\"security-advisories@github.com\",\"type\":\"Secondary\",\"cvssData\":{\"version\":\"3.1\",\"vectorString\":\"CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H\",\"baseScore\":9.8,\"baseSeverity\":\"CRITICAL\",\"attackVector\":\"NETWORK\",\"attackComplexity\":\"LOW\",\"privilegesRequired\":\"NONE\",\"userInteraction\":\"NONE\",\"scope\":\"UNCHANGED\",\"confidentialityImpact\":\"HIGH\",\"integrityImpact\":\"HIGH\",\"availabilityImpact\":\"HIGH\"},\"exploitabilityScore\":3.9,\"impactScore\":5.9}]},\"weaknesses\":[{\"source\":\"security-advisories@github.com\",\"type\":\"Primary\",\"description\":[{\"lang\":\"en\",\"value\":\"CWE-502\"}]}],\"references\":[{\"url\":\"https://github.com/InternLM/lmdeploy/releases/tag/v0.10.2\",\"source\":\"security-advisories@github.com\"},{\"url\":\"https://github.com/InternLM/lmdeploy/security/advisories/GHSA-5h8j-6crg-7rmw\",\"source\":\"security-advisories@github.com\"}]}}"
  }
}



Log in or create an account to share your comment.




Tags
Taxonomy of the tags.


Loading…

Loading…

Loading…

Forecast uses a logistic model when the trend is rising, or an exponential decay model when the trend is falling. Fitted via linearized least squares.

Sightings

Author Source Type Date Other

Nomenclature

  • Seen: The vulnerability was mentioned, discussed, or observed by the user.
  • Confirmed: The vulnerability has been validated from an analyst's perspective.
  • Published Proof of Concept: A public proof of concept is available for this vulnerability.
  • Exploited: The vulnerability was observed as exploited by the user who reported the sighting.
  • Patched: The vulnerability was observed as successfully patched by the user who reported the sighting.
  • Not exploited: The vulnerability was not observed as exploited by the user who reported the sighting.
  • Not confirmed: The user expressed doubt about the validity of the vulnerability.
  • Not patched: The vulnerability was not observed as successfully patched by the user who reported the sighting.

Loading…

Detection rules are retrieved from Rulezet.

Loading…

Loading…

Loading…