FKIE_CVE-2026-55253

Vulnerability from fkie_nvd - Published: 2026-09-14 18:17 - Updated: 2026-09-30 17:43
Summary
LangChain MongoDB provides integrations between MongoDB, Atlas, LangChain, and LangGraph. Prior to langgraph-checkpoint-mongodb 0.3.0 and langgraph-store-mongodb 0.4.0, MongoDBSaver.list(), MongoDBSaver.alist(), and MongoDBStore.search() incorporate filter dictionaries into MongoDB queries without recursively rejecting keys prefixed with $. An authenticated caller who controls a filter argument through HTTP query parameters, request body fields, or agent tool arguments can inject MongoDB Query Language operators such as $regex or $where. In a multi-tenant deployment that uses the filter to enforce per-user or per-tenant isolation, injected operators can bypass intended equality filtering and expose other tenants' checkpoint or store data. Filters constructed entirely from trusted server-side values have lower practical risk. This issue is fixed in langgraph-checkpoint-mongodb 0.3.0 and langgraph-store-mongodb 0.4.0.
Impacted products
Vendor Product Version

{
  "affected": [
    {
      "affectedData": [
        {
          "product": "langchain-mongodb",
          "vendor": "langchain-ai",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 0.4.0"
            }
          ]
        },
        {
          "product": "langgraph-checkpoint-mongodb",
          "vendor": "langchain-ai",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 0.3.0"
            }
          ]
        },
        {
          "product": "langgraph-store-mongodb",
          "vendor": "langchain-ai",
          "versions": [
            {
              "status": "affected",
              "version": "\u003c 0.4.0"
            }
          ]
        }
      ],
      "source": "security-advisories@github.com"
    }
  ],
  "cveTags": [],
  "descriptions": [
    {
      "lang": "en",
      "value": "LangChain MongoDB provides integrations between MongoDB, Atlas, LangChain, and LangGraph. Prior to langgraph-checkpoint-mongodb 0.3.0 and langgraph-store-mongodb 0.4.0, MongoDBSaver.list(), MongoDBSaver.alist(), and MongoDBStore.search() incorporate filter dictionaries into MongoDB queries without recursively rejecting keys prefixed with $. An authenticated caller who controls a filter argument through HTTP query parameters, request body fields, or agent tool arguments can inject MongoDB Query Language operators such as $regex or $where. In a multi-tenant deployment that uses the filter to enforce per-user or per-tenant isolation, injected operators can bypass intended equality filtering and expose other tenants\u0027 checkpoint or store data. Filters constructed entirely from trusted server-side values have lower practical risk. This issue is fixed in langgraph-checkpoint-mongodb 0.3.0 and langgraph-store-mongodb 0.4.0."
    }
  ],
  "id": "CVE-2026-55253",
  "lastModified": "2026-09-30T17:43:24.057",
  "metrics": {
    "cvssMetricV31": [
      {
        "cvssData": {
          "attackComplexity": "LOW",
          "attackVector": "NETWORK",
          "availabilityImpact": "NONE",
          "baseScore": 7.7,
          "baseSeverity": "HIGH",
          "confidentialityImpact": "HIGH",
          "integrityImpact": "NONE",
          "privilegesRequired": "LOW",
          "scope": "CHANGED",
          "userInteraction": "NONE",
          "vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N",
          "version": "3.1"
        },
        "exploitabilityScore": 3.1,
        "impactScore": 4.0,
        "source": "security-advisories@github.com",
        "type": "Secondary"
      }
    ],
    "ssvcV203": [
      {
        "source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
        "ssvcData": {
          "id": "CVE-2026-55253",
          "options": [
            {
              "exploitation": "none"
            },
            {
              "automatable": "no"
            },
            {
              "technicalImpact": "partial"
            }
          ],
          "role": "CISA Coordinator",
          "timestamp": "2026-09-14T19:20:51.424583Z",
          "version": "2.0.3"
        }
      }
    ]
  },
  "published": "2026-09-14T18:17:55.500",
  "references": [
    {
      "source": "security-advisories@github.com",
      "url": "https://github.com/langchain-ai/langchain-mongodb/commit/14a6cc39e67d23fd409cd13a9caae2c329df0a09"
    },
    {
      "source": "security-advisories@github.com",
      "url": "https://github.com/langchain-ai/langchain-mongodb/commit/240e7ecee432ea006d9fef6ea506bfd2e009a3f4"
    },
    {
      "source": "security-advisories@github.com",
      "url": "https://github.com/langchain-ai/langchain-mongodb/commit/5465e4d3ea0ef5c88a666a6442bd853ff4bd70e5"
    },
    {
      "source": "security-advisories@github.com",
      "url": "https://github.com/langchain-ai/langchain-mongodb/pull/384"
    },
    {
      "source": "security-advisories@github.com",
      "url": "https://github.com/langchain-ai/langchain-mongodb/releases/tag/libs/langgraph-checkpoint-mongodb/v0.4.0"
    },
    {
      "source": "security-advisories@github.com",
      "url": "https://github.com/langchain-ai/langchain-mongodb/releases/tag/libs/langgraph-store-mongodb/v0.3.0"
    },
    {
      "source": "security-advisories@github.com",
      "url": "https://github.com/langchain-ai/langchain-mongodb/security/advisories/GHSA-533j-2v4q-mw5h"
    }
  ],
  "sourceIdentifier": "security-advisories@github.com",
  "vulnStatus": "Awaiting Analysis",
  "weaknesses": [
    {
      "description": [
        {
          "lang": "en",
          "value": "CWE-943"
        }
      ],
      "source": "security-advisories@github.com",
      "type": "Secondary"
    }
  ]
}



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…

Loading…

Loading…

Related by attack behaviour

Vulnerabilities whose description is nearest to this one in the vector space of the CIRCL/vulnerability-attack-technique-biencoder model. This is a similarity search over the bi-encoder space (plain cosine), not a classification, and it has no measured accuracy.


Loading…