CVE-2026-55253 (GCVE-0-2026-55253)
Vulnerability from cvelistv5 – Published: 2026-09-14 17:09 – Updated: 2026-09-14 17:09
VLAI
EPSS
VEX
Title
LangChain MongoDB: NoSQL Operator Injection in MongoDBSaver.list() leading to cross-tenant data exposure
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.
Severity
7.7 (High)
CWE
- CWE-943 - Improper Neutralization of Special Elements in Data Query Logic
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://github.com/langchain-ai/langchain-mongodb… | x_refsource_CONFIRM |
| https://github.com/langchain-ai/langchain-mongodb… | x_refsource_MISC |
| https://github.com/langchain-ai/langchain-mongodb… | x_refsource_MISC |
| https://github.com/langchain-ai/langchain-mongodb… | x_refsource_MISC |
| https://github.com/langchain-ai/langchain-mongodb… | x_refsource_MISC |
| https://github.com/langchain-ai/langchain-mongodb… | x_refsource_MISC |
| https://github.com/langchain-ai/langchain-mongodb… | x_refsource_MISC |
Impacted products
3 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| langchain-ai | langchain-mongodb |
Affected:
< 0.4.0
|
guessed | |
| langchain-ai | langgraph-checkpoint-mongodb |
Affected:
< 0.3.0
|
guessed | |
| langchain-ai | langgraph-store-mongodb |
Affected:
< 0.4.0
|
guessed |
{
"containers": {
"cna": {
"affected": [
{
"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"
}
]
}
],
"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."
}
],
"metrics": [
{
"cvssV3_1": {
"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"
}
}
],
"problemTypes": [
{
"descriptions": [
{
"cweId": "CWE-943",
"description": "CWE-943: Improper Neutralization of Special Elements in Data Query Logic",
"lang": "en",
"type": "CWE"
}
]
}
],
"providerMetadata": {
"dateUpdated": "2026-09-14T17:09:04.088Z",
"orgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"shortName": "GitHub_M"
},
"references": [
{
"name": "https://github.com/langchain-ai/langchain-mongodb/security/advisories/GHSA-533j-2v4q-mw5h",
"tags": [
"x_refsource_CONFIRM"
],
"url": "https://github.com/langchain-ai/langchain-mongodb/security/advisories/GHSA-533j-2v4q-mw5h"
},
{
"name": "https://github.com/langchain-ai/langchain-mongodb/pull/384",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/langchain-ai/langchain-mongodb/pull/384"
},
{
"name": "https://github.com/langchain-ai/langchain-mongodb/commit/14a6cc39e67d23fd409cd13a9caae2c329df0a09",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/langchain-ai/langchain-mongodb/commit/14a6cc39e67d23fd409cd13a9caae2c329df0a09"
},
{
"name": "https://github.com/langchain-ai/langchain-mongodb/commit/240e7ecee432ea006d9fef6ea506bfd2e009a3f4",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/langchain-ai/langchain-mongodb/commit/240e7ecee432ea006d9fef6ea506bfd2e009a3f4"
},
{
"name": "https://github.com/langchain-ai/langchain-mongodb/commit/5465e4d3ea0ef5c88a666a6442bd853ff4bd70e5",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/langchain-ai/langchain-mongodb/commit/5465e4d3ea0ef5c88a666a6442bd853ff4bd70e5"
},
{
"name": "https://github.com/langchain-ai/langchain-mongodb/releases/tag/libs/langgraph-checkpoint-mongodb/v0.4.0",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/langchain-ai/langchain-mongodb/releases/tag/libs/langgraph-checkpoint-mongodb/v0.4.0"
},
{
"name": "https://github.com/langchain-ai/langchain-mongodb/releases/tag/libs/langgraph-store-mongodb/v0.3.0",
"tags": [
"x_refsource_MISC"
],
"url": "https://github.com/langchain-ai/langchain-mongodb/releases/tag/libs/langgraph-store-mongodb/v0.3.0"
}
],
"source": {
"advisory": "GHSA-533j-2v4q-mw5h",
"discovery": "UNKNOWN"
},
"title": "LangChain MongoDB: NoSQL Operator Injection in MongoDBSaver.list() leading to cross-tenant data exposure"
}
},
"cveMetadata": {
"assignerOrgId": "a0819718-46f1-4df5-94e2-005712e83aaa",
"assignerShortName": "GitHub_M",
"cveId": "CVE-2026-55253",
"datePublished": "2026-09-14T17:09:04.088Z",
"dateReserved": "2026-06-16T16:44:00.625Z",
"dateUpdated": "2026-09-14T17:09:04.088Z",
"state": "PUBLISHED"
},
"dataType": "CVE_RECORD",
"dataVersion": "5.2"
}
Loading…
Loading…
Experimental. This forecast is provided for visualization only and may change without notice. Do not use it for operational decisions.
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…
The MITRE ATT&CK techniques below are AI-generated suggestions, inferred from the description of the
vulnerability by the CIRCL/vulnerability-attack-technique-classification-roberta-base
model, served locally by ML-Gateway.
They have not been verified by an analyst and are provided for guidance only.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
The approach is described in our paper Mapping CVEs to MITRE ATT&CK Techniques: A Curated Gold-Set Classifier and the Limits of LLM-Assisted Label Expansion.
Loading…
Loading…