CVE-2026-106119 (GCVE-0-2026-106119)
Vulnerability from cvelistv5 – Published: 2026-10-06 18:01 – Updated: 2026-10-06 19:45
VLAI
EPSS
VEX
Title
LangChain: MongoDBChatMessageHistory query injection can allow cross-session access
Summary
LangChain is a framework for building LLM-powered applications. Prior to 1.3.1, MongoDBChatMessageHistory does not enforce the documented string type for an untrusted structured session identifier at runtime, allowing the identifier to be interpreted as a MongoDB query condition rather than as a literal value when multiple users' histories are stored in a shared MongoDB collection. An attacker able to invoke chat-history operations can read, modify, or delete another user's stored conversation. Applications using authenticated, server-controlled string identifiers are not affected. This issue is fixed in version 1.3.1.
Severity
SSVC
Exploitation: none
Automatable: no
Technical Impact: partial
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-10-06 19:45 UTC
CWE
- CWE-943 - Improper Neutralization of Special Elements in Data Query Logic
Assigner
References
4 references
| URL | Tags |
|---|---|
| https://github.com/langchain-ai/langchainjs/secur… | x_refsource_CONFIRM |
| https://github.com/langchain-ai/langchainjs/pull/11672 | x_refsource_MISC |
| https://github.com/langchain-ai/langchainjs/commi… | x_refsource_MISC |
| https://github.com/langchain-ai/langchainjs/relea… | x_refsource_MISC |
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| langchain-ai | langchainjs |
Affected:
< 1.5.14
|
guessed | |
| @langchain | mongodb |
Affected:
< 1.3.1
|
guessed |
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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.
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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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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.
Browse all ATT&CK techniques and the vulnerabilities related to each.
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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.
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