GSD-2022-48282
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
Under very specific circumstances (see Required configuration section below), a privileged user is able to cause arbitrary code to be executed which may cause further disruption to services. This is specific to applications written in C#. This affects all MongoDB .NET/C# Driver versions prior to and including v2.18.0
Following configuration must be true for the vulnerability to be applicable: * Application must written in C# taking arbitrary data from users and serializing data using _t without any validation AND
* Application must be running on a Windows host using the full .NET Framework, not .NET Core AND
* Application must have domain model class with a property/field explicitly of type System.Object or a collection of type System.Object (against MongoDB best practice) AND
* Malicious attacker must have unrestricted insert access to target database to add a _t discriminator."Following configuration must be true for the vulnerability to be applicable
Aliases
Aliases
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"alias": "CVE-2022-48282",
"description": "Under very specific circumstances (see Required configuration section below), a privileged user is able to cause arbitrary code to be executed which may cause further disruption to services. This is specific to applications written in C#. This affects all MongoDB .NET/C# Driver versions prior to and including v2.18.0",
"id": "GSD-2022-48282"
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"modified": "2023-12-13T01:19:26.031423Z",
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"date": "2023-03-03",
"description": "Under very specific circumstances (see Required configuration section below), a privileged user is able to cause arbitrary code to be executed which may cause further disruption to services. This is specific to applications written in C#. This affects all MongoDB .NET/C# Driver versions prior to and including v2.18.0",
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"CVE-2022-48282"
],
"not_impacted": "All versions starting from 2.19.0",
"package_slug": "nuget/MongoDB.Driver",
"pubdate": "2023-02-21",
"solution": "Upgrade to version 2.19.0 or above.",
"title": "Deserialization of Untrusted Data",
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"lastModifiedDate": "2023-06-21T13:15Z",
"publishedDate": "2023-02-21T19:15Z"
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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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