GSD-2022-22980
Vulnerability from gsd - Updated: 2023-12-13 01:19Details
A Spring Data MongoDB application is vulnerable to SpEL Injection when using @Query or @Aggregation-annotated query methods with SpEL expressions that contain query parameter placeholders for value binding if the input is not sanitized.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2022-22980",
"description": "A Spring Data MongoDB application is vulnerable to SpEL Injection when using @Query or @Aggregation-annotated query methods with SpEL expressions that contain query parameter placeholders for value binding if the input is not sanitized.",
"id": "GSD-2022-22980"
},
"gsd": {
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"exploitCode": "unknown",
"remediation": "unknown",
"reportConfidence": "confirmed",
"type": "vulnerability"
},
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],
"details": "A Spring Data MongoDB application is vulnerable to SpEL Injection when using @Query or @Aggregation-annotated query methods with SpEL expressions that contain query parameter placeholders for value binding if the input is not sanitized.",
"id": "GSD-2022-22980",
"modified": "2023-12-13T01:19:29.203403Z",
"schema_version": "1.4.0"
}
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"affected_range": "(,3.3.4],[3.4.0]",
"affected_versions": "All versions up to 3.3.4, version 3.4.0",
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"cwe_ids": [
"CWE-1035",
"CWE-917",
"CWE-937"
],
"date": "2022-06-30",
"description": "A Spring Data MongoDB application is vulnerable to SpEL Injection when using @Query or @Aggregation-annotated query methods with SpEL expressions that contain query parameter placeholders for value binding if the input is not sanitized.",
"fixed_versions": [
"3.3.5",
"3.4.1"
],
"identifier": "CVE-2022-22980",
"identifiers": [
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"GHSA-w24x-87mr-4r23"
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"not_impacted": "All versions after 3.3.4 before 3.4.0, all versions after 3.4.0",
"package_slug": "maven/org.springframework.data/spring-data-mongodb",
"pubdate": "2022-06-23",
"solution": "Upgrade to versions 3.3.5, 3.4.1 or above.",
"title": "SpEL Injection in Spring Data MongoDB",
"urls": [
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"https://tanzu.vmware.com/security/cve-2022-22980",
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"uuid": "893c482d-8443-4f45-8d1a-8a4e57409427"
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"impact": {
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"version": "2.0"
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"exploitabilityScore": 8.6,
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"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
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"severity": "MEDIUM",
"userInteractionRequired": false
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"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
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"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H",
"version": "3.1"
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"exploitabilityScore": 3.9,
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},
"lastModifiedDate": "2022-06-30T19:13Z",
"publishedDate": "2022-06-23T17:15Z"
}
}
}
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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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