GSD-2023-6341
Vulnerability from gsd - Updated: 2023-12-13 01:20Details
Catalis (previously Icon Software) CMS360 allows a remote, unauthenticated attacker to view sensitive court documents by modifying document and other identifiers in URLs. The impact varies based on the intention and configuration of a specific CMS360 installation.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2023-6341",
"id": "GSD-2023-6341"
},
"gsd": {
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"remediation": "unknown",
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],
"details": "Catalis (previously Icon Software) CMS360 allows a remote, unauthenticated attacker to view sensitive court documents by modifying document and other identifiers in URLs. The impact varies based on the intention and configuration of a specific CMS360 installation.\n",
"id": "GSD-2023-6341",
"modified": "2023-12-13T01:20:32.857809Z",
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"version": {
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"baseScore": 5.3,
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"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
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},
"references": {
"reference_data": [
{
"name": "https://catalisgov.com/courts-land-records-support/",
"refsource": "MISC",
"url": "https://catalisgov.com/courts-land-records-support/"
},
{
"name": "https://github.com/qwell/disorder-in-the-court/blob/main/README-Catalis.md",
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"url": "https://github.com/qwell/disorder-in-the-court/blob/main/README-Catalis.md"
},
{
"name": "https://techcrunch.com/2023/11/30/us-court-records-systems-vulnerabilities-exposed-sealed-documents/",
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}
]
},
"source": {
"discovery": "UNKNOWN"
}
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"nodes": [
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"cpe_match": [
{
"cpe23Uri": "cpe:2.3:a:catalisgov:cms360:-:*:*:*:*:*:*:*",
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"vulnerable": true
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"description": {
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"refsource": "",
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"url": "https://catalisgov.com/courts-land-records-support/"
},
{
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},
"impact": {
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"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 5.3,
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"confidentialityImpact": "LOW",
"integrityImpact": "NONE",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
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"vectorString": "CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:L/I:N/A:N",
"version": "3.1"
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"exploitabilityScore": 3.9,
"impactScore": 1.4
}
},
"lastModifiedDate": "2023-12-08T15:22Z",
"publishedDate": "2023-11-30T18:15Z"
}
}
}
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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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