Vulnerability-Lookup

CNVD-2019-12461

Vulnerability from cnvd - Published: 2019-04-26

Title

Lobby Track Desktop权限提升漏洞（CNVD-2019-12461）

Description

Jolly Technologies Lobby Track Desktop是美国Jolly Technologies公司的一款桌面访客管理应用程序。该程序具有预先注册访客、捕获照片和扫描驾驶执照等功能。 Jolly Technologies Lobby Track Desktop 8.2.186版本中存在提权漏洞。本地攻击者可利用该漏洞获取提升的权限。

Severity

中

Formal description

厂商尚未提供漏洞修复方案，请关注厂商主页更新： https://www.jollytech.com/lobbytrack/desktop-features.php

Reference

https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-17487

Impacted products

Name
Jolly Technologies Lobby Track Desktop 8.2.186

Show details on source website

JSON

To clipboard

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      "cveNumber": "CVE-2018-17487"
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  "discovererName": "Hannah Robbins and Scott Brink from X-Force Red research team.",
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  "isEvent": "\u901a\u7528\u8f6f\u786c\u4ef6\u6f0f\u6d1e",
  "number": "CNVD-2019-12461",
  "openTime": "2019-04-26",
  "products": {
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  "referenceLink": "https://cve.mitre.org/cgi-bin/cvename.cgi?name=CVE-2018-17487",
  "serverity": "\u4e2d",
  "submitTime": "2019-03-20",
  "title": "Lobby Track Desktop\u6743\u9650\u63d0\u5347\u6f0f\u6d1e\uff08CNVD-2019-12461\uff09"
}

CVE-2018-17487 (GCVE-0-2018-17487)

Vulnerability from cvelistv5 – Published: 2019-03-19 19:47 – Updated: 2024-09-16 22:03

Summary

Lobby Track Desktop could allow a local attacker to gain elevated privileges on the system, caused by an error in the printer dialog. By visiting the kiosk and signing in as a visitor, an attacker could exploit this vulnerability using the command line to break out of kiosk mode.

Severity

8.4 (High)


                        
                          CVSS:3.0/C:H/AC:L/I:H/A:H/PR:N/AV:L/S:U/UI:N/RC:R/E:U/RL:U

CWE

Gain Privileges

Assigner

ibm mitre root

References

1 reference

URL	Tags
https://exchange.xforce.ibmcloud.com/vulnerabilit…	vdb-entryx_refsource_XF

Impacted products

1 product

Vendor	Product	Version
Jolly Technologies	Lobby Track Desktop	Affected: 8.2.186

Date Public

2019-03-04 00:00

Show details on NVD website

JSON

To clipboard

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            "attackComplexity": "LOW",
            "attackVector": "LOCAL",
            "availabilityImpact": "HIGH",
            "baseScore": 8.4,
            "baseSeverity": "HIGH",
            "confidentialityImpact": "HIGH",
            "exploitCodeMaturity": "UNPROVEN",
            "integrityImpact": "HIGH",
            "privilegesRequired": "NONE",
            "remediationLevel": "UNAVAILABLE",
            "reportConfidence": "REASONABLE",
            "scope": "UNCHANGED",
            "temporalScore": 7.4,
            "temporalSeverity": "HIGH",
            "userInteraction": "NONE",
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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.

Detection rules are retrieved from Rulezet.

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.

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.