CVE-2023-49344 (GCVE-0-2023-49344)
Vulnerability from cvelistv5 – Published: 2023-12-14 21:31 – Updated: 2024-08-02 21:53
VLAI
EPSS
VEX
Summary
Temporary data passed between application components by Budgie Extras Window Shuffler applet could potentially be viewed or manipulated. The data is stored in a location that is accessible to any user who has local access to the system. Attackers may pre-create and control this file to present false information to users or deny access to the application and panel.
Severity
6 (Medium)
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://github.com/UbuntuBudgie/budgie-extras/sec… | issue-tracking |
| https://ubuntu.com/security/notices/USN-6556-1 | third-party-advisory |
| https://cve.mitre.org/cgi-bin/cvename.cgi?name=CV… | issue-tracking |
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Ubuntu Budgie | Budgie Extras |
Affected:
v1.4.0 , < v1.7.1
(semver)
|
Date Public
2023-12-14 00:00
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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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