Vulnerability-Lookup

CVE-2022-34177 (GCVE-0-2022-34177)

Vulnerability from cvelistv5 – Published: 2022-06-22 00:00 – Updated: 2024-08-03 08:16

Summary

Jenkins Pipeline: Input Step Plugin 448.v37cea_9a_10a_70 and earlier archives files uploaded for `file` parameters for Pipeline `input` steps on the controller as part of build metadata, using the parameter name without sanitization as a relative path inside a build-related directory, allowing attackers able to configure Pipelines to create or replace arbitrary files on the Jenkins controller file system with attacker-specified content.

Severity

7.5 (High)


                        
                          CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N

Assigner

jenkins mitre root

References

1 reference

URL	Tags
https://www.jenkins.io/security/advisory/2022-06-…

Impacted products

1 product

Vendor	Product	Version	CPE status
Jenkins project	Jenkins Pipeline: Input Step Plugin	Unaffected: 447.449.v193fd29f6021 Unaffected: 2.12.2 Affected: unspecified , ≤ 448.v37cea_9a_10a_70 (custom)	guessed

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WID-SEC-W-2022-0445

Vulnerability from csaf_certbund - Published: 2022-06-22 22:00 - Updated: 2023-02-22 23:00

Summary

Jenkins: Mehrere Schwachstellen

Severity

Mittel

Scope

45 vulnerabilities in this advisory, including CVE-2022-34177.

CVE-2022-34177

Es existieren mehrere Schwachstellen in Jenkins Core und verschiedenen Plugins. Zu diesen Plugins gehören: Agent Server Parameter Plugin, Beaker builder Plugin, Convertigo Mobile Platform Plugin, CRX Content Package Deployer Plugin, Date Parameter Plugin, Dynamic Extended, Choice Parameter Plugin, EasyQA Plugin, Embeddable Build Status Plugin, Filesystem List Parameter Plugin, Hidden Parameter Plugin, Image Tag Parameter Plugin, Jianliao Notification Plugin, JUnit Plugin, Maven Metadata Plugin for Jenkins CI server Plugin, Nested View Plugin, NS-ND Integration Performance Publisher Plugin, ontrack Jenkins Plugin, Package Version Plugin, Pipeline: Input Step Plugin, Readonly Parameter Plugin, Repository Connector Plugin, REST List Parameter Plugin, Sauce OnDemand Plugin, Squash TM Publisher (Squash4Jenkins) Plugin, Stash Branch Parameter Plugin, ThreadFix Plugin, vRealize Orchestrator Plugin, xUnit Plugin. Ein entfernter, anonymer oder authentisierter Angreifer kann diese Schwachstellen ausnutzen, um Sicherheitsmaßnahmen zu umgehen, beliebigen Code auszuführen, einen Cross-Site-Scripting-Angriff durchzuführen und Daten zu manipulieren. Die erfolgreiche Ausnutzung einiger dieser Schwachstellen erfordert eine Benutzerinteraktion.

Known affected: 3 products

Open the full advisory

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.