CVE-2020-15768 (GCVE-0-2020-15768)
Vulnerability from cvelistv5 – Published: 2020-09-18 13:12 – Updated: 2024-08-04 13:22
VLAI
EPSS
VEX
Summary
An issue was discovered in Gradle Enterprise 2017.3 - 2020.2.4 and Gradle Enterprise Build Cache Node 1.0 - 9.2. Unrestricted HTTP header reflection in Gradle Enterprise allows remote attackers to obtain authentication cookies, if they are able to discover a separate XSS vulnerability. This potentially allows an attacker to impersonate another user. Gradle Enterprise affected application request paths:/info/headers, /cache-info/headers, /admin-info/headers, /distribution-broker-info/headers. Gradle Enterprise Build Cache Node affected application request paths:/cache-node-info/headers.
Severity
7.5 (High)
CWE
- n/a
Assigner
References
2 references
| URL | Tags |
|---|---|
| https://github.com/gradle/gradle/security/advisories | x_refsource_MISC |
| https://security.gradle.com/advisory/CVE-2020-15768 | x_refsource_CONFIRM |
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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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