GSD-2017-7561
Vulnerability from gsd - Updated: 2023-12-13 01:21Details
Red Hat JBoss EAP version 3.0.7 through before 4.0.0.Beta1 is vulnerable to a server-side cache poisoning or CORS requests in the JAX-RS component resulting in a moderate impact.
Aliases
Aliases
{
"GSD": {
"alias": "CVE-2017-7561",
"description": "Red Hat JBoss EAP version 3.0.7 through before 4.0.0.Beta1 is vulnerable to a server-side cache poisoning or CORS requests in the JAX-RS component resulting in a moderate impact.",
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"https://access.redhat.com/errata/RHSA-2018:0479",
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"https://access.redhat.com/errata/RHSA-2018:0002"
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"remediation": "unknown",
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{
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"cwe_ids": [
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"CWE-444",
"CWE-937"
],
"date": "2022-07-01",
"description": "Red Hat JBoss EAP version 3.0.7 through before 4.0.0.Beta1 is vulnerable to a server-side cache poisoning or CORS requests in the JAX-RS component resulting in a moderate impact.",
"fixed_versions": [
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"3.5.0.CR1"
],
"identifier": "CVE-2017-7561",
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"package_slug": "maven/org.jboss.resteas/resteasy-jaxrs",
"pubdate": "2022-05-13",
"solution": "Upgrade to versions 3.0.25.Final, 3.5.0.CR1 or above.",
"title": "Inconsistent Interpretation of HTTP Requests (\u0027HTTP Request Smuggling\u0027)",
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"impact": {
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"accessVector": "NETWORK",
"authentication": "NONE",
"availabilityImpact": "NONE",
"baseScore": 5.0,
"confidentialityImpact": "NONE",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:L/Au:N/C:N/I:P/A:N",
"version": "2.0"
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"exploitabilityScore": 10.0,
"impactScore": 2.9,
"obtainAllPrivilege": false,
"obtainOtherPrivilege": false,
"obtainUserPrivilege": false,
"severity": "MEDIUM",
"userInteractionRequired": false
},
"baseMetricV3": {
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"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.5,
"baseSeverity": "HIGH",
"confidentialityImpact": "NONE",
"integrityImpact": "HIGH",
"privilegesRequired": "NONE",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.0/AV:N/AC:L/PR:N/UI:N/S:U/C:N/I:H/A:N",
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},
"lastModifiedDate": "2019-10-03T00:03Z",
"publishedDate": "2017-09-13T17:29Z"
}
}
}
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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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