RHSA-2016_1272
Vulnerability from csaf_redhat - Published: 2016-06-21 22:39 - Updated: 2024-11-14 19:56Summary
Red Hat Security Advisory: python-django-horizon security, bug fix, and enhancement update
Severity
Important
Notes
Topic: An update for python-django-horizon is now available for Red Hat
Enterprise Linux OpenStack Platform 7.0 (Kilo) for RHEL 7.
Red Hat Product Security has rated this update as having a security
impact of Important. A Common Vulnerability Scoring System (CVSS) base
score, which gives a detailed severity rating, is available for each
vulnerability from the CVE link(s) in the References section.
Details: OpenStack Dashboard (Horizon) provides administrators and users with a
graphical interface to access, provision, and automate cloud-based
resources.
The following packages have been upgraded to a newer upstream version:
python-django-horizon: 2015.1.4 (BZ#1345822)
Security Fix(es):
* A DOM-based, cross-site scripting vulnerability was found in the
OpenStack dashboard, where user input was not filtered correctly. An
authenticated dashboard user could exploit the flaw by injecting an
AngularJS template into a dashboard form (for example, using an image's
description), triggering the vulnerability when another user browsed
the affected page. As a result, this flaw could result in user accounts
being compromised (for example, user-access credentials being stolen).
(CVE-2016-4428)
Red Hat would like to thank the OpenStack project for reporting this issue.
Upstream acknowledges Beth Lancaster (Virginia Tech) and Brandon Sawyers
(Virginia Tech) as the original reporters.
Terms of Use: This content is licensed under the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/). If you distribute this content, or a modified version of it, you must provide attribution to Red Hat Inc. and provide a link to the original.
A DOM-based, cross-site scripting vulnerability was found in the OpenStack dashboard, where user input was not filtered correctly. An authenticated dashboard user could exploit the flaw by injecting an AngularJS template into a dashboard form (for example, using an image's description), triggering the vulnerability when another user browsed the affected page. As a result, this flaw could result in user accounts being compromised (for example, user-access credentials being stolen).
4.9 ()
Affected products
Fixed
5 products
| Product | Identifier | Version | Remediation |
|---|---|---|---|
| Unresolved product id: 7Server-RH7-RHOS-7.0:openstack-dashboard-0:2015.1.4-1.el7ost.noarch | — |
Vendor Fix
fix
|
|
| Unresolved product id: 7Server-RH7-RHOS-7.0:openstack-dashboard-theme-0:2015.1.4-1.el7ost.noarch | — |
Vendor Fix
fix
|
|
| Unresolved product id: 7Server-RH7-RHOS-7.0:python-django-horizon-0:2015.1.4-1.el7ost.noarch | — |
Vendor Fix
fix
|
|
| Unresolved product id: 7Server-RH7-RHOS-7.0:python-django-horizon-0:2015.1.4-1.el7ost.src | — |
Vendor Fix
fix
|
|
| Unresolved product id: 7Server-RH7-RHOS-7.0:python-django-horizon-doc-0:2015.1.4-1.el7ost.noarch | — |
Vendor Fix
fix
|
Threats
Impact
Important
References
9 references
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"text": "OpenStack Dashboard (Horizon) provides administrators and users with a\ngraphical interface to access, provision, and automate cloud-based\nresources.\n\nThe following packages have been upgraded to a newer upstream version:\npython-django-horizon: 2015.1.4 (BZ#1345822)\n\nSecurity Fix(es):\n\n* A DOM-based, cross-site scripting vulnerability was found in the\nOpenStack dashboard, where user input was not filtered correctly. An\nauthenticated dashboard user could exploit the flaw by injecting an\nAngularJS template into a dashboard form (for example, using an image\u0027s\ndescription), triggering the vulnerability when another user browsed\nthe affected page. As a result, this flaw could result in user accounts\nbeing compromised (for example, user-access credentials being stolen).\n(CVE-2016-4428)\n\nRed Hat would like to thank the OpenStack project for reporting this issue.\nUpstream acknowledges Beth Lancaster (Virginia Tech) and Brandon Sawyers\n(Virginia Tech) as the original reporters.",
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"title": "Vulnerability description"
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"text": "python-django-horizon: XSS in client side template",
"title": "Vulnerability summary"
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"date": "2016-06-21T22:39:29+00:00",
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"accessVector": "NETWORK",
"authentication": "SINGLE",
"availabilityImpact": "NONE",
"baseScore": 4.9,
"confidentialityImpact": "PARTIAL",
"integrityImpact": "PARTIAL",
"vectorString": "AV:N/AC:M/Au:S/C:P/I:P/A:N",
"version": "2.0"
},
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"title": "python-django-horizon: XSS in client side template"
}
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}
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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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