CVE-2016-0829 (GCVE-0-2016-0829)
Vulnerability from cvelistv5 – Published: 2016-03-12 21:00 – Updated: 2024-08-05 22:30
VLAI
EPSS
VEX
Summary
The BnGraphicBufferProducer::onTransact function in libs/gui/IGraphicBufferConsumer.cpp in mediaserver in Android 4.x before 4.4.4, 5.x before 5.1.1 LMY49H, and 6.x before 2016-03-01 does not initialize a certain output data structure, which allows attackers to obtain sensitive information, and consequently bypass an unspecified protection mechanism, by triggering a QUEUE_BUFFER action, as demonstrated by obtaining Signature or SignatureOrSystem access, aka internal bug 26338109.
Severity
7.5 (High)
CWE
- n/a
Assigner
References
3 references
| URL | Tags |
|---|---|
| https://android.googlesource.com/platform/framewo… | x_refsource_CONFIRM |
| http://www.securityfocus.com/bid/84261 | vdb-entryx_refsource_BID |
| http://source.android.com/security/bulletin/2016-… | x_refsource_CONFIRM |
Date Public
2016-03-07 00:00
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"lastModified": "2024-11-21T02:42:27.937",
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anteriores a 2016-03-01 no inicializa una cierta estructura de datos de salida, lo que permite a atacantes obtener informaci\u00f3n sensible, y consecuentemente eludir un mecanismo de protecci\u00f3n no especificado, desencadenando una acci\u00f3n QUEUE_BUFFER, seg\u00fan lo demostrado mediante la obtenci\u00f3n de acceso a Signature o SignatureOrSystem, tambi\u00e9n conocida como error 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Advisory\"]},{\"url\":\"http://www.securityfocus.com/bid/84261\",\"source\":\"security@android.com\"},{\"url\":\"https://android.googlesource.com/platform/frameworks/native/+/d06421fd37fbb7fd07002e6738fac3a223cb1a62\",\"source\":\"security@android.com\"},{\"url\":\"http://source.android.com/security/bulletin/2016-03-01.html\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\",\"tags\":[\"Vendor Advisory\"]},{\"url\":\"http://www.securityfocus.com/bid/84261\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\"},{\"url\":\"https://android.googlesource.com/platform/frameworks/native/+/d06421fd37fbb7fd07002e6738fac3a223cb1a62\",\"source\":\"af854a3a-2127-422b-91ae-364da2661108\"}]}}"
}
}
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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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