CVE-2016-8759 (GCVE-0-2016-8759)
Vulnerability from cvelistv5 – Published: 2017-04-02 20:00 – Updated: 2024-08-06 02:35
VLAI
EPSS
VEX
Summary
Video driver in Huawei P9 phones with software versions before EVA-AL10C00B192 and Huawei Honor 6 phones with software versions before H60-L02_6.10.1 has a stack overflow vulnerability, which allows attackers to crash the system or escalate user privilege.
Severity
7.8 (High)
CWE
- stack overflow
Assigner
References
2 references
| URL | Tags |
|---|---|
| http://www.securityfocus.com/bid/93530 | vdb-entryx_refsource_BID |
| http://www.huawei.com/en/psirt/security-advisorie… | x_refsource_CONFIRM |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | P9,Honor 6 Versions before EVA-AL10C00B192,Versions before H60-L02_6.10.1 |
Affected:
P9,Honor 6 Versions before EVA-AL10C00B192,Versions before H60-L02_6.10.1
|
Date Public
2017-03-27 00:00
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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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