CVE-2017-9608 (GCVE-0-2017-9608)
Vulnerability from cvelistv5 – Published: 2017-12-27 19:00 – Updated: 2024-08-05 17:11
VLAI
EPSS
VEX
Summary
The dnxhd decoder in FFmpeg before 3.2.6, and 3.3.x before 3.3.3 allows remote attackers to cause a denial of service (NULL pointer dereference) via a crafted mov file.
Severity
6.5 (Medium)
CWE
- n/a
Assigner
References
7 references
| URL | Tags |
|---|---|
| https://github.com/FFmpeg/FFmpeg/commit/0a709e2a1… | x_refsource_CONFIRM |
| http://www.securityfocus.com/bid/100348 | vdb-entryx_refsource_BID |
| https://www.debian.org/security/2017/dsa-3957 | vendor-advisoryx_refsource_DEBIAN |
| https://github.com/FFmpeg/FFmpeg/commit/611b35627… | x_refsource_CONFIRM |
| http://www.openwall.com/lists/oss-security/2017/08/15/8 | mailing-listx_refsource_MLIST |
| https://github.com/FFmpeg/FFmpeg/commit/31c1c0b46… | x_refsource_CONFIRM |
| http://www.openwall.com/lists/oss-security/2017/08/14/1 | mailing-listx_refsource_MLIST |
Date Public
2017-06-14 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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