CVE-2017-12598 (GCVE-0-2017-12598)
Vulnerability from cvelistv5 – Published: 2017-08-07 01:00 – Updated: 2024-08-05 18:43
VLAI
EPSS
VEX
Summary
OpenCV (Open Source Computer Vision Library) through 3.3 has an out-of-bounds read error in the cv::RBaseStream::readBlock function in modules/imgcodecs/src/bitstrm.cpp when reading an image file by using cv::imread, as demonstrated by the 8-opencv-invalid-read-fread test case.
Severity
8.8 (High)
CWE
- n/a
Assigner
References
5 references
| URL | Tags |
|---|---|
| https://lists.debian.org/debian-lts-announce/2018… | mailing-listx_refsource_MLIST |
| https://security.gentoo.org/glsa/201712-02 | vendor-advisoryx_refsource_GENTOO |
| https://github.com/xiaoqx/pocs/blob/master/opencv.md | x_refsource_MISC |
| https://github.com/opencv/opencv/issues/9309 | x_refsource_MISC |
| https://lists.debian.org/debian-lts-announce/2021… | mailing-listx_refsource_MLIST |
Date Public
2017-08-06 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
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