CVE-2017-3000 (GCVE-0-2017-3000)
Vulnerability from cvelistv5 – Published: 2017-03-14 16:00 – Updated: 2024-08-05 14:09
VLAI
EPSS
VEX
Summary
Adobe Flash Player versions 24.0.0.221 and earlier have a vulnerability in the random number generator used for constant blinding. Successful exploitation could lead to information disclosure.
Severity
6.5 (Medium)
CWE
- Use of Insufficiently Random Values
Assigner
References
5 references
| URL | Tags |
|---|---|
| http://www.securityfocus.com/bid/96862 | vdb-entryx_refsource_BID |
| https://helpx.adobe.com/security/products/flash-p… | x_refsource_CONFIRM |
| https://security.gentoo.org/glsa/201703-02 | vendor-advisoryx_refsource_GENTOO |
| http://www.securitytracker.com/id/1037994 | vdb-entryx_refsource_SECTRACK |
| http://rhn.redhat.com/errata/RHSA-2017-0526.html | vendor-advisoryx_refsource_REDHAT |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| n/a | Adobe Flash Player 24.0.0.221 and earlier. |
Affected:
Adobe Flash Player 24.0.0.221 and earlier.
|
Date Public
2017-03-14 00:00
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"id": "CVE-2017-3000",
"lastModified": "2024-11-21T03:24:37.277",
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"redhat_vex": {
"aggregate_severity": "Critical",
"current_release_date": "2026-06-28T11:13:11+00:00",
"cve": "CVE-2017-3000",
"id": "CVE-2017-3000",
"initial_release_date": "2017-03-14T00:00:00+00:00",
"product_status:fixed": "3",
"product_status:known_affected": "1",
"source": "Red Hat CSAF VEX",
"status": "final",
"title": "flash-plugin: multiple code execution issues fixed in APSB17-07",
"url": "https://security.access.redhat.com/data/csaf/v2/vex/2017/cve-2017-3000.json",
"version": "3"
},
"suse_vex": {
"aggregate_severity": "important",
"current_release_date": "2026-05-13T16:11:54Z",
"cve": "CVE-2017-3000",
"id": "CVE-2017-3000",
"initial_release_date": "2023-02-15T04:52:24Z",
"product_status:recommended": "8",
"source": "SUSE CSAF VEX",
"status": "interim",
"title": "SUSE CVE CVE-2017-3000",
"url": "https://ftp.suse.com/pub/projects/security/csaf-vex/cve-2017-3000.json",
"version": "8"
}
}
}
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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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