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EUVD-2026-192513
European Vulnerability Database identifier assigned by ENISAReserved
2026-10-02 06:43
Assigner
ENISA
Alias of
a CVE record, shown under related vulnerabilities.
This identifier carries no description, severity or references of its own:
they belong to that CVE.
{
"assigner": "ENISA",
"date_reserved": "2026-10-02T06:43:32.940890+00:00",
"id": "EUVD-2026-192513"
}
CVE-2017-10388 (GCVE-0-2017-10388)
Vulnerability from cvelistv5 – Published: 2017-10-19 17:00 – Updated: 2024-10-04 16:44
VLAI
EPSS
VEX
Summary
Vulnerability in the Java SE, Java SE Embedded component of Oracle Java SE (subcomponent: Libraries). Supported versions that are affected are Java SE: 6u161, 7u151, 8u144 and 9; Java SE Embedded: 8u144. Difficult to exploit vulnerability allows unauthenticated attacker with network access via Kerberos to compromise Java SE, Java SE Embedded. Successful attacks require human interaction from a person other than the attacker. Successful attacks of this vulnerability can result in takeover of Java SE, Java SE Embedded. Note: Applies to the Java SE Kerberos client. CVSS 3.0 Base Score 7.5 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.0/AV:N/AC:H/PR:N/UI:R/S:U/C:H/I:H/A:H).
Severity
No CVSS data available.
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2024-10-04 15:38 UTC
CWE
- Difficult to exploit vulnerability allows unauthenticated attacker with network access via Kerberos to compromise Java SE, Java SE Embedded. Successful attacks require human interaction from a person other than the attacker. Successful attacks of this vulnerability can result in takeover of Java SE, Java SE Embedded.
Assigner
References
19 references
Impacted products
1 product
| Vendor | Product | Version | |
|---|---|---|---|
| Oracle Corporation | Java |
Affected:
Java SE: 6u161
Affected: 7u151 Affected: 8u144 Affected: 9; Java SE Embedded: 8u144 |
Date Public
2017-10-17 00:00
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Trend slope:
-
(linear fit over daily sighting counts)
Show additional events:
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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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