CVE-2026-34311 (GCVE-0-2026-34311)
Vulnerability from cvelistv5 – Published: 2026-05-28 20:17 – Updated: 2026-05-29 03:56
VLAI
EPSS
VEX
Summary
Vulnerability in the Oracle Hospitality OPERA 5 Property Services product of Oracle Hospitality Applications (component: Opera). Supported versions that are affected are 5.6.19.24, 5.6.22, 5.6.25.19, 5.6.27.6 and 5.6.28. Easily exploitable vulnerability allows unauthenticated attacker with network access via HTTP to compromise Oracle Hospitality OPERA 5 Property Services. Successful attacks of this vulnerability can result in takeover of Oracle Hospitality OPERA 5 Property Services. CVSS 3.1 Base Score 9.8 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:N/AC:L/PR:N/UI:N/S:U/C:H/I:H/A:H).
Severity
9.8 (Critical)
SSVC
Exploitation: none
Automatable: yes
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-05-28 00:00 UTC
CWE
- Easily exploitable vulnerability allows unauthenticated attacker with network access via HTTP to compromise Oracle Hospitality OPERA 5 Property Services. Successful attacks of this vulnerability can result in takeover of Oracle Hospitality OPERA 5 Property Services.
- CWE-noinfo Not enough information
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://www.oracle.com/security-alerts/cspumay2026.html | vendor-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Oracle Corporation | Oracle Hospitality OPERA 5 Property Services |
Affected:
5.6.19.24
(semver)
Affected: 5.6.22 (semver) Affected: 5.6.25.19 (semver) Affected: 5.6.27.6 (semver) Affected: 5.6.28 (semver) cpe:2.3:a:oracle:hospitality_opera_5_property_services:5.6.19.24:*:*:*:*:*:*:* cpe:2.3:a:oracle:hospitality_opera_5_property_services:5.6.22:*:*:*:*:*:*:* cpe:2.3:a:oracle:hospitality_opera_5_property_services:5.6.25.19:*:*:*:*:*:*:* cpe:2.3:a:oracle:hospitality_opera_5_property_services:5.6.27.6:*:*:*:*:*:*:* cpe:2.3:a:oracle:hospitality_opera_5_property_services:5.6.28:*:*:*:*:*:*:* |
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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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