CVE-2026-83247 (GCVE-0-2026-83247)
Vulnerability from cvelistv5 – Published: 2026-09-15 20:04 – Updated: 2026-09-17 13:10
VLAI
EPSS
VEX
Summary
Vulnerability in the Oracle Commerce Guided Search / Oracle Commerce Experience Manager product of Oracle Commerce (component: Forge). The supported version that is affected is 11.4.0. Easily exploitable vulnerability allows unauthenticated attacker with logon to the infrastructure where Oracle Commerce Guided Search / Oracle Commerce Experience Manager executes to compromise Oracle Commerce Guided Search / Oracle Commerce Experience Manager. Successful attacks require human interaction from a person other than the attacker. Successful attacks of this vulnerability can result in takeover of Oracle Commerce Guided Search / Oracle Commerce Experience Manager. CVSS 3.1 Base Score 7.8 (Confidentiality, Integrity and Availability impacts). CVSS Vector: (CVSS:3.1/AV:L/AC:L/PR:N/UI:R/S:U/C:H/I:H/A:H).
Severity
7.8 (High)
SSVC
Exploitation: none
Automatable: no
Technical Impact: total
CISA Coordinator · CISA-ADP (v2.0.3)
Decision recorded 2026-09-17 13:01 UTC
CWE
- Easily exploitable vulnerability allows unauthenticated attacker with logon to the infrastructure where Oracle Commerce Guided Search / Oracle Commerce Experience Manager executes to compromise Oracle Commerce Guided Search / Oracle Commerce Experience Manager. Successful attacks require human interaction from a person other than the attacker. Successful attacks of this vulnerability can result in takeover of Oracle Commerce Guided Search / Oracle Commerce Experience Manager.
- CWE-269 - Improper Privilege Management
Assigner
References
1 reference
| URL | Tags |
|---|---|
| https://www.oracle.com/security-alerts/cspusep2026.html | vendor-advisory |
Impacted products
1 product
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Oracle Corporation | Oracle Commerce Guided Search / Oracle Commerce Experience Manager |
Affected:
11.4.0
(semver)
cpe:2.3:a:oracle:commerce_guided_search_\/_oracle_commerce_experience_manager:11.4.0:*:*:*:*:*:*:* |
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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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