CVE-2026-76280 (GCVE-0-2026-76280)
Vulnerability from cvelistv5 – Published: 2026-10-07 20:46 – Updated: 2026-10-07 20:46
VLAI
EPSS
VEX
Title
Incorrect Permission Assignment for App Key Value Store Collections in Splunk Secure Gateway
Summary
In Splunk Enterprise versions below 10.4.3, 10.2.7, 10.0.10, and 9.4.15, and Splunk Secure Gateway versions below 3.10.11, 3.9.25, and 3.8.72, an authenticated user who does not hold the "admin" or "sc_admin" Splunk roles could modify Splunk Secure Gateway alert and mobile-device recipient data in App Key Value Store (KV Store) collections that later alert and subscription workflows use. The vulnerability is possible because the affected collections allow unrestricted write access instead of limiting writes to authorized Splunk Secure Gateway workflows. For more information see About the app key value store (https://help.splunk.com/en/splunk-enterprise/administer/admin-manual/10.4/administer-the-app-key-value-store/about-the-app-key-value-store), KV store endpoint descriptions (https://help.splunk.com/en/splunk-enterprise/leverage-rest-apis/rest-api-reference/10.4/kv-store-endpoints/kv-store-endpoint-descriptions), and About configuring role-based user access (https://help.splunk.com/en/splunk-enterprise/administer/manage-users-and-security/10.4/manage-splunk-platform-users-and-roles/about-configuring-role-based-user-access) in the Splunk documentation.
Severity
6.3 (Medium)
CWE
- CWE-732 - The product specifies permissions for a security-critical resource in a way that allows that resource to be read or modified by unintended actors.
Assigner
References
1 reference
Impacted products
2 products
| Vendor | Product | Version | CPE status | |
|---|---|---|---|---|
| Splunk | Splunk Enterprise |
Affected:
10.4 , < 10.4.3
(custom)
Affected: 10.2 , < 10.2.7 (custom) Affected: 10.0 , < 10.0.10 (custom) Affected: 9.4 , < 9.4.15 (custom) |
guessed | |
| Splunk | Splunk Secure Gateway |
Affected:
3.10 , < 3.10.11
(custom)
Affected: 3.9 , < 3.9.25 (custom) Affected: 3.8 , < 3.8.72 (custom) |
guessed |
Date Public
2026-10-07 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
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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