FKIE_CVE-2026-76397
Vulnerability from fkie_nvd - Published: 2026-08-19 22:17 - Updated: 2026-08-20 13:01
Severity
Summary
In Splunk AI Toolkit versions below 6.0.0, a user who holds the "power" Splunk role could access and delete all relevant data in experiment history, including data associated with other users. The vulnerability is possible because Splunk AI Toolkit does not preserve the trusted experiment scope when it processes caller-controlled query values before accessing restricted history data. For more information see Experiment Assistants (https://help.splunk.com/en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/experiment-assistants) in the Splunk documentation.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "Splunk AI Toolkit",
"vendor": "Splunk",
"versions": [
{
"lessThan": "6.0.0",
"status": "affected",
"version": "5.7",
"versionType": "custom"
}
]
}
],
"source": "psirt@cisco.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "In Splunk AI Toolkit versions below 6.0.0, a user who holds the \"power\" Splunk role could access and delete all relevant data in experiment history, including data associated with other users. The vulnerability is possible because Splunk AI Toolkit does not preserve the trusted experiment scope when it processes caller-controlled query values before accessing restricted history data. For more information see Experiment Assistants (https://help.splunk.com/en/splunk-cloud-platform/apply-machine-learning/use-ai-toolkit/5.6.4/experiment-assistants) in the Splunk documentation."
}
],
"id": "CVE-2026-76397",
"lastModified": "2026-08-20T13:01:40.103",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 8.1,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "HIGH",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:N",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 5.2,
"source": "psirt@cisco.com",
"type": "Primary"
}
]
},
"published": "2026-08-19T22:17:26.270",
"references": [
{
"source": "psirt@cisco.com",
"url": "https://advisory.splunk.com/advisories/SVD-2026-0808"
}
],
"sourceIdentifier": "psirt@cisco.com",
"vulnStatus": "Undergoing Analysis",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-639"
}
],
"source": "psirt@cisco.com",
"type": "Primary"
}
]
}
Loading…
Loading…
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.
Loading…
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.
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.
Loading…
Loading…