FKIE_CVE-2026-55519
Vulnerability from fkie_nvd - Published: 2026-08-19 19:17 - Updated: 2026-08-21 22:16
Severity
Summary
Snipe-IT is an IT asset/license management system. Prior to 8.4.1, an authenticated user with generic asset edit permission can delete files attached to assets outside the user's ownership or company assignment. The destroy() methods in app/Http/Controllers/Api/UploadedFilesController.php and app/Http/Controllers/UploadedFilesController.php authorize update against the object class instead of the resolved object instance, creating an insecure direct object reference. This issue is fixed in version 8.4.1.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "snipe-it",
"vendor": "grokability",
"versions": [
{
"status": "affected",
"version": "\u003c 8.4.1"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "Snipe-IT is an IT asset/license management system. Prior to 8.4.1, an authenticated user with generic asset edit permission can delete files attached to assets outside the user\u0027s ownership or company assignment. The destroy() methods in app/Http/Controllers/Api/UploadedFilesController.php and app/Http/Controllers/UploadedFilesController.php authorize update against the object class instead of the resolved object instance, creating an insecure direct object reference. This issue is fixed in version 8.4.1."
}
],
"id": "CVE-2026-55519",
"lastModified": "2026-08-21T22:16:41.103",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "LOW",
"baseScore": 5.4,
"baseSeverity": "MEDIUM",
"confidentialityImpact": "NONE",
"integrityImpact": "LOW",
"privilegesRequired": "LOW",
"scope": "UNCHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:L/A:L",
"version": "3.1"
},
"exploitabilityScore": 2.8,
"impactScore": 2.5,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-55519",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-08-21T20:58:58.195464Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-08-19T19:17:20.263",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/grokability/snipe-it/commit/8bc7d50e35d93eee5a0d48b4923e497937cf93fd"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/grokability/snipe-it/releases/tag/v8.4.1"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/grokability/snipe-it/security/advisories/GHSA-x667-r589-43m7"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-285"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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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.
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.
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