GHSA-H754-2546-79G9
Vulnerability from github – Published: 2026-09-16 18:32 – Updated: 2026-09-16 18:32
VLAI
Details
Scirius through 3.8.0 contains an arbitrary file write vulnerability in the PCAP filestore upload endpoint that allows default User role users to write attacker-controlled JSON content to filesystem paths. Attackers can supply path traversal sequences in the uploaded document's _id field to escape the intended directory and write files with .json extension to arbitrary locations as root.
Severity
{
"affected": [],
"aliases": [
"CVE-2026-92604"
],
"database_specific": {
"cwe_ids": [
"CWE-22"
],
"github_reviewed": false,
"github_reviewed_at": null,
"nvd_published_at": "2026-09-16T18:17:22Z",
"severity": "HIGH"
},
"details": "Scirius through 3.8.0 contains an arbitrary file write vulnerability in the PCAP filestore upload endpoint that allows default User role users to write attacker-controlled JSON content to filesystem paths. Attackers can supply path traversal sequences in the uploaded document\u0027s _id field to escape the intended directory and write files with .json extension to arbitrary locations as root.",
"id": "GHSA-h754-2546-79g9",
"modified": "2026-09-16T18:32:07Z",
"published": "2026-09-16T18:32:07Z",
"references": [
{
"type": "ADVISORY",
"url": "https://nvd.nist.gov/vuln/detail/CVE-2026-92604"
},
{
"type": "WEB",
"url": "https://github.com/StamusNetworks/scirius"
},
{
"type": "WEB",
"url": "https://github.com/StamusNetworks/scirius/blob/3bb49d383f4801b79e6356f9de9f25806afe0311/suricata/rest_api.py#L105-L113"
},
{
"type": "WEB",
"url": "https://github.com/geo-chen/oss/blob/main/scirius.md"
},
{
"type": "WEB",
"url": "https://www.vulncheck.com/advisories/scirius-through-3.8.0-arbitrary-file-write-via-pcap-upload"
}
],
"schema_version": "1.4.0",
"severity": [
{
"score": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:U/C:N/I:H/A:H",
"type": "CVSS_V3"
},
{
"score": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:H/VA:H/SC:N/SI:N/SA:N/E:X/CR:X/IR:X/AR:X/MAV:X/MAC:X/MAT:X/MPR:X/MUI:X/MVC:X/MVI:X/MVA:X/MSC:X/MSI:X/MSA:X/S:X/AU:X/R:X/V:X/RE:X/U:X",
"type": "CVSS_V4"
}
]
}
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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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