FKIE_CVE-2026-61595
Vulnerability from fkie_nvd - Published: 2026-09-16 16:17 - Updated: 2026-09-30 17:51
Severity
Summary
djust provides Phoenix LiveView-style reactive server-side rendering for Django with Rust-powered performance. Prior to version 1.0.7, `djust.tenants` isolation was enforced only on the HTTP path. The current tenant was stored in `threading.local()` and set exclusively by the HTTP-only `TenantMiddleware`, so on the live (WebSocket/SSE) path `get_current_tenant()` was always `None` during mount and every event handler — and the tenant-aware `QuerySet` manager failed OPEN (returned the unfiltered queryset, ignoring `STRICT_MODE`), disclosing every tenant's rows to whoever held the socket. `threading.local` was additionally shared across connections on the `sync_to_async` executor thread. This issue is fixed in djust 1.0.7. Tenant storage moved to a `contextvars.ContextVar` (per async task); the resolved tenant is bound around WS/SSE mount and every dispatch; both managers scope the base queryset once and fail CLOSED (`.none()` under the default `STRICT_MODE`); and system check S006 warns when `STRICT_MODE=False`. No known workarounds are available on the live path.
References
Impacted products
| Vendor | Product | Version |
|---|
{
"affected": [
{
"affectedData": [
{
"product": "djust",
"vendor": "djust-org",
"versions": [
{
"status": "affected",
"version": "\u003c 1.0.7"
}
]
}
],
"source": "security-advisories@github.com"
}
],
"cveTags": [],
"descriptions": [
{
"lang": "en",
"value": "djust provides Phoenix LiveView-style reactive server-side rendering for Django with Rust-powered performance. Prior to version 1.0.7, `djust.tenants` isolation was enforced only on the HTTP path. The current tenant was stored in `threading.local()` and set exclusively by the HTTP-only `TenantMiddleware`, so on the live (WebSocket/SSE) path `get_current_tenant()` was always `None` during mount and every event handler \u2014 and the tenant-aware `QuerySet` manager failed OPEN (returned the unfiltered queryset, ignoring `STRICT_MODE`), disclosing every tenant\u0027s rows to whoever held the socket. `threading.local` was additionally shared across connections on the `sync_to_async` executor thread. This issue is fixed in djust 1.0.7. Tenant storage moved to a `contextvars.ContextVar` (per async task); the resolved tenant is bound around WS/SSE mount and every dispatch; both managers scope the base queryset once and fail CLOSED (`.none()` under the default `STRICT_MODE`); and system check S006 warns when `STRICT_MODE=False`. No known workarounds are available on the live path."
}
],
"id": "CVE-2026-61595",
"lastModified": "2026-09-30T17:51:56.193",
"metrics": {
"cvssMetricV31": [
{
"cvssData": {
"attackComplexity": "LOW",
"attackVector": "NETWORK",
"availabilityImpact": "NONE",
"baseScore": 7.7,
"baseSeverity": "HIGH",
"confidentialityImpact": "HIGH",
"integrityImpact": "NONE",
"privilegesRequired": "LOW",
"scope": "CHANGED",
"userInteraction": "NONE",
"vectorString": "CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N",
"version": "3.1"
},
"exploitabilityScore": 3.1,
"impactScore": 4.0,
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-61595",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-09-16T18:18:43.770267Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-09-16T16:17:14.080",
"references": [
{
"source": "security-advisories@github.com",
"url": "https://github.com/djust-org/djust/releases/tag/v1.0.7"
},
{
"source": "security-advisories@github.com",
"url": "https://github.com/djust-org/djust/security/advisories/GHSA-3492-cvg7-9mr2"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Deferred",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-636"
},
{
"lang": "en",
"value": "CWE-862"
}
],
"source": "security-advisories@github.com",
"type": "Secondary"
}
]
}
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…
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.
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.
Loading…
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.
Loading…