FKIE_CVE-2026-61598
Vulnerability from fkie_nvd - Published: 2026-09-16 14:17 - Updated: 2026-09-16 16:17
Severity
Summary
djust provides Phoenix LiveView-style reactive server-side rendering for Django with Rust-powered performance. Prior to version 1.0.7, `djust.mixins.model_binding.ModelBindingMixin` provides a default `update_model` event handler and is part of the LiveView base MRO, so every LiveView exposes it. It `setattr`s a view attribute whose name is client-supplied (`field`), gated only by: reject `_`-prefixed names; reject a 14-entry denylist of framework internals (`FORBIDDEN_MODEL_FIELDS`); optional `allowed_model_fields` which defaults to None = allow all; and `hasattr` existence. As a result, a client can set any public, existing view attribute — not just the fields actually bound with `dj-model=` in the rendered template. The denylist covers framework plumbing but nothing about developer business/authz state, and the allowlist is opt-in (off by default). A developer who binds one `dj-model="search"` input and also keeps `self.account_id` / `self.is_admin` / `self.total_price` as view state does not realize a client can set ALL of them via `{type:event, event:"update_model", params:{field, value}}` over the WebSocket. Type coercion matches the target attribute's type (so `"true"` -> bool True), aiding the attacker. This issue is fixed in djust 1.0.7. As a workaround, set `allowed_model_fields` explicitly on every view using dj-model (or subclassing LiveView) to the minimal list of bindable fields; do not keep authorization/ownership state in public view attributes that share the view with dj-model bindings.
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.mixins.model_binding.ModelBindingMixin` provides a default `update_model` event handler and is part of the LiveView base MRO, so every LiveView exposes it. It `setattr`s a view attribute whose name is client-supplied (`field`), gated only by: reject `_`-prefixed names; reject a 14-entry denylist of framework internals (`FORBIDDEN_MODEL_FIELDS`); optional `allowed_model_fields` which defaults to None = allow all; and `hasattr` existence. As a result, a client can set any public, existing view attribute \u2014 not just the fields actually bound with `dj-model=` in the rendered template. The denylist covers framework plumbing but nothing about developer business/authz state, and the allowlist is opt-in (off by default). A developer who binds one `dj-model=\"search\"` input and also keeps `self.account_id` / `self.is_admin` / `self.total_price` as view state does not realize a client can set ALL of them via `{type:event, event:\"update_model\", params:{field, value}}` over the WebSocket. Type coercion matches the target attribute\u0027s type (so `\"true\"` -\u003e bool True), aiding the attacker. This issue is fixed in djust 1.0.7. As a workaround, set `allowed_model_fields` explicitly on every view using dj-model (or subclassing LiveView) to the minimal list of bindable fields; do not keep authorization/ownership state in public view attributes that share the view with dj-model bindings."
}
],
"id": "CVE-2026-61598",
"lastModified": "2026-09-16T16:17:14.220",
"metrics": {
"cvssMetricV40": [
{
"cvssData": {
"Automatable": "NOT_DEFINED",
"Recovery": "NOT_DEFINED",
"Safety": "NOT_DEFINED",
"attackComplexity": "LOW",
"attackRequirements": "NONE",
"attackVector": "NETWORK",
"availabilityRequirement": "NOT_DEFINED",
"baseScore": 7.1,
"baseSeverity": "HIGH",
"confidentialityRequirement": "NOT_DEFINED",
"exploitMaturity": "NOT_DEFINED",
"integrityRequirement": "NOT_DEFINED",
"modifiedAttackComplexity": "NOT_DEFINED",
"modifiedAttackRequirements": "NOT_DEFINED",
"modifiedAttackVector": "NOT_DEFINED",
"modifiedPrivilegesRequired": "NOT_DEFINED",
"modifiedSubAvailabilityImpact": "NOT_DEFINED",
"modifiedSubConfidentialityImpact": "NOT_DEFINED",
"modifiedSubIntegrityImpact": "NOT_DEFINED",
"modifiedUserInteraction": "NOT_DEFINED",
"modifiedVulnAvailabilityImpact": "NOT_DEFINED",
"modifiedVulnConfidentialityImpact": "NOT_DEFINED",
"modifiedVulnIntegrityImpact": "NOT_DEFINED",
"privilegesRequired": "LOW",
"providerUrgency": "NOT_DEFINED",
"subAvailabilityImpact": "NONE",
"subConfidentialityImpact": "NONE",
"subIntegrityImpact": "NONE",
"userInteraction": "NONE",
"valueDensity": "NOT_DEFINED",
"vectorString": "CVSS:4.0/AV:N/AC:L/AT:N/PR:L/UI:N/VC:N/VI:H/VA:N/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",
"version": "4.0",
"vulnAvailabilityImpact": "NONE",
"vulnConfidentialityImpact": "NONE",
"vulnIntegrityImpact": "HIGH",
"vulnerabilityResponseEffort": "NOT_DEFINED"
},
"source": "security-advisories@github.com",
"type": "Secondary"
}
],
"ssvcV203": [
{
"source": "134c704f-9b21-4f2e-91b3-4a467353bcc0",
"ssvcData": {
"id": "CVE-2026-61598",
"options": [
{
"exploitation": "none"
},
{
"automatable": "no"
},
{
"technicalImpact": "partial"
}
],
"role": "CISA Coordinator",
"timestamp": "2026-09-16T15:27:26.525288Z",
"version": "2.0.3"
}
}
]
},
"published": "2026-09-16T14:17:06.827",
"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-cc7c-9jff-58wj"
}
],
"sourceIdentifier": "security-advisories@github.com",
"vulnStatus": "Received",
"weaknesses": [
{
"description": [
{
"lang": "en",
"value": "CWE-915"
}
],
"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.
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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