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djust: Client mass-assignment of arbitrary view attributes via the default dj-model update_model handler

High severity GitHub Reviewed Published Jun 22, 2026 in djust-org/djust • Updated Sep 16, 2026

Package

pip djust (pip)

Affected versions

< 1.0.7

Patched versions

1.0.7

Description

Impact

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 setattrs 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.

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.

Severity High for apps that hold authorization/ownership/business state in public view attributes (the normal djust pattern) -> state tampering / IDOR / authz-flag manipulation; Low otherwise. Default-on across every LiveView. For a public (no-login) view an anonymous client can mass-assign; for an authenticated view a logged-in user can tamper their own session's view state (the IDOR/authz vector when downstream handlers act on it without re-authorizing).

Reproduced: a view with account_id/is_admin/total_price (none bound with dj-model) had all three set via update_model calls.

Patches

Restrict the default handler to fields actually exposed via dj-model=: have the template renderer record the bound-field set per render and reject any field outside it (preferred, secure + zero-config); or make allowed_model_fields fail-closed (required). Keep FORBIDDEN_MODEL_FIELDS only as defense-in-depth. Add a regression that a non-dj-model public attribute (e.g. is_admin) is rejected while a bound field still updates.

Workarounds

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

Reproducer + finding writeup retained privately by the maintainer.

References

@johnrtipton johnrtipton published to djust-org/djust Jun 22, 2026
Published to the GitHub Advisory Database Sep 16, 2026
Reviewed Sep 16, 2026
Last updated Sep 16, 2026

Severity

High

CVSS overall score

This score calculates overall vulnerability severity from 0 to 10 and is based on the Common Vulnerability Scoring System (CVSS).
/ 10

CVSS v4 base metrics

Exploitability Metrics
Attack Vector Network
Attack Complexity Low
Attack Requirements None
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality None
Integrity High
Availability None
Subsequent System Impact Metrics
Confidentiality None
Integrity None
Availability None

CVSS v4 base metrics

Exploitability Metrics
Attack Vector: This metric reflects the context by which vulnerability exploitation is possible. This metric value (and consequently the resulting severity) will be larger the more remote (logically, and physically) an attacker can be in order to exploit the vulnerable system. The assumption is that the number of potential attackers for a vulnerability that could be exploited from across a network is larger than the number of potential attackers that could exploit a vulnerability requiring physical access to a device, and therefore warrants a greater severity.
Attack Complexity: This metric captures measurable actions that must be taken by the attacker to actively evade or circumvent existing built-in security-enhancing conditions in order to obtain a working exploit. These are conditions whose primary purpose is to increase security and/or increase exploit engineering complexity. A vulnerability exploitable without a target-specific variable has a lower complexity than a vulnerability that would require non-trivial customization. This metric is meant to capture security mechanisms utilized by the vulnerable system.
Attack Requirements: This metric captures the prerequisite deployment and execution conditions or variables of the vulnerable system that enable the attack. These differ from security-enhancing techniques/technologies (ref Attack Complexity) as the primary purpose of these conditions is not to explicitly mitigate attacks, but rather, emerge naturally as a consequence of the deployment and execution of the vulnerable system.
Privileges Required: This metric describes the level of privileges an attacker must possess prior to successfully exploiting the vulnerability. The method by which the attacker obtains privileged credentials prior to the attack (e.g., free trial accounts), is outside the scope of this metric. Generally, self-service provisioned accounts do not constitute a privilege requirement if the attacker can grant themselves privileges as part of the attack.
User interaction: This metric captures the requirement for a human user, other than the attacker, to participate in the successful compromise of the vulnerable system. This metric determines whether the vulnerability can be exploited solely at the will of the attacker, or whether a separate user (or user-initiated process) must participate in some manner.
Vulnerable System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the VULNERABLE SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the VULNERABLE SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the VULNERABLE SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
Subsequent System Impact Metrics
Confidentiality: This metric measures the impact to the confidentiality of the information managed by the SUBSEQUENT SYSTEM due to a successfully exploited vulnerability. Confidentiality refers to limiting information access and disclosure to only authorized users, as well as preventing access by, or disclosure to, unauthorized ones.
Integrity: This metric measures the impact to integrity of a successfully exploited vulnerability. Integrity refers to the trustworthiness and veracity of information. Integrity of the SUBSEQUENT SYSTEM is impacted when an attacker makes unauthorized modification of system data. Integrity is also impacted when a system user can repudiate critical actions taken in the context of the system (e.g. due to insufficient logging).
Availability: This metric measures the impact to the availability of the SUBSEQUENT SYSTEM resulting from a successfully exploited vulnerability. While the Confidentiality and Integrity impact metrics apply to the loss of confidentiality or integrity of data (e.g., information, files) used by the system, this metric refers to the loss of availability of the impacted system itself, such as a networked service (e.g., web, database, email). Since availability refers to the accessibility of information resources, attacks that consume network bandwidth, processor cycles, or disk space all impact the availability of a system.
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

EPSS score

Exploit Prediction Scoring System (EPSS)

This score estimates the probability of this vulnerability being exploited within the next 30 days. Data provided by FIRST.
(35th percentile)

Weaknesses

Improperly Controlled Modification of Dynamically-Determined Object Attributes

The product receives input from an upstream component that specifies multiple attributes, properties, or fields that are to be initialized or updated in an object, but it does not properly control which attributes can be modified. Learn more on MITRE.

CVE ID

CVE-2026-61598

GHSA ID

GHSA-cc7c-9jff-58wj

Source code

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