CVE-2026-61595 in djust
Summary
by MITRE • 09/16/2026
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.
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Analysis
by VulDB Data Team • 09/16/2026
The vulnerability identified in djust versions prior to 1.0.7 represents a critical multi-tenant data isolation failure rooted in improper context management within asynchronous execution environments. The core issue stems from the reliance on Python's threading.local storage mechanism for maintaining tenant identity across request boundaries. While this approach functioned adequately for traditional synchronous HTTP requests handled by Django’s standard middleware stack, it proved fundamentally incompatible with the concurrency model required for live features such as WebSockets and Server-Sent Events SSE. In these asynchronous contexts, the execution threads are often reused or shared via executors like sync_to_async, causing the threading.local variable to be inadvertently shared across multiple distinct user connections rather than remaining isolated per logical task. This architectural mismatch resulted in a complete breakdown of tenant isolation where the system failed to distinguish between different users operating within the same multi-tenant application instance.
The operational impact of this flaw is severe, leading directly to unauthorized data disclosure and cross-tenant information leakage. Because the current tenant context was not properly initialized or maintained during WebSocket or SSE mount events and subsequent event handlers, the function responsible for retrieving the active tenant consistently returned None. This null value propagated through the application’s query layer, specifically affecting the tenant-aware QuerySet manager. Under default configurations where strict mode is enabled, this failure caused the system to operate in an OPEN state rather than a CLOSED one. Consequently, database queries executed without any tenant-specific filtering clauses, effectively returning all rows from every tenant's tables to whoever held the active socket connection. This means that any authenticated user could potentially access sensitive data belonging to other tenants by simply maintaining a live connection and triggering specific events or views within the application.
From a security classification perspective, this vulnerability aligns with CWE-200 Information Exposure and more critically CWE-640 Weakness in Trust Establishment for Multi-Tenant Isolation. The failure to enforce strict data partitioning violates fundamental principles of secure software design regarding logical access controls. In terms of the MITRE ATT&CK framework, this flaw facilitates lateral movement within a multi-tenant environment by allowing an attacker to pivot from their own tenant context into others without authentication or authorization checks. It also relates to CWE-798 Use of Hard-coded Credentials if one considers the implicit trust placed in the threading.local variable as a hard-coded assumption about thread safety that does not hold under async concurrency models. The vulnerability exploits the gap between synchronous middleware expectations and asynchronous runtime behavior, creating an attack vector where standard authentication mechanisms are bypassed at the data access layer.
The resolution implemented in djust version 1.0.7 addresses these issues by migrating tenant storage from threading.local to contextvars.ContextVar. Context variables provide true isolation for each async task or coroutine, ensuring that tenant identity remains strictly bound to its specific execution context regardless of thread reuse. The fix also ensures that the resolved tenant is correctly bound around WebSocket and SSE mount events as well as every subsequent dispatch operation. Furthermore, the QuerySet manager logic was updated to scope the base queryset immediately upon initialization and enforce a CLOSED state by default under STRICT_MODE. This means that if no valid tenant context exists, queries will return an empty result set using .none() rather than exposing all data. Additionally, a new system check S006 has been introduced to warn developers when STRICT_MODE is disabled in production environments, reinforcing the defense-in-depth strategy against such isolation failures.
To mitigate this vulnerability and prevent similar issues in future developments, organizations must ensure they are running djust version 1.0.7 or later. For applications still on older versions, there are no reliable workarounds for the live path due to the fundamental nature of how threading.local behaves under async execution; therefore upgrading is mandatory. Developers should also audit their custom middleware and context management code to ensure compatibility with Python's contextvars module when dealing with asynchronous views or background tasks. It is crucial to verify that all data access layers respect tenant scoping even in edge cases such as real-time connections, scheduled jobs, and external API integrations. Regular security assessments focusing on multi-tenant isolation logic should be conducted to detect any deviations from expected behavior where context variables might leak across task boundaries due to improper wrapping or execution model mismatches.