CVE-2026-33530
Inventree Project Inventree ≤ 1.2.6
Raw vector
CVSS:3.1/AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:NSummary
CVE-2026-33530 is a high-severity Exposure of Sensitive Information Through Data Queries (CWE-202) vulnerability in Inventree Project Inventree. Its CVSS base score is 7.7 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Data from Information Repositories (T1213); ranked at the 11th percentile by exploit likelihood (below the median); it is not currently listed in the CISA KEV catalog.
The strongest mitigations our analysis identified map to AC-23 (Data Mining Protection) — see the control section below for these in your framework.
Deeper analysis AI-assisted summary
Synthesised by an AI model from the NVD description and linked references — a reading aid, not an authoritative source.
CVE-2026-33530 affects InvenTree, an open source inventory management system, in versions prior to 1.2.6. The vulnerability arises in certain API endpoints for bulk data operations, such as `/api/part/`, `/api/stock/`, `/api/order/so/allocation/`, and others. These endpoints accept a `filters` parameter that is passed directly to Django's ORM `queryset.filter(**filters)` without field allowlisting, enabling traversal of model relationships via Django's `__` lookup syntax for blind boolean-based data extraction from the database. It is rated 7.7 on the CVSS 3.1 scale (AV:N/AC:L/PR:L/UI:N/S:C/C:H/I:N/A:N) and maps to CWE-202 (Observable Discrepancy).
Any authenticated user with low privileges can exploit this over the network with low complexity and no user interaction. By crafting malicious `filters` parameters, attackers can hijack bulk operation endpoints to exfiltrate sensitive database information across model relationships, achieving high confidentiality impact in a changed scope without affecting integrity or availability.
The issue is patched in InvenTree versions 1.2.6 and 1.3.0 or above; users should update to these versions. No known workarounds are available. Details are provided in the GitHub security advisory (GHSA-m8j2-vfmq-p6qg) and the patching pull request (#11581).
EU & UK References
- 🇪🇺 ENISA EUVD: EUVD-2026-16359
Vulnerability Data
InvenTree is an Open Source Inventory Management System. Prior to version 1.2.6, certain API endpoints associated with bulk data operations can be hijacked to exfiltrate sensitive information from the database. The bulk operation API endpoints (e.g. `/api/part/`, `/api/stock/`, `/api/order/so/allocation/`, and…
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others) accept a filters parameter that is passed directly to Django's ORM queryset.filter(**filters) without any field allowlisting. This enables any authenticated user to traverse model relationships using Django's __ lookup syntax and perform blind boolean-based data extraction. This issue is patched in version 1.2.6, and 1.3.0 (or above). Users should update to the patched versions. No known workarounds are available.
- CWE(s)
Related Threats
MITRE ATT&CK Enterprise Techniques
CVEs Like This One
Affected Assets
Mitigating Controls
Mitigating Controls (NIST 800-53 r5) AI
AC-23 directly requires mechanisms to protect against unauthorized data mining and inference from statistical queries that would expose sensitive information.
Mitigating Controls (NIST CSF 2.0) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→CSF cross-walk (authority under review) — links open the control.
Least-privilege query permissions directly limit the data an attacker can request or infer.
Behavior analytics on query activity can detect inference attempts but does not prevent exposure at query time.
Protecting data-in-use reduces what remains available for inference via queries.
Mitigating Controls (ISO/IEC 27001:2022 Annex A) AI
Derived directly from the weakness types (CWEs) cited in the NVD entry via our AI-authored CWE→ISO cross-walk (authority under review) — links open the control.
Access control limits who can run queries that could expose sensitive information via inference.
Granular access rights reduce the ability of users to craft inference queries.
Data masking prevents inference by obscuring sensitive values returned in query results.
Information access restriction directly limits query scope that could lead to inference.
Classification helps identify sensitive data that must be protected from inference attacks.
DLP can detect and block queries or result sets that risk exposing sensitive information.