CVE-2026-41277
Published: 23 April 2026
Summary
CVE-2026-41277 is a high-severity Improper Access Control (CWE-284) vulnerability in Flowiseai Flowise. Its CVSS base score is 8.8 (High).
Operationally, exploitation aligns with the MITRE ATT&CK technique Exploit Public-Facing Application (T1190); ranked in the top 48.7% of CVEs by exploit likelihood; it is not currently listed in the CISA KEV catalog; a public proof-of-concept is referenced.
This vulnerability is AI-related — categorised as Other AI Platforms.
The strongest mitigations our analysis identified are NIST 800-53 AC-3 (Access Enforcement) and AC-6 (Least Privilege).
Threat & Defense at a Glance
Threat & Defense Details
Mitigating Controls (NIST 800-53 r5)AI
Validates client-supplied inputs at system entry points to prevent mass assignment exploitation where users control unauthorized fields like primary keys and internal states.
Enforces approved authorizations to block unauthorized overwrites of DocumentStore objects across workspaces, mitigating IDOR and cross-tenant takeover.
Applies least privilege to restrict low-privileged authenticated users from performing high-impact actions like UPSERT operations on other tenants' objects.
MITRE ATT&CK Enterprise TechniquesAI
Why these techniques?
The vulnerability is a remotely exploitable web application flaw (AV:N/PR:L) enabling authenticated low-privilege users to perform unauthorized overwrites of DocumentStore objects, directly mapping to exploitation of a public-facing application.
NVD Description
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, a Mass Assignment vulnerability in the DocumentStore creation endpoint allows authenticated users to control the primary key (id) and internal state…
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fields of DocumentStore entities. Because the service uses repository.save() with a client-supplied primary key, the POST create endpoint behaves as an implicit UPSERT operation. This enables overwriting existing DocumentStore objects. In multi-workspace or multi-tenant deployments, this can lead to cross-workspace object takeover and broken object-level authorization (IDOR), allowing an attacker to reassign or modify DocumentStore objects belonging to other workspaces. This vulnerability is fixed in 3.1.0.
Deeper analysisAI
CVE-2026-41277 is a mass assignment vulnerability in Flowise, an open-source drag-and-drop user interface for building customized large language model (LLM) flows. Affecting versions prior to 3.1.0, the issue resides in the DocumentStore creation endpoint, where authenticated users can manipulate the primary key (id) and internal state fields of DocumentStore entities. The backend's use of repository.save() with client-supplied primary keys turns the POST create endpoint into an implicit UPSERT operation, enabling attackers to overwrite existing DocumentStore objects. Associated CWEs include CWE-284 (Improper Access Control), CWE-639 (Authorization Bypass Through User-Controlled Key), and CWE-915 (Improperly Controlled Modification of Dynamically-Determined Object Attributes), with a CVSS v3.1 base score of 8.8 (AV:N/AC:L/PR:L/UI:N/S:U/C:H/I:H/A:H).
Authenticated users with low privileges can exploit this vulnerability remotely over the network with no user interaction required. In multi-workspace or multi-tenant Flowise deployments, attackers can achieve cross-workspace object takeover by overwriting DocumentStore entities owned by other workspaces, bypassing object-level authorization (IDOR). This grants high confidentiality, integrity, and availability impacts, such as reassigning or modifying sensitive DocumentStore objects, potentially disrupting LLM flows or exposing data across tenants.
The official GitHub security advisory (GHSA-3prp-9gf7-4rxx) confirms the vulnerability is fixed in Flowise version 3.1.0, recommending immediate upgrades to mitigate risks. No additional workarounds are specified in the provided references.
Flowise's focus on LLM orchestration introduces AI/ML relevance, as compromised DocumentStores could tamper with model flows, embeddings, or retrieval-augmented generation components in production environments. No public evidence of real-world exploitation is available as of the CVE publication on 2026-04-23.
Details
- CWE(s)
Affected Products
AI Security AnalysisAI
- AI Category
- Other AI Platforms
- Risk Domain
- N/A
- OWASP Top 10 for LLMs 2025
- None mapped
- Classification Reason
- Matched keywords: large language model