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Flowise: Mass Assignment in DocumentStore Create Endpoint Leads to Cross-Workspace Object Takeover (IDOR)

High severity GitHub Reviewed Published Apr 15, 2026 in FlowiseAI/Flowise • Updated Apr 24, 2026

Package

npm flowise (npm)

Affected versions

<= 3.0.13

Patched versions

3.1.0

Description

Summary

A Mass Assignment vulnerability in the DocumentStore creation endpoint allows authenticated users to control the primary key (id) and internal state 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.

Details

The DocumentStore entity defines a globally unique primary key:

@PrimaryGeneratedColumn('uuid')
id: string

The create logic is implemented as:

const documentStore = repo.create(newDocumentStore)
const dbResponse = await repo.save(documentStore)

Here is no DTO allowlist or field filtering before persistence. The entire request body is mapped directly to the entity.
TypeORM save() behavior:

  1. If the primary key (id) exists → UPDATE
  2. If not → INSERT

Because id is accepted from the client, the create endpoint effectively functions as an UPSERT endpoint.

This allows an authenticated user to submit:

{
  "id": "<existing_store_id>",
  "name": "modified",
  "description": "modified",
  "status": "SYNC",
  "embeddingConfig": "...",
  "vectorStoreConfig": "...",
  "recordManagerConfig": "..."
}

If a DocumentStore with the supplied id already exists, save() performs an UPDATE rather than creating a new record.

Importantly:

The primary key is globally unique (uuid)
It is not composite with workspaceId
The create path does not enforce ownership validation before calling save()
This introduces a broken object-level authorization risk.

If an attacker can obtain or enumerate a valid DocumentStore UUID belonging to another workspace, they can:
Submit a POST create request with that UUID.
Trigger an UPDATE on the existing record.
Potentially overwrite fields including workspaceId, effectively reassigning the object to their own workspace.

Because the service layer does not verify that the existing record belongs to the caller’s workspace before updating, this may result in cross-workspace object takeover.

Additionally, several service functions retrieve DocumentStore entities by id without consistently scoping by workspaceId, increasing the risk of IDOR if controller-level protections are bypassed or misconfigured.

PoC

  1. Create a normal DocumentStore in Workspace A.
  2. Capture its id from the API response.
  3. From Workspace B (or another authenticated context), submit:
POST /api/v1/document-store
Content-Type: application/json

{
  "id": "<id_from_workspace_A>",
  "name": "hijacked",
  "description": "hijacked"
}

Because the service uses repository.save() with a client-supplied primary key:

  • The existing record is updated.
  • The object may become reassigned depending on how workspaceId is handled at controller level.
  • If workspaceId is overwritten during the create flow, the store is effectively migrated to the attacker’s workspace.
  • This demonstrates object takeover via UPSERT semantics on a create endpoint.

Impact

This vulnerability enables:

  • Mass Assignment on server-managed fields
  • Overwrite of existing objects via implicit UPSERT behavior
  • Broken Object Level Authorization (BOLA)
  • Potential cross-workspace object takeover in multi-tenant deployments
  • In a SaaS or shared-workspace environment, an attacker who can obtain or guess a valid UUID may modify or reassign DocumentStore objects belonging to other tenants.

Because DocumentStore objects control embedding providers, vector store configuration, and record management logic, successful takeover can affect data indexing, retrieval, and AI workflow execution.

This represents a high-risk authorization flaw in multi-tenant environments.

References

@igor-magun-wd igor-magun-wd published to FlowiseAI/Flowise Apr 15, 2026
Published to the GitHub Advisory Database Apr 17, 2026
Reviewed Apr 17, 2026
Published by the National Vulnerability Database Apr 23, 2026
Last updated Apr 24, 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 Present
Privileges Required Low
User interaction None
Vulnerable System Impact Metrics
Confidentiality High
Integrity High
Availability Low
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:P/PR:L/UI:N/VC:H/VI:H/VA:L/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.
(15th percentile)

Weaknesses

Improper Access Control

The product does not restrict or incorrectly restricts access to a resource from an unauthorized actor. Learn more on MITRE.

Authorization Bypass Through User-Controlled Key

The system's authorization functionality does not prevent one user from gaining access to another user's data or record by modifying the key value identifying the data. Learn more on MITRE.

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-2026-41277

GHSA ID

GHSA-3prp-9gf7-4rxx

Source code

Credits

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