Flowise: Mass Assignment in Chatflow Update Endpoint Allows Cross-Workspace AgentFlow Reassignment
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, a mass assignment vulnerability exists in the chatflow update endpoint of FlowiseAI. The endpoint allows clients to modify server-controlled properties such as deployed, isPublic, workspaceId, createdDate, and updatedDate when updating a chatflow object. Due to missing server-side validation and authorization checks, an authenticated user can manipulate internal attributes of a chatflow and reassign it to another workspace. This allows cross-workspace resource reassignment and unauthorized modification of deployment and visibility
Flowise: Mass Assignment in Tool Update Endpoint Allows Cross-Workspace Resource Reassignment
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, a mass assignment vulnerability exists in the tool update endpoint of FlowiseAI. The endpoint allows authenticated users to modify server-controlled properties such as workspaceId, createdDate, and updatedDate when updating a tool resource. Due to missing server-side validation and authorization checks, an attacker can manipulate the workspaceId field and reassign tools to arbitrary workspaces. This breaks tenant isolation in multi-workspace environments. This issue has been patched in version 3.1.2.
Flowise: Mass Assignment in Variable Update Endpoint Allows Cross-Workspace Resource Reassignment
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, a mass assignment vulnerability exists in the variable update endpoint of FlowiseAI. The endpoint allows authenticated users to modify server-controlled properties such as workspaceId, createdDate, and updatedDate when updating a variable resource. Due to missing server-side validation and authorization checks, an attacker can manipulate the workspaceId field and reassign variables to arbitrary workspaces. This behavior may break tenant isolation in multi-workspace environments. This issue has been patched in version 3.1.2.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, all CRUD endpoints for OpenAI Assistants Vector Store have no authentication middleware and the route path /api/v1/openai-assistants-vector-store is not in WHITELIST_URLS. However, it is also not protected by the main auth middleware when accessed via API key — the route requires API key auth (not whitelisted), but no permission checks exist on any operation. This issue has been patched in version 3.1.2.
Flowise: SSRF Protection Bypass via Direct node-fetch / axios Usage (Patch Enforcement Failure)
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, multiple tool implementations directly import and invoke raw HTTP clients (node-fetch, axios) instead of using the secured wrapper. These tools include (1) OpenAPIToolkit/OpenAPIToolkit.ts, (2) WebScraperTool/WebScraperTool.ts, (3) MCP/core.ts, and (4) Arxiv/core.ts. This vulnerability is fixed in 3.1.0.
FlowiseAI Flowise API Response account.service.ts login information disclosure
A security flaw has been discovered in FlowiseAI Flowise up to 3.0.12. Affected is the function Login of the file packages/server/src/enterprise/services/account.service.ts of the component API Response Handler. The manipulation results in information disclosure. The attack can be launched remotely. A high complexity level is associated with this attack. The exploitability is told to be difficult. You should upgrade the affected component.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the GraphCypherQAChain node forwards user-provided input directly into the Cypher query execution pipeline without proper sanitization. An attacker can inject arbitrary Cypher commands that are executed on the underlying Neo4j database, enabling data exfiltration, modification, or deletion. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the CSV_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. An attacker can leverage this vulnerability to execute code in the context of the user running the server. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the CSV Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the Flowise server. Th
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the specific flaw exists within the run method of the Airtable_Agents class. The issue results from the lack of proper sandboxing when evaluating an LLM generated python script. Using prompt injection techniques, an unauthenticated attacker with the ability to send prompts to a chatflow using the Airtable Agent node may convince an LLM to respond with a malicious python script that executes attacker controlled commands on the flowise server. This vulnerability is fixed in 3.1.0.
Flowise: Unauthenticated TTS endpoint accepts arbitrary credential IDs — enables API credit abuse via stored credentials
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the text-to-speech generation endpoint (POST /api/v1/text-to-speech/generate) is whitelisted (no auth) and accepts a credentialId directly in the request body. When called without a chatflowId, the endpoint uses the provided credentialId to decrypt the stored credential (e.g., OpenAI or ElevenLabs API key) and generate speech. This vulnerability is fixed in 3.1.0.
Flowise: Public chatflow endpoints return unsanitized flowData including plaintext API keys, passwords, and credential IDs
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the GET /api/v1/public-chatflows/:id endpoint returns the full chatflow object without sanitization for public chatflows. Docker validation revealed this is worse than initially assessed: the sanitizeFlowDataForPublicEndpoint function does NOT exist in the released v3.0.13 Docker image. Both public-chatflows AND public-chatbotConfig return completely raw flowData including credential IDs, plaintext API keys, and password-type fields. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, this vulnerability allows remote attackers to bypass authentication on affected installations of FlowiseAI Flowise. Authentication is not required to exploit this vulnerability. The specific flaw exists within the resetPassword method of the AccountService class. There is no check performed to ensure that a password reset token has actually been generated for a user account. By default the value of the reset token stored in a users account is null, or an empty string if they've reset their password before. An attacker with knowledge of the
Flowise: Mass Assignment in DocumentStore Create Endpoint Leads to Cross-Workspace Object Takeover (IDOR)
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 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 attac
Flowise: Password Reset Link Sent Over Unsecured HTTP
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the password reset functionality on cloud.flowiseai.com sends a reset password link over the unsecured HTTP protocol instead of HTTPS. This behavior introduces the risk of a man-in-the-middle (MITM) attack, where an attacker on the same network as the user (e.g., public Wi-Fi) can intercept the reset link and gain unauthorized access to the victim’s account. This vulnerability is fixed in 3.1.0.
Flowise: Unauthenticated OAuth 2.0 Access Token Disclosure via Public Chatflow
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, Flowise contains an authentication bypass vulnerability that allows an unauthenticated attacker to obtain OAuth 2.0 access tokens associated with a public chatflow. By accessing a public chatflow configuration endpoint, an attacker can retrieve internal workflow data, including OAuth credential identifiers, which can then be used to refresh and obtain valid OAuth 2.0 access tokens without authentication. This vulnerability is fixed in 3.1.0.
Flowise: APIChain Prompt Injection SSRF in GET/POST API Chains
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, a Server-Side Request Forgery (SSRF) vulnerability exists in FlowiseAI's POST/GET API Chain components that allows unauthenticated attackers to force the server to make arbitrary HTTP requests to internal and external systems. By injecting malicious prompt templates, attackers can bypass the intended API documentation constraints and redirect requests to sensitive internal services, potentially leading to internal network reconnaissance and data exfiltration. This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the core security wrappers (secureAxiosRequest and secureFetch) intended to prevent Server-Side Request Forgery (SSRF) contain multiple logic flaws. These flaws allow attackers to bypass the allow/deny lists via DNS Rebinding (Time-of-Check Time-of-Use) or by exploiting the default configuration which fails to enforce any deny list. This vulnerability is fixed in 3.1.0.
Flowise: SSRF Protection Bypass via Unprotected Built-in HTTP Modules in Custom Function Sandbox
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, a Server-Side Request Forgery (SSRF) protection bypass vulnerability exists in the Custom Function feature. While the application implements SSRF protection via HTTP_DENY_LIST for axios and node-fetch libraries, the built-in Node.js http, https, and net modules are allowed in the NodeVM sandbox without equivalent protection. This allows authenticated users to bypass SSRF controls and access internal network resources (e.g., cloud provider metadata services) This vulnerability is fixed in 3.1.0.
Flowise: File Upload Validation Bypass in createAttachment
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, the Chatflow configuration file upload settings can be modified to allow the application/javascript MIME type. This lets an attacker upload .js files even though the frontend doesn’t normally allow JavaScript uploads. This enables attackers to persistently store malicious Node.js web shells on the server, potentially leading to Remote Code Execution (RCE). This vulnerability is fixed in 3.1.0.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.0, Flowise is vulnerable to a critical unauthenticated remote command execution (RCE) vulnerability. It can be exploited via a parameter override bypass using the FILE-STORAGE:: keyword combined with a NODE_OPTIONS environment variable injection. This allows for the execution of arbitrary system commands with root privileges within the containerized Flowise instance, requiring only a single HTTP request and no authentication or knowledge of the instance. This vulnerability is fixed in 3.1.0.