Flowise before 3.1.0 (npm package flowise, versions 3.0.13 and earlier) uses a weak hardcoded default value 'Secre$t' for the TOKEN_HASH_SECRET environment variable in packages/server/src/enterprise/utils/tempTokenUtils.ts when the variable is not configured. This secret derives the AES-256-CBC key used to encrypt user IDs and workspace IDs in the 'meta' field of JWT tokens. An attacker who knows the default secret can decrypt this metadata to extract internal user and workspace identifiers, and re-encrypt manipulated values such as altered user or workspace IDs. Because the JWT signature is validated separately, decrypting or tampering with this metadata does not by itself grant access, but the disclosure of internal identifiers and possible metadata manipulation could aid privilege escalation or unauthorized data access.
Flowise before 3.1.0 (versions 3.0.13 and earlier) contains a missing authentication vulnerability in the /api/v1/loginmethod endpoint that allows unauthenticated users to retrieve an organization's complete SSO configuration, including OAuth client secrets in cleartext, by providing an organizationId parameter. Remote attackers can send a GET request to harvest sensitive API credentials for Google, Microsoft/Azure, GitHub, and Auth0 integrations. This affects FlowiseAI Cloud and self-hosted instances where the endpoint is exposed.
Flowise before 3.0.13 uses bcrypt with default salt rounds of 5, providing only 32 iterations instead of the OWASP-recommended minimum of 10 rounds. Attackers can crack password hashes approximately 30 times faster with modern GPU hardware, potentially compromising all user accounts in a database breach scenario.
Flowise before 3.1.2 contains multiple OS command injection vulnerabilities in the Custom MCP Server feature due to incomplete command-flag validation and a regex bypass in local file access restrictions. An attacker with a Flowise account of any role, or API access with view/update permissions for chatflows, can configure a malicious MCP server to bypass the validateCommandFlags blocklist (for example, 'docker build' is not blocked, and 'npx --yes' is not blocked while only '-y' is) and the validateArgsForLocalFileAccess checks, resulting in execution of arbitrary commands on the Flowise host.
Flowise before 3.1.0 contains a server-side request forgery vulnerability in the Execute Flow node that allows attackers to bypass security validation by providing intranet addresses through the base URL field. Attackers can initiate HTTP requests to internal network addresses, access cloud metadata, and enumerate internal services by exploiting the missing secureFetch verification in httpSecurity.ts.
Flowise before 3.1.2 contains an information disclosure vulnerability in the /api/v1/chatflows/apikey/:apikey endpoint. When the keyonly query parameter is omitted (the default), the endpoint returns not only the chatflows bound to the supplied API key but also all chatflows across every workspace that have no API key assigned, because the underlying query lacks any workspace filter. An attacker with a valid API key for one workspace can therefore retrieve the full ChatFlow configuration (including flowData with system prompts and node configurations, chatbotConfig, apiConfig, and credential IDs) of unprotected chatflows belonging to other workspaces.
Flowise is a drag & drop user interface to build a customized large language model flow. Prior to version 3.1.2, dataset create and update mass-assignment allows cross-workspace dataset takeover. 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, DatasetRow create and update mass-assignment allows cross-workspace row takeover. 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, evaluation create and update mass-assignment allows cross-workspace evaluation takeover. 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, evaluator create and update mass-assignment allows cross-workspace evaluator takeover. This issue has been patched in version 3.1.2.