CVE-2026-70472

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, Flowise openai-assistants-vector-store endpoints accept a client-controlled credential parameter and load credentials by id without checking whether that credential belongs to the caller workspace. Route permissions assistants:* only check feature access. The controller passes req.query.credential straight to the service, and the service uses findOneBy({ id: credentialId }), decrypts the credential, and calls OpenAI APIs without a workspaceId check. If an attacker knows another workspace credentialId, the attacker can use that workspace OpenAI key, read, modify, or delete victim vector stores and files, cause billing impact on the victim OpenAI account, and violate multi-tenant boundaries. This issue is fixed in version 3.1.3.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, Flowise openai-assistants-vector-store endpoints accept a client-controlled credential parameter and load credentials by id without checking whether that credential belongs to the caller workspace. Route permissions assistants:* only check feature access. The controller passes req.query.credential straight to the service, and the service uses findOneBy({ id: credentialId }), decrypts the credential, and calls OpenAI APIs without a workspaceId check. If an attacker knows another workspace credentialId, the attacker can use that workspace OpenAI key, read, modify, or delete victim vector stores and files, cause billing impact on the victim OpenAI account, and violate multi-tenant boundaries. This issue is fixed in version 3.1.3.

Summary

These endpoints accept a client-controlled credential parameter. The server loads credentials by id and uses them directly, without checking whether that credential belongs to the caller’s workspace. If an attacker knows another workspace’s credentialId, they can use that workspace’s OpenAI key.

Details

Route permissions (assistants:*) only check feature access. They do not check credential ownership. The controller passes req.query.credential straight to the service. The service does findOneBy({ id: credentialId }), decrypts the credential, and calls OpenAI APIs. There is no workspaceId check in this flow, so this is an IDOR.

Impact

• Cross-workspace unauthorized use of stored OpenAI keys.
• Unauthorized read/modify/delete of victim vector stores and files.
• Direct billing impact on victim OpenAI account.
• Multi-tenant boundary violation with practical exploitability.

Reproduction steps

1. Set up two workspaces: A (attacker) and B (victim), each with an OpenAI credential.
2. Log in as a user in workspace A (with assistants-related permissions).
3. Call /api/v1/openai-assistants-vector-store and set credential to B’s credential ID.
4. Example: GET /api/v1/openai-assistants-vector-store?credential=<B_credentialId>.
5. If responses/actions are executed using B’s credential context, the issue is confirmed.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, Flowise openai-assistants-vector-store endpoints accept a client-controlled credential parameter and load credentials by id without checking whether that credential belongs to the caller workspace. Route permissions assistants:* only check feature access. The controller passes req.query.credential straight to the service, and the service uses findOneBy({ id: credentialId }), decrypts the credential, and calls OpenAI APIs without a workspaceId check. If an attacker knows another workspace credentialId, the attacker can use that workspace OpenAI key, read, modify, or delete victim vector stores and files, cause billing impact on the victim OpenAI account, and violate multi-tenant boundaries. This issue is fixed in version 3.1.3.

Flowise is a drag & drop user interface to build a customized large language model flow. Prior to 3.1.3, Flowise openai-assistants-vector-store endpoints accept a client-controlled credential parameter and load credentials by id without checking whether that credential belongs to the caller workspace. Route permissions assistants:* only check feature access. The controller passes req.query.credential straight to the service, and the service uses findOneBy({ id: credentialId }), decrypts the credential, and calls OpenAI APIs without a workspaceId check. If an attacker knows another workspace credentialId, the attacker can use that workspace OpenAI key, read, modify, or delete victim vector stores and files, cause billing impact on the victim OpenAI account, and violate multi-tenant boundaries. This issue is fixed in version 3.1.3.

Summary

These endpoints accept a client-controlled credential parameter. The server loads credentials by id and uses them directly, without checking whether that credential belongs to the caller’s workspace. If an attacker knows another workspace’s credentialId, they can use that workspace’s OpenAI key.

Details

Route permissions (assistants:*) only check feature access. They do not check credential ownership. The controller passes req.query.credential straight to the service. The service does findOneBy({ id: credentialId }), decrypts the credential, and calls OpenAI APIs. There is no workspaceId check in this flow, so this is an IDOR.

Impact

• Cross-workspace unauthorized use of stored OpenAI keys.
• Unauthorized read/modify/delete of victim vector stores and files.
• Direct billing impact on victim OpenAI account.
• Multi-tenant boundary violation with practical exploitability.

Reproduction steps

1. Set up two workspaces: A (attacker) and B (victim), each with an OpenAI credential.
2. Log in as a user in workspace A (with assistants-related permissions).
3. Call /api/v1/openai-assistants-vector-store and set credential to B’s credential ID.
4. Example: GET /api/v1/openai-assistants-vector-store?credential=<B_credentialId>.
5. If responses/actions are executed using B’s credential context, the issue is confirmed.