Open WebUI is a self-hosted artificial intelligence platform designed to operate entirely offline. Versions 0.6.224 and prior contain a code injection vulnerability in the Direct Connections feature that allows malicious external model servers to execute arbitrary JavaScript in victim browsers via Server-Sent Event (SSE) execute events. This leads to authentication token theft, complete account takeover, and when chained with the Functions API, enables remote code execution on the backend server. The attack requires the victim to enable Direct Connections (disabled by default) and add the attackers malicious model URL, achievable through social engineering of the admin and subsequent users. This issue is fixed in version 0.6.35.
Weakness
The product receives input from an upstream component, but it does not neutralize or incorrectly neutralizes code syntax before using the input in a dynamic evaluation call (e.g. “eval”).
Affected Software
| Name |
Vendor |
Start Version |
End Version |
| Open_webui |
Openwebui |
* |
0.6.35 (excluding) |
Potential Mitigations
- Assume all input is malicious. Use an “accept known good” input validation strategy, i.e., use a list of acceptable inputs that strictly conform to specifications. Reject any input that does not strictly conform to specifications, or transform it into something that does.
- When performing input validation, consider all potentially relevant properties, including length, type of input, the full range of acceptable values, missing or extra inputs, syntax, consistency across related fields, and conformance to business rules. As an example of business rule logic, “boat” may be syntactically valid because it only contains alphanumeric characters, but it is not valid if the input is only expected to contain colors such as “red” or “blue.”
- Do not rely exclusively on looking for malicious or malformed inputs. This is likely to miss at least one undesirable input, especially if the code’s environment changes. This can give attackers enough room to bypass the intended validation. However, denylists can be useful for detecting potential attacks or determining which inputs are so malformed that they should be rejected outright.
- Inputs should be decoded and canonicalized to the application’s current internal representation before being validated (CWE-180, CWE-181). Make sure that your application does not inadvertently decode the same input twice (CWE-174). Such errors could be used to bypass allowlist schemes by introducing dangerous inputs after they have been checked. Use libraries such as the OWASP ESAPI Canonicalization control.
- Consider performing repeated canonicalization until your input does not change any more. This will avoid double-decoding and similar scenarios, but it might inadvertently modify inputs that are allowed to contain properly-encoded dangerous content.
- For Python programs, it is frequently encouraged to use the ast.literal_eval() function instead of eval, since it is intentionally designed to avoid executing code. However, an adversary could still cause excessive memory or stack consumption via deeply nested structures [REF-1372], so the python documentation discourages use of ast.literal_eval() on untrusted data [REF-1373].
References