CVE-2024-10131

The add_llm function in llm_app.py in infiniflow/ragflow version 0.11.0 contains a remote code execution (RCE) vulnerability. The function uses user-supplied input req[llm_factory] and req[llm_name] to dynamically instantiate classes from various model dictionaries. This approach allows an attacker to potentially execute arbitrary code due to the lack of comprehensive input validation or sanitization. An attacker could provide a malicious value for llm_factory that, when used as an index to these model dictionaries, results in the execution of arbitrary code.

Weakness

The product constructs all or part of a command using externally-influenced input from an upstream component, but it does not neutralize or incorrectly neutralizes special elements that could modify the intended command when it is sent to a downstream component.

Affected Software

Extended Description

Command injection vulnerabilities typically occur when:

Many protocols and products have their own custom command language. While OS or shell command strings are frequently discovered and targeted, developers may not realize that these other command languages might also be vulnerable to attacks. Command injection is a common problem with wrapper programs.

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.

Related Attack Patterns

• https://cwe.mitre.org/data/definitions/136.html
• https://cwe.mitre.org/data/definitions/15.html
• https://cwe.mitre.org/data/definitions/183.html
• https://cwe.mitre.org/data/definitions/248.html
• https://cwe.mitre.org/data/definitions/40.html
• https://cwe.mitre.org/data/definitions/43.html
• https://cwe.mitre.org/data/definitions/75.html
• https://cwe.mitre.org/data/definitions/76.html

References

• https://huntr.com/bounties/42ae0b27-e851-4b58-a991-f691a437fbaa