Python-Multipart is a streaming multipart parser for Python. Prior to 0.0.31, parse_form() did not validate the Content-Length header before using it to bound its chunked read of the request body. A negative Content-Length turned the bounded read into a read-until-EOF, so the entire body was loaded into memory in a single read instead of in fixed-size chunks. This vulnerability is fixed in 0.0.31.
Weakness
The product receives input that is expected to specify a quantity (such as size or length), but it does not validate or incorrectly validates that the quantity has the required properties.
Affected Software
| Name | Vendor | Start Version | End Version |
|---|---|---|---|
| Python-multipart | Fastapiexpert | * | 0.0.31 (excluding) |
| Python-multipart | Ubuntu | questing | * |
Extended Description
Specified quantities include size, length, frequency, price, rate, number of operations, time, and others. Code may rely on specified quantities to allocate resources, perform calculations, control iteration, etc.
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.