Using an LLM to fuzz business logic bugs
Fuzzing has usually been linked to C and C++, where it often looks for crashes and memory errors. In Python, the same style of testing can feel less useful because many failures only become ordinary exceptions.
An LLM can shift the focus from simply running many code paths to probing whether can be broken. This makes it possible to look for , not just crashes.
At least one project has already shown that this approach can work. The result depends heavily on how well the harness is built for the specific system being tested.
Key points
- Traditional fuzzing often targets crashes and memory errors in C and C++.
- In Python, fuzzing may only trigger normal exceptions, which can make it feel less valuable.
- An LLM can help test whether can be broken.
- The main target is , not just program crashes.
- The quality of the harness is the key practical factor.