The concept of proof of work, often used to describe cryptographic challenges, may not be relevant in the context of bug detection in large language models (LLMs). Unlike proof of work, which relies on computational power, bug detection involves the ability of a model to understand and identify vulnerabilities.
Research has shown that the effectiveness of LLMs in detecting bugs is limited by their intelligence level, rather than the number of computations performed. For example, the OpenBSD SACK bug was found using advanced models, but attempts to use weaker models resulted in hallucinations - incorrect diagnoses that lacked a deep understanding of the underlying issue.
This has significant implications for cybersecurity in the future. Rather than relying on sheer computational power or GPU capabilities, better models and faster access to these models will be key factors in identifying vulnerabilities.
