  Security critical software, e.g., OpenSSL, comes with numerous side-channel
leakages left unpatched due to a lack of resources or experts. The situation
will only worsen as the pace of code development accelerates, with developers
relying on Large Language Models (LLMs) to automatically generate code. In this
work, we explore the use of LLMs in generating patches for vulnerable code with
microarchitectural side-channel leakages. For this, we investigate the
generative abilities of powerful LLMs by carefully crafting prompts following a
zero-shot learning approach. All generated code is dynamically analyzed by
leakage detection tools, which are capable of pinpointing information leakage
at the instruction level leaked either from secret dependent accesses or
branches or vulnerable Spectre gadgets, respectively. Carefully crafted prompts
are used to generate candidate replacements for vulnerable code, which are then
analyzed for correctness and for leakage resilience. From a cost/performance
perspective, the GPT4-based configuration costs in API calls a mere few cents
per vulnerability fixed. Our results show that LLM-based patching is far more
cost-effective and thus provides a scalable solution. Finally, the framework we
propose will improve in time, especially as vulnerability detection tools and
LLMs mature.
