  This paper proposes LATTE, the first static binary taint analysis that is
powered by a large language model (LLM). LATTE is superior to the state of the
art (e.g., Emtaint, Arbiter, Karonte) in three aspects. First, LATTE is fully
automated while prior static binary taint analyzers need rely on human
expertise to manually customize taint propagation rules and vulnerability
inspection rules. Second, LATTE is significantly effective in vulnerability
detection, demonstrated by our comprehensive evaluations. For example, LATTE
has found 37 new bugs in real-world firmware which the baselines failed to
find, and 7 of them have been assigned CVE numbers. Lastly, LATTE incurs
remarkably low engineering cost, making it a cost-efficient and scalable
solution for security researchers and practitioners. We strongly believe that
LATTE opens up a new direction to harness the recent advance in LLMs to improve
vulnerability analysis for binary programs.
