Fuzzing is a widely adopted and pragmatic methodology for bug hunting as a means of software hardening. Research reveals that increasing fuzzing throughput directly increases bug discovery rate. The highest performance fuzzing strategy is persistent fuzzing, which reuses a single process for all test cases by looping back to the start upon completion, instead of exiting. This eliminates all process creation, initialization, and tear-down costs---which are on-par with execution cost. Unfortunately, persistent fuzzing leads to semantically inconsistent program states because process state changes from one test case remain for subsequent test cases. This semantic inconsistency results in missed crashes, false crashes, and overall incorrectness that undermines fuzzer effectiveness. We observe that existing fuzzing execution mechanisms exist on a continuum, based on the amount of state that gets discarded and restored between test cases. We present ClosureX, a fuzzing execution mechanism that sits at a new spot on this state restoration continuum, where only test-case-execution-specific state is reset. This fine-grain state restoration provides near-persistent performance with the correctness of heavyweight state restoration. We construct ClosureX as a set of LLVM passes that integrate with AFL++. Our evaluation on ten popular open-source fuzzing targets show that ClosureX maintains semantic correctness, while increasing test case execution rate by over 3.5x, on average, compared to AFL++. ClosureX also finds bugs more consistently and 1.9x faster than AFL++, with ClosureX discovering 15 0-day bugs (4 CVEs).

@inproceedings{RanjanPH25,
  title = {<scp>ClosureX:</scp>
            Compiler Support for Correct Persistent Fuzzing},
  author = {Rishi Ranjan and Ian Paterson and Matthew Hicks},
  booktitle = {ASPLOS},
  year = {2025},
  doi = {10.1145/3669940.3707281}
}