Yu-Chuan Liang, Hsu-Chun Hsiao (National Taiwan University)

libFuzzer is a powerful fuzzer that has helped find thousands of bugs in real-world programs. However, fuzzers that seek to compare with libFuzzer and its variants face two significant limitations. First, they are restricted to use the time-to-first-crash metric rather than the code-coverage metric because libFuzzer will abort whenever the fuzzing target crashes. Second, even if libFuzzer in the ignore-crash mode can continue after finding a crash, it may produce wrong results for programs expecting a clean global context. Thus, fuzzers wishing to compare with libFuzzer are restricted to use carefully modified programs or programs without global-context dependency. To solve this context pollution problem and enhance comparability between libFuzzer and other fuzzers, we present a new libFuzzer mode called isolated-context mode (icLibFuzzer) that isolates the contexts of each fuzzer instance and fuzzing target, allowing to reinitialize the fuzzing target’s context after each execution efficiently. To implement icLibFuzzer, we modify libFuzzer’s in-process infrastructure into a lightweight forkserver infrastructure inspired by AFL’s design and propose structure packing, which speeds up the fuzzing speed by about 2x. We compare icLibFuzzer with four state-of-the-art fuzzers (AFL, Angora, QSYM, and Honggfuzz) using several real-world programs. The experiment result shows that icLibFuzzer outperforms these four fuzzers in most target programs after 24 hours of fuzzing and maintains the lead from 24 to 72 hours. To demonstrate that we can easily keep up with libFuzzer’s updates, we upgrade icLibFuzzer to using the latest libFuzzer (from LLVM9 to LLVM11) with no change to our code base. Our preliminary evaluation hints at icLibFuzzer-LLVM11’s promising improvement compared with icLibFuzzer-LLVM9 and AFL++, one of the latest fuzzers in the AFL family. We hope icLibFuzzer can serve as another baseline for fuzzing research. Our source code is available at GitHub.

View More Papers

Demo #4: Attacking Tesla Model X’s Autopilot Using Compromised...

Ben Nassi (Ben-Gurion University of the Negev), Yisroel Mirsky (Ben-Gurion University of the Negev, Georgia Tech), Dudi Nassi, Raz Ben Netanel (Ben-Gurion University of the Negev), Oleg Drokin (Independent Researcher), and Yuval Elovici (Ben-Gurion University of the Negev) Best Demo Award Winner ($300 cash prize)!

Read More

Доверя́й, но проверя́й: SFI safety for native-compiled Wasm

Evan Johnson (University of California San Diego), David Thien (University of California San Diego), Yousef Alhessi (University of California San Diego), Shravan Narayan (University Of California San Diego), Fraser Brown (Stanford University), Sorin Lerner (University of California San Diego), Tyler McMullen (Fastly Labs), Stefan Savage (University of California San Diego), Deian Stefan (University of California…

Read More

Rapid Vulnerability Mitigation with Security Workarounds

Zhen Huang (Pennsylvania State University), Gang Tan (Pennsylvania State University)

Read More

DOVE: A Data-Oblivious Virtual Environment

Hyun Bin Lee (University of Illinois at Urbana-Champaign), Tushar M. Jois (Johns Hopkins University), Christopher W. Fletcher (University of Illinois at Urbana-Champaign), Carl A. Gunter (University of Illinois at Urbana-Champaign)

Read More