Charles Averill, Ilan Buzzetti (The University of Texas at Dallas), Alex Bellon (UC San Diego), Kevin Hamlen (The University of Texas at Dallas)

LAPSE is a new framework for developing faulttolerant correctness proofs for near-arbitrary native code. It lifts binary code into an intermediate representation (IR) whose operational semantics admit hardware faults. LAPSE implements a machine-verified symbolic execution engine for the resulting IR within the Rocq automated theorem proving framework, creating a proof environment in which the space of possible executions includes all potential fault possibilities. To cope with the increase in proof space, automation tools succinctly describe and reason about the desired fault model. An implementation for 32-bit RISC-V semantics and evaluation on security-critical cryptographic subroutines from OpenSSL and BearSSL demonstrates that fault-aware proofs can be constructed from standard correctness proofs with little additional work, often requiring no novel proof techniques. The results show that developing fault-tolerant correctness proofs is not only feasible, but rote for certain kinds of fault-tolerant programs.

View More Papers

Revealing The Secret Power: How Algorithms Can Influence Content...

Alessandro Galeazzi (University of Padua), Pujan Paudel (Boston University), Mauro Conti (University of Padua and Orebro University), Emiliano De Cristofaro (University of California, Riverside), Gianluca Stringhini (Boston University)

Read More

MinBucket MPSI: Breaking the Max-Size Bottleneck in Multi-Party Private...

Binbin Tu (School of Cyber Science and Technology, Shandong University; State Key Laboratory of Cryptography and Digital Economy Security, Shandong University), Boyudong Zhu (School of Cyber Science and Technology, Shandong University; State Key Laboratory of Cryptography and Digital Economy Security, Shandong University), Yang Cao (School of Cyber Science and Technology, Shandong University; State Key Laboratory…

Read More

Kick Bad Guys Out! Conditionally Activated Anomaly Detection in...

Shanshan Han (University of California, Irvine), Wenxuan Wu (Texas A&M University), Baturalp Buyukates (University of Birmingham), Weizhao Jin (University of Southern California), Qifan Zhang (Palo Alto Networks), Yuhang Yao (Carnegie Mellon University), Salman Avestimehr (University of Southern California)

Read More