Adam Humphries (University of North Carolina), Kartik Cating-Subramanian (University of Colorado), Michael K. Reiter (Duke University)

We present the design and implementation of a tool called TASE that uses transactional memory to reduce the latency of symbolic-execution applications with small amounts of symbolic state.
Execution paths are executed natively while operating on concrete values, and only when execution encounters symbolic values (or modeled functions) is native execution suspended and interpretation begun. Execution then returns to its native mode when symbolic values are no longer encountered. The key innovations in the design of TASE are a technique for amortizing the cost of checking whether values are symbolic over few instructions, and the use of hardware-supported transactional memory (TSX) to implement native execution that rolls back with no effect when use of a symbolic value is detected (perhaps belatedly). We show that TASE has the potential to dramatically improve some latency-sensitive applications of symbolic execution, such as methods to verify the behavior of a client in a client-server application.

View More Papers

HERA: Hotpatching of Embedded Real-time Applications

Christian Niesler (University of Duisburg-Essen), Sebastian Surminski (University of Duisburg-Essen), Lucas Davi (University of Duisburg-Essen)

Read More

Taking a Closer Look at the Alexa Skill Ecosystem

Christopher Lentzsch (Ruhr-Universität Bochum), Anupam Das (North Carolina State University)

Read More

Demo #3: Detecting Illicit Drone Video Filming Using Cryptanalysis

Ben Nassi, Raz Ben-Netanel (Ben-Gurion University of the Negev), Adi Shamir (Weizmann Institute of Science), and Yuval Elovic (Ben-Gurion University of the Negev)

Read More

FLTrust: Byzantine-robust Federated Learning via Trust Bootstrapping

Xiaoyu Cao (Duke University), Minghong Fang (The Ohio State University), Jia Liu (The Ohio State University), Neil Zhenqiang Gong (Duke University)

Read More