Laura Matzen, Michelle A Leger, Geoffrey Reedy (Sandia National Laboratories)

Binary reverse engineers combine automated and manual techniques to answer questions about software. However, when evaluating automated analysis results, they rarely have additional information to help them contextualize these results in the binary. We expect that humans could more readily understand the binary program and these analysis results if they had access to information usually kept internal to the analysis, like value-set analysis (VSA) information. However, these automated analyses often give up precision for scalability, and imprecise information might hinder human decision making.

To assess how precision of VSA information affects human analysts, we designed a human study in which reverse engineers answered short information flow problems, determining whether code snippets would print sensitive information. We hypothesized that precise VSA information would help our participants analyze code faster and more accurately, and that imprecise VSA information would lead to slower, less accurate performance than no VSA information. We presented hand-crafted code snippets with precise, imprecise, or no VSA information in a blocked design, recording participants’ eye movements, response times, and accuracy while they analyzed the snippets. Our data showed that precise VSA information changed participants’ problem-solving strategies and supported faster, more accurate analyses. However, surprisingly, imprecise VSA information also led to increased accuracy relative to no VSA information, likely due to the extra time participants spent working through the code.

View More Papers

FARE: Enabling Fine-grained Attack Categorization under Low-quality Labeled Data

Junjie Liang (The Pennsylvania State University), Wenbo Guo (The Pennsylvania State University), Tongbo Luo (Robinhood), Vasant Honavar (The Pennsylvania State University), Gang Wang (University of Illinois at Urbana-Champaign), Xinyu Xing (The Pennsylvania State University)

Read More

SquirRL: Automating Attack Analysis on Blockchain Incentive Mechanisms with...

Charlie Hou (CMU, IC3), Mingxun Zhou (Peking University), Yan Ji (Cornell Tech, IC3), Phil Daian (Cornell Tech, IC3), Florian Tramèr (Stanford University), Giulia Fanti (CMU, IC3), Ari Juels (Cornell Tech, IC3)

Read More

The evolution of program analysis approaches in the era...

Alex Matrosov (CEO and Founder of Binarly Inc.)

Read More

As Strong As Its Weakest Link: How to Break...

Kai Li (Syracuse University), Jiaqi Chen (Syracuse University), Xianghong Liu (Syracuse University), Yuzhe Tang (Syracuse University), XiaoFeng Wang (Indiana University Bloomington), Xiapu Luo (Hong Kong Polytechnic University)

Read More