Manuel Egele

About

Manuel Egele is an Associate Professor in the Department of Electrical and Computer Engineering at Boston University, with an affiliation as Faculty in the Department of Computer Science. He earned his PhD from Vienna University of Technology in 2011. His areas of interest include Software Security, Web Security, and Security & Privacy on Mobile Systems and Online Social Networks. His research focuses on these security domains, contributing to the understanding and development of secure systems and privacy-preserving technologies.

Research topics

• Computer Science
• Computer Security
• Embedded system
• Programming language
• Software engineering
• Operating system

Selected publications

• SoK: Enabling Security Analyses of Embedded Systems via Rehosting

  2021 · 46 citations

  Closely monitoring the behavior of a software system during its execution enables developers and analysts to observe, and ultimately understand, how it works. This kind of dynamic analysis can be instrumental to reverse engineering, vulnerability discovery, exploit development, and debugging. While these analyses are typically well-supported for homogeneous desktop platforms (e.g., x86 desktop PCs), they can rarely be applied in the heterogeneous world of embedded systems. One approach to enable…

  DOI

• DirectFuzz: Automated Test Generation for RTL Designs using Directed Graybox Fuzzing

  2021 · 45 citations

  A critical challenge in RTL verification is to generate effective test inputs. Recently, RFUZZ proposed to use an automated software testing technique, namely Graybox Fuzzing, to effectively generate test inputs to maximize the coverage of the whole hardware design. For a scenario where a tiny fraction of a large hardware design needs to be tested, the RFUZZ approach is extremely time consuming. In this work, we present DirectFuzz, a directed test generation mechanism. DirectFuzz uses Directed G…

  DOI

• No Grammar, No Problem: Towards Fuzzing the Linux Kernel without System-Call Descriptions

  2023-01-01 · 22 citations

  articleSenior author

  The integrity of the entire computing ecosystem depends on the security of our operating systems (OSes).Unfortunately, due to the scale and complexity of OS code, hundreds of security issues are found in OSes, every year [32].As such, operating systems have constantly been prime use-cases for applying security-analysis tools.In recent years, fuzz-testing has appeared as the dominant technique for automatically finding security issues in software.As such, fuzzing has been adapted to find thousand…

  PublisherDOI

• ProcessorFuzz: Processor Fuzzing with Control and Status Registers Guidance

  2023-05-01 · 19 citations

  article

  As the complexity of modern processors has increased over the years, developing effective verification strategies to identify bugs prior to manufacturing has become critical. Inspired by software fuzzing, a technique commonly used for software testing, multiple recent works use hardware fuzzing for the verification of Register-Transfer Level (RTL) designs. However, these works suffer from several limitations such as lack of support for widelyused Hardware Description Languages (HDLs) and mislead…

  PublisherDOI

• Evocatio

  Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security · 2022-11-07 · 19 citations

  articleOpen access

  The popularity of coverage-guided greybox fuzzers has led to a tsunami of security-critical bugs that developers must prioritize and fix. Knowing the capabilities a bug exposes (e.g., type of vulnerability, number of bytes read/written) enables prioritization of bug fixes. Unfortunately, understanding a bug's capabilities is a time consuming process, requiring (a) an understanding of the bug's root cause, (b) an understanding how an attacker may exploit the bug, and (c) the development of a patc…

  PublisherOA PDFDOI

Recent grants

• CAREER: Toward Securing Emerging Computing Platforms via Large-Scale Dynamic Analysis

  NSF · $559k · 2020–2026

• SaTC: CORE: Medium: Collaborative: Taming Memory Corruption with Security Monitors

  NSF · $800k · 2019–2024

Frequent coauthors

• Christopher Kruegel

  University of California, Santa Barbara

  24 shared

• Gianluca Stringhini

  21 shared

• Ayse K. Coskun

  Boston University

  20 shared

• Engin Kirda

  Northeastern University

  19 shared

• Vitus J. Leung

  Sandia National Laboratories

  16 shared

• Giovanni Vigna

  University of California, Santa Barbara

  15 shared

• Ajay Joshi

  15 shared

• Jim Brandt

  14 shared

Labs

• Data Mining & Data ManagementPI

Education

• Ph.D.

  Vienna University of Technology

  2011

Similar researchers at Boston University

• Kate Saenkoh-89
• Ran Canettih-78
• Stan Sclaroffh-69