Naif Saleh Almakhdhub (Purdue University and King Saud University), Abraham A. Clements (Sandia National Laboratories), Saurabh Bagchi (Purdue University), Mathias Payer (EPFL)

Embedded systems are deployed in security critical environments and have become a prominent target for remote attacks. Microcontroller-based systems (MCUS) are particularly vulnerable due to a combination of limited resources and low level programming which leads to bugs. Since MCUS are often a part of larger systems, vulnerabilities may jeopardize not just the security of the device itself but that of other systems as well. For example, exploiting a WiFi System on Chip (SoC) allows an attacker to hijack the smart phone's application processor.

Control-flow hijacking targeting the backward edge (e.g., Return-Oriented Programming--ROP) remains a threat for MCUS. Current defenses are either susceptible to ROP-style attacks or require special hardware such as a Trusted Execution Environment (TEE) that is not commonly available on MCUS.

We present µRAI, a compiler-based mitigation to emph{prevent} control-flow hijacking attacks targeting backward edges by enforcing the emph{Return Address Integrity (RAI)} property on MCUS. µRAI does not require any additional hardware such as TEE, making it applicable to the wide majority of MCUS. To achieve this, µRAI introduces a technique that moves return addresses from writable memory, to readable and executable memory. It re-purposes a single general purpose register that is never spilled, and uses it to resolve the correct return location. We evaluate against the different control-flow hijacking attacks scenarios targeting return addresses (e.g., arbitrary write), and demonstrate how µRAI prevents them all. Moreover, our evaluation shows that µRAI enforces its protection with negligible overhead.

View More Papers

coucouArray ( [post_type] => ndss-paper [post_status] => publish [posts_per_page] => 4 [orderby] => rand [tax_query] => Array ( [0] => Array ( [taxonomy] => category [field] => id [terms] => Array ( [0] => 39 ) ) ) [post__not_in] => Array ( [0] => 5858 ) )

OmegaLog: High-Fidelity Attack Investigation via Transparent Multi-layer Log Analysis

Wajih Ul Hassan (University of Illinois Urbana-Champaign), Mohammad A. Noureddine (University of Illinois Urbana-Champaign), Pubali Datta (University of Illinois Urbana-Champaign), Adam Bates (University of Illinois Urbana-Champaign)

Read More

Withdrawing the BGP Re-Routing Curtain: Understanding the Security Impact...

Jared M. Smith (University of Tennessee, Knoxville), Kyle Birkeland (University of Tennessee, Knoxville), Tyler McDaniel (University of Tennessee, Knoxville), Max Schuchard (University of Tennessee, Knoxville)

Read More

When Match Fields Do Not Need to Match: Buffered...

Jiahao Cao (Tsinghua University; George Mason University), Renjie Xie (Tsinghua University), Kun Sun (George Mason University), Qi Li (Tsinghua University), Guofei Gu (Texas A&M University), Mingwei Xu (Tsinghua University)

Read More

Towards Plausible Graph Anonymization

Yang Zhang (CISPA Helmholtz Center for Information Security), Mathias Humbert (armasuisse Science and Technology), Bartlomiej Surma (CISPA Helmholtz Center for Information Security), Praveen Manoharan (CISPA Helmholtz Center for Information Security), Jilles Vreeken (CISPA Helmholtz Center for Information Security), Michael Backes (CISPA Helmholtz Center for Information Security)

Read More

Privacy Starts with UI: Privacy Patterns and Designer Perspectives in UI/UX Practice

Anxhela Maloku (Technical University of Munich), Alexandra Klymenko (Technical University of Munich), Stephen Meisenbacher (Technical University of Munich), Florian Matthes (Technical University of Munich)

Vision: Profiling Human Attackers: Personality and Behavioral Patterns in Deceptive Multi-Stage CTF Challenges

Khalid Alasiri (School of Computing and Augmented Intelligence Arizona State University), Rakibul Hasan (School of Computing and Augmented Intelligence Arizona State University)

From Underground to Mainstream Marketplaces: Measuring AI-Enabled NSFW Deepfakes on Fiverr

Mohamed Moustafa Dawoud (University of California, Santa Cruz), Alejandro Cuevas (Princeton University), Ram Sundara Raman (University of California, Santa Cruz)