Fuzzing BusyBox - Leveraging LLM and Crash Reuse for Embedded Bug Unearthing
Discover innovative techniques for enhancing embedded system security through LLM-powered fuzzing and crash reuse, focusing on BusyBox vulnerability detection and software testing optimization.
Intellectual Property Exposure: Subverting and Securing Intellectual Property Encapsulation in Texas Instruments Microcontrollers
Explore security vulnerabilities in Texas Instruments MSP430 microcontrollers' IPE technology and learn practical attack scenarios plus proposed hardware solutions for enhanced protection.
PentestGPT: Evaluating and Harnessing Large Language Models for Automated Penetration Testing
Explore how PentestGPT leverages LLMs to automate penetration testing, addressing challenges in context maintenance and demonstrating significant improvements in task completion for security testing.
AttackGNN: Red-Teaming GNNs in Hardware Security Using Reinforcement Learning
Discover how reinforcement learning can generate adversarial examples to challenge GNN-based hardware security systems, demonstrating vulnerabilities in IP piracy detection and Trojan identification methods.
Shesha: Multi-head Microarchitectural Leakage Discovery in New-generation Intel Processors
Discover how Shesha framework automates detection of transient execution attacks in Intel processors, exploring new vulnerabilities in instruction set extensions and cryptographic implementations.
EVOKE: Efficient Revocation of Verifiable Credentials in IoT Networks
Discover an innovative approach to managing verifiable credentials in IoT networks through EVOKE, a system that enables efficient revocation mechanisms while minimizing computing and storage overhead for resource-constrained devices.
GoFetch: Breaking Constant-Time Cryptographic Implementations Using Data Memory-Dependent Prefetchers
Explore groundbreaking research on data memory-dependent prefetchers and their impact on cryptographic security, focusing on vulnerabilities in Apple M-series processors and constant-time implementations.
Instruction Backdoor Attacks Against Customized Large Language Models
Explore groundbreaking research on instruction backdoor attacks targeting customized LLMs, examining vulnerabilities in GPT applications and proposing effective defense strategies against security threats.
ZenHammer: Rowhammer Attacks on AMD Zen-based Platforms
Discover the first successful Rowhammer attack implementation on AMD Zen processors, exploring reverse engineering techniques, synchronization methods, and exploitation of DDR4/DDR5 memory vulnerabilities.
In Wallet We Trust: Bypassing Digital Wallets Payment Security for Free Shopping
Uncover critical security vulnerabilities in digital wallet payment systems, exploring authentication bypasses, authorization flaws, and access control weaknesses affecting major banks and payment apps.
Formalizing and Benchmarking Prompt Injection Attacks and Defenses
Explore a systematic framework for understanding and evaluating prompt injection attacks on LLM applications, including analysis of 5 attack types and 10 defense mechanisms across multiple models.
Vulnerability-oriented Testing for RESTful APIs
Discover a novel framework for detecting security vulnerabilities in RESTful APIs through functionality-based testing, featuring practical demonstrations and real-world applications in API security testing.
Peep With A Mirror: Breaking The Integrity of Android App Sandboxing via Unprivileged Cache Side Channel
Discover how cache side-channel attacks can breach Android app sandboxing, revealing private app behaviors through unprivileged memory access and dynamic inter-app component sharing techniques.
SpotProxy: Rediscovering the Cloud for Censorship Circumvention
Discover how SpotProxy leverages cloud Spot VMs to create cost-effective, dynamic censorship circumvention proxies that automatically refresh IP addresses while maintaining seamless connectivity.
GuideEnricher: Protecting the Anonymity of Ethereum Mixing Service Users with Deep Reinforcement Learning
Explore how deep reinforcement learning enhances anonymity protection in Ethereum mixing services through automated pattern detection and guidebook enrichment for safer transactions.
FAMOS: Robust Privacy-Preserving Authentication on Payment Apps via Federated Multi-Modal Contrastive Learning
Discover how FAMOS framework enhances mobile payment security through federated multi-modal contrastive learning, offering robust user authentication while protecting privacy and handling background noise.
Threat Experiences and Security Practices of Pakistani Content Creators
Explore the security challenges and protective strategies of Pakistani content creators, focusing on online threats, sociocultural contexts, and the lack of support systems for vulnerable creators.
BUDAlloc: Defeating Use-After-Free Bugs by Decoupling Virtual Address Management from Kernel
Discover how BUDAlloc improves memory safety by detecting use-after-free bugs through virtual address management separation, offering better performance and reduced memory overhead compared to existing solutions.
Safer Digital Intimacy via Proactive Protection Against Image-Based Sexual Abuse
Explore groundbreaking research on protecting digital intimate content sharing through interviews with 52 European content creators, examining threats, defense strategies, and technological solutions for preventing abuse.
Go Go Gadget Hammer: Flipping Nested Pointers for Arbitrary Data Leakage
Explore a novel Rowhammer exploit technique targeting nested pointer dereferences in code patterns, demonstrating enhanced data leakage capabilities and improved attack primitives in Linux kernel security.
VulSim: Leveraging Similarity of Multi-Dimensional Neighbor Embeddings for Vulnerability Detection
Explore advanced vulnerability detection techniques using multi-dimensional neighbor embeddings and deep learning models to enhance software security through improved pattern recognition and data analysis.