Hardware-Efficient Compound IC Protection with Lightweight Cryptography

Over the years, many techniques have been introduced to protect integrated circuits ICs from hardware security threats that emerged in the globalized IC manufacturing supply chain, such as overproduction and piracy. However, most of these techniques have been rendered inefficient since they do no...

Blue Teaming Function-Calling Agents

We present an experimental evaluation that assesses the robustness of four open source LLMs claiming function-calling capabilities against three different attacks, and we measure the effectiveness of eight different defences. Our results show how these models are not safe by default, and how the...

Model Inversion Attacks Meet Cryptographic Fuzzy Extractors

Model inversion attacks pose an open challenge to privacy-sensitive applications that use machine learning ML models. For example, face authentication systems use modern ML models to compute embedding vectors from face images of the enrolled users and store them. If leaked, inversion attacks can...

Multi-Channel Secure Communication Framework for Wireless IoT (MCSC-WoT): Enhancing Security in Internet of Things

In modern smart systems, the convergence of the Internet of Things IoT and Wireless of Things WoT have been revolutionized by offering a broad level of wireless connectivity and communication among various devices. Hitherto, this greater interconnectivity poses important security problems,...

Cascade: Token-Sharded Private LLM Inference

As LLMs continue to increase in parameter size, the computational resources required to run them are available to fewer parties. Therefore, third-party inference services -- where LLMs are hosted by third parties with significant computational resources -- are becoming increasingly popular...

ARMOR: Robust Reinforcement Learning-Based Control for UAVs under Physical Attacks

Unmanned Aerial Vehicles UAVs depend on onboard sensors for perception, navigation, and control. However, these sensors are susceptible to physical attacks, such as GPS spoofing, that can corrupt state estimates and lead to unsafe behavior. While reinforcement learning RL offers adaptive control...

SAFER-D: a Self-Adaptive Security Framework for Distributed Computing Architectures

The rise of the Internet of Things and Cyber-Physical Systems has introduced new challenges on ensuring secure and robust communication. The growing number of connected devices increases network complexity, leading to higher latency and traffic. Distributed computing architectures DCAs have gaine...

libp2p DoS vulnerability from lack of resource management

Impact Versions older than v0.18.0 of go-libp2p are vulnerable to targeted resource exhaustion attacks. These attacks target libp2p’s connection, stream, peer, and memory management. An attacker can cause the allocation of large amounts of memory, ultimately leading to the process getting killed ...