International Conference on Dependable Systems and Networks workshops : [proceedings]

Autonomous systems increasingly rely on Large Language Models (LLMs) yet the safety infrastructure surrounding these models introduces latency and compute overhead. This limits utility in resource-constrained, time-critical deployments. Existing external guardrail models remain blind to the model's internal workings, creating a fundamental assurance gap. We ask: does the model already know when the content is harmful? We extract activations from LLaMA-3.1-8B and train lightweight MLP classifier probes (12.6M parameters) to detect harmful prompts. Evaluated on WildJailbreak, Beavertails, and AEGIS 2.0, our probes achieve F1 scores of 99

Agentic artificial intelligence (Agentic AI) systems have undergone a significant evolution, progressing from static predictive models to autonomous entities capable of intricate reasoning, planning, and action over extended temporal horizons. While Agentic AI demonstrates considerable utility across diverse application domains, its deployment in critical infrastructure increasingly highlights fundamental challenges concerning its trustworthiness. Current agentic architectures primarily prioritize performance and flexibility, yet they frequently lack inherent compositional guarantees for critical attributes such as safety, robustness, and accountability. In this position paper, we contend that trust in agentic AI remains largely fragmented and is predominantly established through external mechanisms rather than being intrinsically integrated into system design. We present a perspective on composable trust that bridges the gap between advanced agentic capability and dependable deployment by conceptualizing trust as a compositional property spanning agent loops, interactions, and operational environments. We further outline a roadmap toward system-level assurance by identifying structural limitations in existing architectures and defining the prerequisites for enforceable trust.

The Ising model is useful in searching for (sub)-optimal solutions of combinatorial optimization problems (COPs). CMOS implementations of Ising model-based solvers, commonly referred to as Ising machines, provide reliable and accurate solutions with flexible and dense connectivity. However, they incur a significant hardware overhead. Approximate computing, as a low-power technique, offers a way to reduce hardware complexity, while stochastic computing is efficient in simulating the dynamics of the Ising model. The approximations introduced by these techniques may be beneficial in helping the system escape from local minima. In this talk, we discuss the potential of using approximate and stochastic computing to improve the performance of Ising machines.

Graphical security models–such as attack graphs, attack trees, and Hierarchical Attack Representation Models (HARMs)–are widely used for threat, attack, and security modeling and analysis. This talk introduces their core concepts, including key terminology, security metrics, and the model life cycle. It then highlights recent advances in graphical modeling techniques and their applications, with a focus on domains such as the Internet of Things (IoT) and the security and safety analysis of autonomous vehicles.

The next generation of IoT and edge computing platforms must address security, safety, and reliability requirements. This talk presents the advancements made in two European-funded projects, LoLiPoP IoT and SuzECK, that concentrate on safe, interference-free gateway architectures and a robust, self-managed platform for critical edge applications. In LoLiPoP IoT, a modular gateway integrates Mioty and BLE-based applications, ensuring isolation, real-time security key provisioning via HSM over an NXP S32G3 System on Chip (SOC), and a PikeOSbased execution environment enhanced by Grafana visualization, firewalling, and intrusion detection/prevention mechanisms. SuzECK enhances this strategy by creating a secure computing platform powered by Kubernetes, in which nodes are dynamically managed as computing units. For safety-critical edge services, fault-tolerant execution is ensured by hardware-level failover, redundant application copies, and an intelligent voting mechanism. This keynote will explore the architectural challenges, security designs, deployment strategies, and future research perspectives in secure IoT and edge computing. It will also provide insights into leveraging containerization, modular security frameworks, and self-healing node orchestration for next-generation digital infrastructures.