Related papers: What Gets Measured Gets Managed: Mitigating Supply…
Security-critical system requirements are increasingly enforced through mandatory access control systems. These systems are controlled by security policies, highly sensitive system components, which emphasizes the paramount importance of…
With the rapid advancement of large language models , code generation has become a key benchmark for evaluating LLM capabilities. However, existing benchmarks face two major challenges: (1) the escalating cost of constructing high-quality…
Databases in the past have helped businesses maintain and extract insights from their data. Today, it is common for a business to involve multiple independent, distrustful parties. This trend towards decentralization introduces a new and…
Trust is of paramount concern for tenants to deploy their security-sensitive services in the cloud. The integrity of VMs in which these services are deployed needs to be ensured even in the presence of powerful adversaries with…
For given system dynamics, observer structure, and observer-based fault/attack detection procedure, we provide mathematical tools -- in terms of Linear Matrix Inequalities (LMIs) -- for computing outer ellipsoidal bounds on the set of…
Large Language Models (LLMs), especially those accessed via APIs, have demonstrated impressive capabilities across various domains. However, users without technical expertise often turn to (untrustworthy) third-party services, such as…
Industrial Control Systems (ICS) are often built from geographically distributed components and often use programmable logic controllers for localized processes. Since verification of such systems is challenging because of both time…
Tool-augmented LLM agents introduce security risks that extend beyond user-input filtering, including indirect prompt injection through fetched content, unsafe tool execution, credential leakage, and tampering with local control files. We…
The robustness of critical infrastructure systems is contingent upon the integrity and transparency of their software supply chains. A Software Bill of Materials (SBOM) is pivotal in this regard, offering an exhaustive inventory of…
Multi-agent LLM systems introduce a security risk in which sensitive information accessed by one agent can propagate through shared context and reappear in downstream outputs, even without explicit adversarial intent. We formalise this…
Recommender systems (RS) are widely used in e-commerce for personalized suggestions, yet their openness makes them susceptible to shilling attacks, where adversaries inject fake behaviors to manipulate recommendations. Most existing…
Over the past few decades, Industrial Control Systems (ICSs) have been targeted by cyberattacks and are becoming increasingly vulnerable as more ICSs are connected to the internet. Using Machine Learning (ML) for Intrusion Detection Systems…
The software bill of materials (SBOM) concept aims to include more information about a software build such as copyrights, dependencies and security references. But SBOM lacks visibility into the process for building a package. Efforts such…
In this paper, we present VerifyML, the first secure inference framework to check the fairness degree of a given Machine learning (ML) model. VerifyML is generic and is immune to any obstruction by the malicious model holder during the…
Today's Internet maps, which are all collected from a small number of vantage points, are falling short of being accurate. We suggest here a paradigm shift for this task. DIMES is a distributed measurement infrastructure for the Internet…
Critical Infrastructure Protection is one of the main challenges of last years. Security Information and Event Management (SIEM) systems are widely used for coping with this challenge. However, they currently present several limitations…
Attacks on software systems occur world-wide on a daily basis targeting individuals, corporations, and governments alike. The systems that facilitate maritime shipping are at risk of serious disruptions, and these disruptions can stem from…
In this paper, we focus on addressing the challenges of detecting malicious attacks in networks by designing an advanced Explainable Intrusion Detection System (xIDS). The existing machine learning and deep learning approaches have…
The widespread integration of wearable sensing devices in Internet of Things (IoT) ecosystems, particularly in healthcare, smart homes, and industrial applications, has required robust human activity recognition (HAR) techniques to improve…
Traceability systems have become prevalent in supply chains because of the rapid development of RFID and IoT technologies. These systems facilitate product recall and mitigate problems such as counterfeiting, tampering, and theft by…