Offensive Security for AI Systems: Concepts, Practices, and Applications

As artificial intelligence AI systems become increasingly adopted across sectors, the need for robust, proactive security strategies is paramount. Traditional defensive measures often fall short against the unique and evolving threats facing AI-driven technologies, making offensive security an...

RiM: Record, Improve and Maintain Physical Well-Being Using Federated Learning

In academic settings, the demanding environment often forces students to prioritize academic performance over their physical well-being. Moreover, privacy concerns and the inherent risk of data breaches hinder the deployment of traditional machine learning techniques for addressing these health...

Security Steerability Is All You Need

The adoption of Generative AI GenAI in various applications inevitably comes with expanding the attack surface, combining new security threats along with the traditional ones. Consequently, numerous research and industrial initiatives aim to mitigate these security threats in GenAI by developing...

HashKitty: Distributed Password Analysis

This article documents the HashKitty platform, a distributed solution for password analysis based on the hashcat tool, designed to improve efficiency in both offensive and defensive security operations. The main objectives of this work are to utilise and characterise the hashcat tool, to develop ...

An Empirical Study of Fuzz Harness Degradation

The purpose of continuous fuzzing platforms is to enable fuzzing for software projects via \emphfuzz harnesses -- but as the projects continue to evolve, are these harnesses updated in lockstep, or do they run out of date? If these harnesses remain unmaintained, will they \emphdegrade over time i...

Measuring Security in 5G and Future Networks

In today's increasingly interconnected and fast-paced digital ecosystem, mobile networks, such as 5G and future generations such as 6G, play a pivotal role and must be considered as critical infrastructures. Ensuring their security is paramount to safeguard both individual users and the industrie...

Leakage-Resilient Algebraic Manipulation Detection Codes with Optimal Parameters

Algebraic Manipulation Detection AMD codes is a cryptographic primitive that was introduced by Cramer, Dodis, Fehr, Padro and Wichs. They are keyless message authentication codes that protect messages against additive tampering by the adversary assuming that the adversary cannot "see" the codewor...

XNU VM_BEHAVIOR_ZERO_WIRED_PAGES Page Write

There is an issue where XNU VMBEHAVIORZEROWIREDPAGES behavior allows writing to read-only pages...

Towards AI-Driven Human-Machine Co-Teaming for Adaptive and Agile Cyber Security Operation Centers

Security Operations Centers SOCs face growing challenges in managing cybersecurity threats due to an overwhelming volume of alerts, a shortage of skilled analysts, and poorly integrated tools. Human-AI collaboration offers a promising path to augment the capabilities of SOC analysts while reducin...

Remote Rowhammer Attack Using Adversarial Observations on Federated Learning Clients

Federated Learning FL has the potential for simultaneous global learning amongst a large number of parallel agents, enabling emerging AI such as LLMs to be trained across demographically diverse data. Central to this being efficient is the ability for FL to perform sparse gradient updates and...

Comparing Classical and Quantum Conditional Disclosure of Secrets

The conditional disclosure of secrets CDS setting is among the most basic primitives studied in information-theoretic cryptography. Motivated by a connection to non-local quantum computation and position-based cryptography, CDS with quantum resources has recently been considered. Here, we study t...

Intrusion Detection System Using Deep Learning for Network Security

As the number of cyberattacks and their particualr nature escalate, the need for effective intrusion detection systems IDS has become indispensable for ensuring the security of contemporary networks. Adaptive and more sophisticated threats are often beyond the reach of traditional approaches to...

Learning from the Good Ones: Risk Profiling-Based Defenses against Evasion Attacks on DNNs

Safety-critical applications such as healthcare and autonomous vehicles use deep neural networks DNN to make predictions and infer decisions. DNNs are susceptible to evasion attacks, where an adversary crafts a malicious data instance to trick the DNN into making wrong decisions at inference time...

System Prompt Poisoning: Persistent Attacks on Large Language Models beyond User Injection

Large language models LLMs have gained widespread adoption across diverse applications due to their impressive generative capabilities. Their plug-and-play nature enables both developers and end users to interact with these models through simple prompts. However, as LLMs become more integrated in...

Engineering Risk-Aware, Security-By-Design Frameworks for Assurance of Large-Scale Autonomous AI Models

As AI models scale to billions of parameters and operate with increasing autonomy, ensuring their safe, reliable operation demands engineering-grade security and assurance frameworks. This paper presents an enterprise-level, risk-aware, security-by-design approach for large-scale autonomous AI...

Exploring the Susceptibility to Fraud of Monetary Incentive Mechanisms for Strengthening FOSS Projects

Free and open source software FOSS is ubiquitous on modern IT systems, accelerating the speed of software engineering over the past decades. With its increasing importance and historical reliance on uncompensated contributions, questions have been raised regarding the continuous maintenance of FO...

Sponge Attacks on Sensing AI: Energy-Latency Vulnerabilities and Defense Via Model Pruning

Recent studies have shown that sponge attacks can significantly increase the energy consumption and inference latency of deep neural networks DNNs. However, prior work has focused primarily on computer vision and natural language processing tasks, overlooking the growing use of lightweight AI...

LATENT: LLM-Augmented Trojan Insertion and Evaluation Framework for Analog Netlist Topologies

Analog and mixed-signal A/MS integrated circuits ICs are integral to safety-critical applications. However, the globalization and outsourcing of A/MS ICs to untrusted third-party foundries expose them to security threats, particularly analog Trojans. Unlike digital Trojans which have been...

Towards Quantum Resilience: Data-Driven Migration Strategy Design

The advancements in quantum computing are a threat to classical cryptographic systems. The traditional cryptographic methods that utilize factorization-based or discrete-logarithm-based algorithms, such as RSA and ECC, are some of these. This paper thoroughly investigates the vulnerabilities of...

Apache ActiveMQ 6.1.6 Denial of Service

Apache ActiveMQ version 6.1.6 denial of service proof of concept exploit. This tool sends malicious OpenWire packets to exhaust the JVM heap memory of the target server, potentially crashing the ActiveMQ service on port 61616...

Causality for Cyber-Physical Systems

We present a formal theory for analysing causality in cyber-physical systems. To this end, we extend the theory of actual causality by Halpern and Pearl to cope with the continuous nature of cyber-physical systems. Based on our theory, we develop an analysis technique that is used to uncover the...

Nuclei 3.4.3

Nuclei is a modern, high-performance vulnerability scanner that leverages simple YAML-based templates. It empowers you to design custom vulnerability detection scenarios that mimic real-world conditions, leading to zero false positives...

Safety Analysis in the NGAC Model

We study the safety problem for the next-generation access control NGAC model. We show that under mild assumptions it is coNP-complete, and under further realistic assumptions we give an algorithm for the safety problem that significantly outperforms naive brute force search. We also show that...

Economic Security of Multiple Shared Security Protocols

Whitepaper called Economic Security Of Multiple Shared Security Protocols...

Vehicular Communication Security: Multi-Channel and Multi-Factor Authentication

Secure and reliable communications are crucial for Intelligent Transportation Systems ITSs, where Vehicle-to-Infrastructure V2I communication plays a key role in enabling mobility-enhancing and safety-critical services. Current V2I authentication relies on credential-based methods over wireless...

An Agent-Based Modeling Approach to Free-Text Keyboard Dynamics for Continuous Authentication

Continuous authentication systems leveraging free-text keyboard dynamics offer a promising additional layer of security in a multifactor authentication setup that can be used in a transparent way with no impact on user experience. This study investigates the efficacy of behavioral biometrics by...

Large Language Model-Driven Security Assistant for Internet of Things Via Chain-Of-Thought

The rapid development of Internet of Things IoT technology has transformed people's way of life and has a profound impact on both production and daily activities. However, with the rapid advancement of IoT technology, the security of IoT devices has become an unavoidable issue in both research an...

WordPress Depicter Slider and Popup Builder SQL Injection

WordPress Depicter Slider and Popup Builder plugin versions prior to 3.6.2 suffer from a remote SQL injection vulnerability...

Invariant-Based Cryptography

We propose a new symmetric cryptographic scheme based on functional invariants defined over discrete oscillatory functions with hidden parameters. The scheme encodes a secret integer through a four-point algebraic identity preserved under controlled parameterization. Security arises not from...

Building Trustworthy Multimodal AI: a Review of Fairness, Transparency, and Ethics in Vision-Language Tasks

Objective: This review explores the trustworthiness of multimodal artificial intelligence AI systems, specifically focusing on vision-language tasks. It addresses critical challenges related to fairness, transparency, and ethical implications in these systems, providing a comparative analysis of...

Wazuh 4.12.0

Wazuh is a free and open source security platform that unifies XDR and SIEM capabilities. It protects workloads across on-premises, virtualized, containerized, and cloud-based environments. This is the source code release...

Optimal Regret of Bernoulli Bandits under Global Differential Privacy

As sequential learning algorithms are increasingly applied to real life, ensuring data privacy while maintaining their utilities emerges as a timely question. In this context, regret minimisation in stochastic bandits under $ε$-global Differential Privacy DP has been widely studied. Unlike bandit...

A Weighted Byzantine Fault Tolerance Consensus Driven Trusted Multiple Large Language Models Network

Large Language Models LLMs have achieved remarkable success across a wide range of applications. However, individual LLMs often produce inconsistent, biased, or hallucinated outputs due to limitations in their training corpora and model architectures. Recently, collaborative frameworks such as th...

Efficient Full-Stack Private Federated Deep Learning with Post-Quantum Security

Federated learning FL enables collaborative model training while preserving user data privacy by keeping data local. Despite these advantages, FL remains vulnerable to privacy attacks on user updates and model parameters during training and deployment. Secure aggregation protocols have been...

Integrating Communication, Sensing, and Security: Progress and Prospects of PLS in ISAC Systems

The sixth generation of wireless networks defined several key performance indicators KPIs for assessing its networks, mainly in terms of reliability, coverage, and sensing. In this regard, remarkable attention has been paid recently to the integrated sensing and communication ISAC paradigm as an...

Nmap Port Scanner 7.96

Nmap is a utility for port scanning large networks, although it works fine for single hosts. Sometimes you need speed, other times you may need stealth. In some cases, bypassing firewalls may be required. Not to mention the fact that you may want to scan different protocols UDP, TCP, ICMP, etc...

Bringing Forensic Readiness to Modern Computer Firmware

Today's computer systems come with a pre-installed tiny operating system, which is also known as UEFI. UEFI has slowly displaced the former legacy PC-BIOS while the main task has not changed: It is responsible for booting the actual operating system. However, features like the network stack make ...

Crowding out the Noise: Algorithmic Collective Action under Differential Privacy

The integration of AI into daily life has generated considerable attention and excitement, while also raising concerns about automating algorithmic harms and re-entrenching existing social inequities. While the responsible deployment of trustworthy AI systems is a worthy goal, there are many...

SoK: a Taxonomy for Distributed-Ledger-Based Identity Management

The intersection of blockchain distributed ledger and identity management lacks a comprehensive framework for classifying distributed-ledger-based identity solutions. This paper introduces a methodologically developed taxonomy derived from the analysis of 390 scientific papers and expert...

Botan C++ Crypto Algorithms Library 3.8.1

Botan is a C++ library of cryptographic algorithms, including AES, DES, SHA-1, RSA, DSA, Diffie-Hellman, and many others. It also supports X.509 certificates and CRLs, and PKCS 10 certificate requests, and has a high level filter/pipe message processing system. The library is easily portable to...

Privacy-Preserving Transformers: SwiftKey'S Differential Privacy Implementation

In this paper we train a transformer using differential privacy DP for language modeling in SwiftKey. We run multiple experiments to balance the trade-off between the model size, run-time speed and accuracy. We show that we get small and consistent gains in the next-word-prediction and accuracy...

User Behavior Analysis in Privacy Protection with Large Language Models: a Study on Privacy Preferences with Limited Data

With the widespread application of large language models LLMs, user privacy protection has become a significant research topic. Existing privacy preference modeling methods often rely on large-scale user data, making effective privacy preference analysis challenging in data-limited environments...

Enhancing Blockchain Cross Chain Interoperability: a Comprehensive Survey

Blockchain technology, introduced in 2008, has revolutionized data storage and transfer across sectors such as finance, healthcare, intelligent transportation, and the metaverse. However, the proliferation of blockchain systems has led to discrepancies in architectures, consensus mechanisms, and...

Threat Modeling for AI: the Case for an Asset-Centric Approach

Recent advances in AI are transforming AI's ubiquitous presence in our world from that of standalone AI-applications into deeply integrated AI-agents. These changes have been driven by agents' increasing capability to autonomously make decisions and initiate actions, using existing applications;...

QUIC-Exfil: Exploiting QUIC'S Server Preferred Address Feature to Perform Data Exfiltration Attacks

The QUIC protocol is now widely adopted by major tech companies and accounts for a significant fraction of today's Internet traffic. QUIC's multiplexing capabilities, encrypted headers, dynamic IP address changes, and encrypted parameter negotiations make the protocol not only more efficient,...

FedTDP: a Privacy-Preserving and Unified Framework for Trajectory Data Preparation Via Federated Learning

Trajectory data, which capture the movement patterns of people and vehicles over time and space, are crucial for applications like traffic optimization and urban planning. However, issues such as noise and incompleteness often compromise data quality, leading to inaccurate trajectory analyses and...

MTL-UE: Learning to Learn Nothing for Multi-Task Learning

Most existing unlearnable strategies focus on preventing unauthorized users from training single-task learning STL models with personal data. Nevertheless, the paradigm has recently shifted towards multi-task data and multi-task learning MTL, targeting generalist and foundation models that can...

Defending against Indirect Prompt Injection by Instruction Detection

The integration of Large Language Models LLMs with external sources is becoming increasingly common, with Retrieval-Augmented Generation RAG being a prominent example. However, this integration introduces vulnerabilities of Indirect Prompt Injection IPI attacks, where hidden instructions embedded...

Botan C++ Crypto Algorithms Library 3.8.0

Botan is a C++ library of cryptographic algorithms, including AES, DES, SHA-1, RSA, DSA, Diffie-Hellman, and many others. It also supports X.509 certificates and CRLs, and PKCS 10 certificate requests, and has a high level filter/pipe message processing system. The library is easily portable to...

Federated Learning for Cyber Physical Systems: a Comprehensive Survey

The integration of machine learning ML in cyber physical systems CPS is a complex task due to the challenges that arise in terms of real-time decision making, safety, reliability, device heterogeneity, and data privacy. There are also open research questions that must be addressed in order to ful...