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◬ AI & Machine Learning Apr 02, 2026
Differentially Private Manifold Denoising

arXiv:2604.00942v1 Announce Type: cross Abstract: We introduce a differentially private manifold denoising framework that allows users to exploit sensitive reference datasets to correct noisy, non-pri…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Adversarial Attenuation Patch Attack for SAR Object Detection

arXiv:2604.00887v1 Announce Type: cross Abstract: Deep neural networks have demonstrated excellent performance in SAR target detection tasks but remain susceptible to adversarial attacks. Existing SAR…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
UK AISI Alignment Evaluation Case-Study

arXiv:2604.00788v1 Announce Type: cross Abstract: This technical report presents methods developed by the UK AI Security Institute for assessing whether advanced AI systems reliably follow intended go…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
PrivHAR-Bench: A Graduated Privacy Benchmark Dataset for Video-Based Action Recognition

arXiv:2604.00761v1 Announce Type: cross Abstract: Existing research on privacy-preserving Human Activity Recognition (HAR) typically evaluates methods against a binary paradigm: clear video versus a s…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Engineering a Phase-Noise-Based Quantum Random Number Generator for Real-Time Secure Applications: Design, Validation, and Scalability

arXiv:2604.00741v1 Announce Type: cross Abstract: Random Number Generators (RNGs) are crucial for applications ranging from cryptography to simulations. Depending on the source of randomness, RNGs are…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Enhancing REST API Fuzzing with Access Policy Violation Checks and Injection Attacks

arXiv:2604.00702v1 Announce Type: cross Abstract: Due to their widespread use in industry, several techniques have been proposed in the literature to fuzz REST APIs. Existing fuzzers for REST APIs hav…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
LibScan: Smart Contract Library Misuse Detection with Iterative Feedback and Static Verification

arXiv:2604.00657v1 Announce Type: cross Abstract: Smart contracts are self-executing programs that manage financial transactions on blockchain networks. Developers commonly rely on third-party code li…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Secure Forgetting: A Framework for Privacy-Driven Unlearning in Large Language Model (LLM)-Based Agents

arXiv:2604.00430v1 Announce Type: cross Abstract: Large language model (LLM)-based agents have recently gained considerable attention due to the powerful reasoning capabilities of LLMs. Existing resea…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
AgentWatcher: A Rule-based Prompt Injection Monitor

arXiv:2604.01194v1 Announce Type: new Abstract: Large language models (LLMs) and their applications, such as agents, are highly vulnerable to prompt injection attacks. State-of-the-art prompt injectio…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Obfuscating Code Vulnerabilities against Static Analysis in JavaScript Code

arXiv:2604.01131v1 Announce Type: new Abstract: Code obfuscation is widely adopted in modern software development to protect intellectual property and hinder reverse engineering, but it also provides …

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Multi-Agent LLM Governance for Safe Two-Timescale Reinforcement Learning in SDN-IoT Defense

arXiv:2604.01127v1 Announce Type: new Abstract: Software-Defined Networking (SDN) is increasingly adopted to secure Internet-of-Things (IoT) networks due to its centralized control and programmable fo…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
LightGuard: Transparent WiFi Security via Physical-Layer LiFi Key Bootstrapping

arXiv:2604.01092v1 Announce Type: new Abstract: WiFi is inherently vulnerable to eavesdropping because RF signals may penetrate many physical boundaries, such as walls and floors. LiFi, by contrast, i…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Automated Generation of Cybersecurity Exercise Scenarios

arXiv:2604.01079v1 Announce Type: new Abstract: There is a growing need for cybersecurity professionals with practical knowledge and experience to meet societal needs and comply with new standards and…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
VibeGuard: A Security Gate Framework for AI-Generated Code

arXiv:2604.01052v1 Announce Type: new Abstract: "Vibe coding," in which developers delegate code generation to AI assistants and accept the output with little manual review, has gained rapid adoption …

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Automated Framework to Evaluate and Harden LLM System Instructions against Encoding Attacks

arXiv:2604.01039v1 Announce Type: new Abstract: System Instructions in Large Language Models (LLMs) are commonly used to enforce safety policies, define agent behavior, and protect sensitive operation…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
AutoMIA: Improved Baselines for Membership Inference Attack via Agentic Self-Exploration

arXiv:2604.01014v1 Announce Type: new Abstract: Membership Inference Attacks (MIAs) serve as a fundamental auditing tool for evaluating training data leakage in machine learning models. However, exist…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Do Phone-Use Agents Respect Your Privacy?

arXiv:2604.00986v1 Announce Type: new Abstract: We study whether phone-use agents respect privacy while completing benign mobile tasks. This question has remained hard to answer because privacy-compli…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
AutoEG: Exploiting Known Third-Party Vulnerabilities in Black-Box Web Applications

arXiv:2604.00704v1 Announce Type: new Abstract: Large-scale web applications are widely deployed with complex third-party components, inheriting security risks arising from component vulnerabilities. …

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
When Safe Models Merge into Danger: Exploiting Latent Vulnerabilities in LLM Fusion

arXiv:2604.00627v1 Announce Type: new Abstract: Model merging has emerged as a powerful technique for combining specialized capabilities from multiple fine-tuned LLMs without additional training costs…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Quantum-Safe Code Auditing: LLM-Assisted Static Analysis and Quantum-Aware Risk Scoring for Post-Quantum Cryptography Migration

arXiv:2604.00560v1 Announce Type: new Abstract: The impending arrival of cryptographically relevant quantum computers (CRQCs) threatens the security foundations of modern software: Shor's algorithm br…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Lightweight, Practical Encrypted Face Recognition with GPU Support

arXiv:2604.00546v1 Announce Type: new Abstract: Face recognition models operate in a client-server setting where a client extracts a compact face embedding and a server performs similarity search over…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Efficient DPF-based Error-Detecting Information-Theoretic Private Information Retrieval Over Rings

arXiv:2604.00411v1 Announce Type: new Abstract: Authenticated private information retrieval (APIR) is the state-of-the-art error-detecting private information retrieval (ED-PIR), using Distributed Poi…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
RAGShield: Provenance-Verified Defense-in-Depth Against Knowledge Base Poisoning in Government Retrieval-Augmented Generation Systems

arXiv:2604.00387v1 Announce Type: new Abstract: RAG systems deployed across federal agencies for citizen-facing services are vulnerable to knowledge base poisoning attacks, where adversaries inject ma…

arXiv Security Read →
◬ AI & Machine Learning Apr 02, 2026
Cybersecurity Risk Assessment for CubeSat Missions: Adapting Established Frameworks for Resource-Constrained Environments

arXiv:2604.00303v1 Announce Type: new Abstract: CubeSats have democratised access to space for universities, start-ups and emerging space nations, but the same design decisions that reduce cost and co…

arXiv Security Read →
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