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◬ AI & Machine Learning Apr 09, 2026
Aegon: Auditable AI Content Access with Ledger-Bound Tokens and Hardware-Attested Mobile Receipts

arXiv:2604.06693v1 Announce Type: new Abstract: Recent standards such as RSL address AI content policy declaration -- telling AI systems what the licensing terms are. However, no existing system provi…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
RPM-Net Reciprocal Point MLP Network for Unknown Network Security Threat Detection

arXiv:2604.06638v1 Announce Type: new Abstract: Effective detection of unknown network security threats in multi-class imbalanced environments is critical for maintaining cyberspace security. Current …

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Argus: Reorchestrating Static Analysis via a Multi-Agent Ensemble for Full-Chain Security Vulnerability Detection

arXiv:2604.06633v1 Announce Type: new Abstract: Recent advancements in Large Language Models (LLMs) have sparked interest in their application to Static Application Security Testing (SAST), primarily …

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
PoC-Adapt: Semantic-Aware Automated Vulnerability Reproduction with LLM Multi-Agents and Reinforcement Learning-Driven Adaptive Policy

arXiv:2604.06618v1 Announce Type: new Abstract: While recent approaches leverage large language models (LLMs) and multi-agent pipelines to automatically generate proof-of-concept (PoC) exploits from v…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Can Drift-Adaptive Malware Detectors Be Made Robust? Attacks and Defenses Under White-Box and Black-Box Threats

arXiv:2604.06599v1 Announce Type: new Abstract: Concept drift and adversarial evasion are two major challenges for deploying machine learning-based malware detectors. While both have been studied sepa…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
SkillSieve: A Hierarchical Triage Framework for Detecting Malicious AI Agent Skills

arXiv:2604.06550v1 Announce Type: new Abstract: OpenClaw's ClawHub marketplace hosts over 13,000 community-contributed agent skills, and between 13% and 26% of them contain security vulnerabilities ac…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Guiding Symbolic Execution with Static Analysis and LLMs for Vulnerability Discovery

arXiv:2604.06506v1 Announce Type: new Abstract: Symbolic execution detects vulnerabilities with precision, but applying it to large codebases requires harnesses that set up symbolic state, model depen…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
The Defense Trilemma: Why Prompt Injection Defense Wrappers Fail?

arXiv:2604.06436v1 Announce Type: new Abstract: We prove that no continuous, utility-preserving wrapper defense-a function $D: X\to X$ that preprocesses inputs before the model sees them-can make all …

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Towards Resilient Intrusion Detection in CubeSats: Challenges, TinyML Solutions, and Future Directions

arXiv:2604.06411v1 Announce Type: new Abstract: CubeSats have revolutionized access to space by providing affordable and accessible platforms for research and education. However, their reliance on Com…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Say Something Else: Rethinking Contextual Privacy as Information Sufficiency

arXiv:2604.06409v1 Announce Type: new Abstract: LLM agents increasingly draft messages on behalf of users, yet users routinely overshare sensitive information and disagree on what counts as private. E…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
WebSP-Eval: Evaluating Web Agents on Website Security and Privacy Tasks

arXiv:2604.06367v1 Announce Type: new Abstract: Web agents automate browser tasks, ranging from simple form completion to complex workflows like ordering groceries. While current benchmarks evaluate g…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Blockchain and AI: Securing Intelligent Networks for the Future

arXiv:2604.06323v1 Announce Type: new Abstract: The rapid evolution of intelligent networks under the Internet of Everything (IoE) paradigm is transforming connectivity by integrating people, processe…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
FedSpy-LLM: Towards Scalable and Generalizable Data Reconstruction Attacks from Gradients on LLMs

arXiv:2604.06297v1 Announce Type: new Abstract: Given the growing reliance on private data in training Large Language Models (LLMs), Federated Learning (FL) combined with Parameter-Efficient Fine-Tuni…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Adversarial Robustness of Time-Series Classification for Crystal Collimator Alignment

arXiv:2604.06289v1 Announce Type: new Abstract: In this paper, we analyze and improve the adversarial robustness of a convolutional neural network (CNN) that assists crystal-collimator alignment at CE…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Harnessing Hyperbolic Geometry for Harmful Prompt Detection and Sanitization

arXiv:2604.06285v1 Announce Type: new Abstract: Vision-Language Models (VLMs) have become essential for tasks such as image synthesis, captioning, and retrieval by aligning textual and visual informat…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
ClawLess: A Security Model of AI Agents

arXiv:2604.06284v1 Announce Type: new Abstract: Autonomous AI agents powered by Large Language Models can reason, plan, and execute complex tasks, but their ability to autonomously retrieve informatio…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Towards the Development of an LLM-Based Methodology for Automated Security Profiling in Compliance with Ukrainian Cybersecurity Regulations

arXiv:2604.06274v1 Announce Type: new Abstract: In recent years, the pace of development of information technology in various areas has increased drastically, forcing cybersecurity specialists to cons…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Zero Trust in the Context of IoT: Industrial Literature Review, Trends, and Challenges

arXiv:2604.06272v1 Announce Type: new Abstract: The Zero-trust (ZT) model is an increasingly popular model that relies on the idea that no trust should be granted to any entity (network, persons, devi…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Attribution-Driven Explainable Intrusion Detection with Encoder-Based Large Language Models

arXiv:2604.06266v1 Announce Type: new Abstract: Software-Defined Networking (SDN) improves network flexibility but also increases the need for reliable and interpretable intrusion detection. Large Lan…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
SE-Enhanced ViT and BiLSTM-Based Intrusion Detection for Secure IIoT and IoMT Environments

arXiv:2604.06254v1 Announce Type: new Abstract: With the rapid growth of interconnected devices in Industrial and Medical Internet of Things (IIoT and MIoT) ecosystems, ensuring timely and accurate de…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
Policy-Driven Vulnerability Risk Quantification framework for Large-Scale Cloud Infrastructure Data Security

arXiv:2604.06252v1 Announce Type: new Abstract: The exponential growth of Common Vulnerabilities and Exposures (CVE) disclosures poses significant challenges for enterprise security management, necess…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
SALLIE: Safeguarding Against Latent Language & Image Exploits

arXiv:2604.06247v1 Announce Type: new Abstract: Large Language Models (LLMs) and Vision-Language Models (VLMs) remain highly vulnerable to textual and visual jailbreaks, as well as prompt injections (…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
ZitPit: Consumer-Side Admission Control for Agentic Software Intake

arXiv:2604.06241v1 Announce Type: new Abstract: AI IDEs and coding agents compress discovery, fetch, workspace open, installation, and execution into one low-observability loop. Existing defenses such…

arXiv Security Read →
◬ AI & Machine Learning Apr 09, 2026
The Art of Building Verifiers for Computer Use Agents

arXiv:2604.06240v1 Announce Type: new Abstract: Verifying the success of computer use agent (CUA) trajectories is a critical challenge: without reliable verification, neither evaluation nor training s…

arXiv Security Read →
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