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AI_SAFETY

EU Regulatory Changes

668 changes tracked across 24 compliance frameworks including DORA, NIS2, GDPR, EU AI Act, Cyber Resilience Act, and more.

All DORA NIS2 GDPR CSRD MaRisk ISO27001 EU_AI_ACT CRA DSA DMA eIDAS2 SOC2 PCI_DSS HIPAA ISO42001 AMLD6 PSD3 DATA_ACT GPSR CER EUDR CVE BREACH AI_SAFETY
arXiv: When Youth Enter the Algorithmic Wild: Discovering and Understanding Potentially Harmful Teen Videos on Douyin...
arXiv: AI Security Research Should Better Incentivize Defense Research
arXiv: Communication Security and Sensing Privacy in FMCW-Based ISAC Through Signal Modulation
arXiv: Sample-wise Targeted Adversarial Attacks on Test-time Adaptation
arXiv: Security, Privacy, and Ethical Risks in OpenClaw
arXiv: Formal Verification of Probing Security via Conditional Independence
arXiv: Are Frontier LLMs Ready for Cybersecurity? Evidence for Vertical Foundation Models from Dual-Mode Vulnerabilit...
arXiv: On APN Exponents and the Differential and Boomerang Properties of Binomials in Characteristic 3
arXiv: Prompt Overflow: What the Guardrail Inspects Is Not What the Model Infers
arXiv: Robust LLM Watermarking with Minimal Semantic Distortion for IP Protection
arXiv: PoisonForge: Task-Level Targeted Poisoning Benchmark for Instruction-Tuned LLMs
arXiv: What Does the Server See? Understanding Privacy Leakage from Large Language Models in Split Inference
arXiv: From Preventive to Reactive: How AI Coding Assistants Transform Developers' Security Awareness
arXiv: TriSweep: A Four-Drone Swarm Framework for Electromagnetic Side-Channel Analysis
This publication, TriSweep: A Four-Drone Swarm Framework for Electromagnetic Side-Channel Analysis, presents a novel research paper detailing a proof-of-concept system where a coordinated swarm of ...
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arXiv: UNAD+: An Explainable Hybrid Framework for Unknown Network Attack Detection
This publication introduces UNAD+, a novel hybrid artificial intelligence framework designed to detect previously unknown network attacks with enhanced explainability. The framework combines deep l...
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arXiv: Innovations in Cardless Artificial Intelligence Banking: A Comprehensive Framework for Cyber Secure and Fraud ...
This is a pre-print academic paper, not a regulatory change. It proposes a technical framework for using machine learning to enhance security and fraud detection in cardless AI-driven banking syste...
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arXiv: Building an Open Source Operational Technology Pentesting Platform: Lessons from LINICS
This publication, released on 21 May 2026, presents a detailed case study on building an open-source operational technology (OT) pentesting platform, derived from the LINICS project. While not a re...
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arXiv: A Formal Basis for Quantum Cryptographic Exposure Measurement under HNDL Threat
This paper, published on arXiv on 21 May 2026, introduces a formal mathematical framework for measuring the exposure of cryptographic systems to threats from High-Dimensional Neural Decryption (HND...
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arXiv: Measuring Security Without Fooling Ourselves: Why Benchmarking Agents Is Hard
This publication is a research paper from arXiv that critically examines the reliability of current benchmarking methods used to measure the safety and security of autonomous AI agents. It argues t...
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arXiv: EnCAgg: Enhanced Clustering Aggregation for Robust Federated Learning against Dynamic Model Poisoning
This paper, published on arXiv, proposes a new technical method called EnCAgg designed to make federated learning systems more resilient to attacks where malicious participants deliberately corrupt...
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