Currently free during beta - premium features coming soon. Subscribe now to lock in early access.
AI_SAFETY

EU Regulatory Changes

1476 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: CHRONOS: Temporally-Aware Multi-Agent Coordination for Evolving Data Marketplaces
This document, published on arXiv, introduces CHRONOS, a proposed technical framework for managing multi-agent AI systems that operate in data marketplaces where information changes over time. It i...
Read analysis →
arXiv: On the Stability of Spherical Hellinger-Kantorovich Flows and Their Implications for Differential Privacy
This publication, dated May 22, 2026, presents a theoretical analysis of Spherical Hellinger-Kantorovich flows, a mathematical framework for measuring and controlling the stability of probability d...
Read analysis →
arXiv: A blueprint for constructing 3-pass AKE protocols under commitment-based models
A new academic paper published on arXiv on May 22, 2026, proposes a technical blueprint for constructing three-pass authenticated key exchange (AKE) protocols under commitment-based models. While t...
Read analysis →
arXiv: Validating Threat Modeling Results with the Help of Vulnerable Test Applications
This paper, published on arXiv, presents a novel methodology for validating the results of threat modeling exercises by using deliberately vulnerable test applications. Rather than relying solely o...
Read analysis →
arXiv: Less Effort, Shorter Proofs: Reinforcement Learning for Security Protocol Analysis in Tamarin
This publication introduces a novel application of reinforcement learning to automate and accelerate the analysis of security protocols using the Tamarin prover, a formal verification tool. The aut...
Read analysis →
arXiv: Kernel-Based ReLU Approximation for Homomorphic Encryption-Compatible Privacy-preserving Deep Learning Models
This publication introduces a novel technical method for improving the efficiency of privacy-preserving deep learning models using homomorphic encryption. Specifically, the authors propose a kernel...
Read analysis →
arXiv: CachePrune: Privacy-Aware and Fine-Grained KV Cache Sharing for Efficient LLM Inference
arXiv: Adversarial Vulnerability Under Temporal Concept Drift: A Longitudinal Study of Android Malware Detection
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