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All Changes

EU Regulatory Changes

1668 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
Ransomware: nova claims Divine IT (BD) — Technology
Ransomware: thegentlemen claims Cole Manufacturing (US) — Manufacturing
Ransomware: thegentlemen claims Kozminski University (PL) — Education
Ransomware: thegentlemen claims Constructions Piraino (FR) — Construction
Ransomware: thegentlemen claims Fecovita (AR) — Agriculture and Food Production
CVE-2018-25436 (CVSS 9.8) — WordPress Plugin Baggage Freight Shipping Australia 0.1.0 contains an unrestricted file u...
CVE-2026-49952 (CVSS 9.1) — Discuz! X5.0 releases 20260320 through 20260501 contains an authentication bypass vulnera...
KEV: CVE-2026-54420 — LiteSpeed cPanel Plugin (LiteSpeed cPanel Plugin UNIX Symbolic Link (Symlink) Following Vulnera...
KEV: CVE-2026-20262 — Cisco Catalyst SD-WAN Manager (Cisco Catalyst SD-WAN Manager Directory or Path Traversal Vulner...
NIS 2 : l’ANSSI poursuit et renforce sa dynamique d’accompagnement
Hired by an algorithm: Data protection and AI regulation in modern HR practices
arXiv: When Good Verifiers Go Bad: Self-Improving VLMs Can Regress on New Tasks
This publication, a research paper titled "When Good Verifiers Go Bad," presents findings that are highly relevant to AI safety compliance under the EU AI Act. The study demonstrates that self-impr...
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arXiv: Security Threats and Their Impact on Blockchain Interoperability: Identification and Countermeasures
This document is a research paper published on arXiv, not an official regulatory change. It analyzes security threats to blockchain interoperability, such as bridge attacks and oracle manipulation,...
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arXiv: Detecting Bot Detection: Prevalence, Techniques, and Implications for Web Measurement Research
This publication from June 2026 presents a systematic study on how websites detect and block automated data collection tools, known as bots. The research reveals that bot detection techniques are n...
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arXiv: From Shield to Target: Denial-of-Service Attacks on LLM-Based Agent Guardrails
This paper, published on arXiv on June 12, 2026, presents a novel vulnerability in AI safety guardrails. The research demonstrates that the very mechanisms designed to protect large language model ...
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arXiv: Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach
This publication, titled "Securing the Future of IoMT in the Post-Quantum Era: An Edge-Native Federated Learning Approach," is a research paper from arXiv, not a binding regulatory change. It propo...
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arXiv: Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis
A new academic paper published on arXiv, titled "Breaking TinyML: Why Quantized Neural Networks Need Domain-Specific Security Analysis," highlights a critical vulnerability in quantized neural netw...
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arXiv: REPOSE: Quantifying the Price of Security in Weakly-Hard Real-Time Cyber-Physical Systems
This publication, titled REPOSE: Quantifying the Price of Security in Weakly-Hard Real-Time Cyber-Physical Systems, introduces a formal framework for measuring the trade-off between security enforc...
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arXiv: AgentCyberRange: Benchmarking Frontier AI Systems in Realistic Cyber Ranges
A new research paper, AgentCyberRange, has been published on arXiv, proposing a framework for benchmarking the cybersecurity capabilities of advanced AI systems within realistic cyber range environ...
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arXiv: Security in a Workflow: Exploring Role-Based Agentic Architectures for Vulnerability Handling
This publication from arXiv presents a technical research paper exploring how role-based agentic architectures—essentially, AI systems with specialized roles—can be used to improve vulnerability ha...
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