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All Changes

EU Regulatory Changes

1866 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: interlock claims Clearview Eye Centre (CA) — Healthcare
CVE-2026-56351 (CVSS 8.2) — n8n before version 2.4.0 contains a sql injection vulnerability in MySQL, PostgreSQL, and...
CVE-2026-39948 (CVSS 9.8) — Cacti is an open source performance and fault management framework. In versions 1.2.30 an...
CVE-2026-40079 (CVSS 9.8) — Cacti is an open source performance and fault management framework. Versions 1.2.30 and p...
CVE-2026-56786 (CVSS 9.8) — RTKLIB through 2.4.3 contains an out-of-bounds write vulnerability in decode_type1033 fun...
CVE-2025-71327 (CVSS 9.1) — Flowise contains an authentication bypass vulnerability in the unprotected /api/v1/accoun...
CVE-2025-71334 (CVSS 9.8) — Flowise before 3.0.6 (affected versions 2.2.8 and earlier) contains an arbitrary file acc...
[NEU] [mittel] Podman: Schwachstelle ermöglicht Offenlegung von Informationen
MaRisk schaffen mehr Spielraum für Banken
arXiv: The Unfireable Safety Kernel: Execution-Time AI Alignment for AI Agents and Other Escapable AI Systems
This paper, published on arXiv in June 2026, proposes a novel technical framework called the "Unfireable Safety Kernel" for ensuring AI alignment at execution time. It addresses a critical gap in c...
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arXiv: Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning
This paper, published on arXiv, introduces a new technical framework called "Detect, Unlearn, Restore" (DUR) designed to defend text summarization models against data poisoning attacks. Data poison...
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arXiv: Can Trustless Agents Be Trusted? An Empirical Study of the ERC-8004 Decentralized AI Agent Ecosystem
This paper, published on arXiv, presents an empirical study of the ERC-8004 decentralized AI agent ecosystem, focusing on the practical trustworthiness of so-called "trustless" agents. It does not ...
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arXiv: Privacy Vulnerabilities of Attention Layers in Tabular Foundation Models and Protection of High-Risk Queries
This paper, published on arXiv, presents a new privacy vulnerability specific to attention layers in tabular foundation models. It demonstrates that an attacker can infer sensitive attributes of hi...
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arXiv: BlowLive: Blow-Based Multi-Factor Biometrics with Liveness Detection and Revocability
A new research paper, BlowLive, has been published on arXiv proposing a biometric authentication system that uses breath patterns as a multi-factor identifier, combined with liveness detection and ...
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arXiv: Do (Not) Tell Me About My Insecurities: Assessing the Status Quo of Coordinated Vulnerability Disclosure in Ge...
This paper, published on arXiv, assesses the current state of Coordinated Vulnerability Disclosure (CVD) in Germany against the backdrop of new EU cybersecurity regulations, particularly the NIS2 D...
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arXiv: A Tattered Cloak of Invisibility: Measuring Anonymity Loss in Railgun on Ethereum
A new academic paper published on arXiv, titled "A Tattered Cloak of Invisibility: Measuring Anonymity Loss in Railgun on Ethereum," presents empirical analysis showing that the Railgun privacy pro...
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arXiv: The Web4 Agent Economy: A Large-Scale Empirical Study of the Landscape, Challenges, and Opportunities
This publication is not a regulatory change but a research paper from arXiv that provides a large-scale empirical study of the emerging "Web4 Agent Economy," where autonomous AI agents perform task...
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arXiv: Automated Detection of Configuration-Specific Security Vulnerabilities via Patch Analysis
A new research paper published on arXiv proposes a method for automatically detecting security vulnerabilities that are specific to particular software configurations, using patch analysis. The stu...
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arXiv: Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks
A new research paper published on arXiv, titled "Color Matters: Trigger Color Affects Success in Federated Backdoor Attacks," presents findings that could have significant implications for AI safet...
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arXiv: Can Machine Learning Break Wi-Fi Privacy? A Study on MAC Address Randomization