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All Changes

EU Regulatory Changes

4330 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: aurora claims GILDE Handwerk Macrander GmbH & Co. KG (DE) — Manufacturing
Ransomware: aurora claims US Installation Group, Inc. (US) — Manufacturing
CVE-2026-61515 (CVSS 9.8) — Puwell IP Camera firmware versions 2.x through 4.x contains an unauthenticated command in...
CVE-2026-69098 (CVSS 9.8) — kotaemon through 0.12.0 contains an insecure deserialization vulnerability in the check_c...
CVE-2026-70552 (CVSS 9.8) — MaxSite CMS 109.5 and earlier contains an authentication bypass vulnerability in the AJAX...
CVE-2026-70553 (CVSS 9.8) — MaxSite CMS contains a remote code execution vulnerability that allows unauthenticated at...
CVE-2026-70554 (CVSS 9.8) — MaxSite CMS contains a PHP object injection vulnerability that allows unauthenticated att...
arXiv: Solving the Shortest Vector Problem in time $2^{0.6039n}$ Time via Mid-point Hessian
A new academic paper, published on arXiv on August 3, 2026, claims a major algorithmic breakthrough in lattice-based cryptography, specifically solving the Shortest Vector Problem (SVP) in time 2^(...
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arXiv: Agentic Incident Response through Digital Twin-Enhanced Multiscale Planning
A new research paper, published on arXiv, proposes a technical framework for using AI agents to manage incident response, specifically by creating digital twins of critical infrastructure to run mu...
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arXiv: Antares: Foundation Models for Agentic Vulnerability Localization
A new research paper, Antares: Foundation Models for Agentic Vulnerability Localization, has been published on arXiv, presenting a novel AI system designed to automatically identify and locate soft...
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arXiv: Self-Supervised Representations for Binary Program Clustering: From Empirical Study to Retrieval-Augmented Lea...
This publication introduces a new machine learning technique that groups similar compiled software programs, known as binaries, by analyzing their underlying structure without needing human-labeled...
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arXiv: Lost in Permissions: Exploring the Microsoft 365 App Ecosystem
A new academic study, published on arXiv in August 2026, examines the security and privacy risks within the Microsoft 365 app ecosystem, specifically focusing on how third-party applications reques...
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arXiv: TrainShield: Targeted Awareness for Cybersecurity Training
TrainShield is a newly published research paper proposing a targeted cybersecurity training framework that uses AI to personalize awareness programs based on an individual’s actual risk exposure an...
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arXiv: A Multi-Objective AutoML-based Efficient Intrusion Detection System for EV Charging Networks
A new research paper proposes a machine learning framework for detecting cyber intrusions in electric vehicle charging networks. The study introduces an automated, multi-objective system that optim...
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arXiv: TurboRetry: Mitigating Large-Scale QUIC Handshake Floods with Off-the-Shelf DPU Offloading
This publication introduces TurboRetry, a technical framework designed to mitigate large-scale QUIC handshake floods by leveraging off-the-shelf Data Processing Units (DPUs). It is not a regulatory...
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arXiv: Measuring Post-Quantum TLS Deployment Across UK Internet Sectors
A new research paper, published on arXiv, measures the real-world deployment of post-quantum cryptography (PQC) in TLS connections across UK internet sectors. This is not a new regulation or legal ...
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arXiv: Vulnerability Detection in AArch64 Machine Code Using a Digital Twin
A new research paper, published on arXiv on August 3, 2026, introduces a digital twin framework for detecting vulnerabilities in AArch64 machine code. This is not a regulatory mandate or legislativ...
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arXiv: Pretraining on Call Graphs: When Binary Analysis Tasks Profit From Context
arXiv: D-MUTRA: DLT-based MUTual Remote Attestation for Multi-Agent Systems
arXiv: Diagnosing High-Performance BFT Consensus via Mixture Modeling of Block Time Distributions