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All Changes

EU Regulatory Changes

4742 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
CVE-2026-44761 (CVSS 9.1) — SAP Commerce Cloud could retain a sample OAuth2 client with publicly documented sample cr...
KEV: CVE-2008-4128 — Cisco IOS (Cisco IOS Cross-Site Request Forgery Vulnerability)
CVE-2026-40468 (CVSS 9.1) — Integer overflow vulnerability has been found in "builtin.c" program file of gawk. This i...
CVE-2026-40469 (CVSS 9.1) — Integer overflow vulnerability has been found in "builtin.c" program file of gawk (do_sub...
CVE-2026-61498 (CVSS 9.8) — Vitec Flamingo 4.12.2 contains an unauthenticated OS command injection vulnerability in t...
CVE-2026-61500 (CVSS 9.8) — Rejetto HFS 3.0.0 through 3.2.0 derives its session-cookie signing key from the non-crypt...
CVE-2026-59801 (CVSS 9.8) — 9Router through version 0.4.41 contains an unauthenticated access vulnerability that allo...
CVE-2026-62327 (CVSS 9.1) — 9Router through version 0.4.41 contain an unauthenticated information disclosure vulnerab...
CVE-2026-27690 (CVSS 9.1) — Due to an HTTP Request Smuggling vulnerability in SAP Approuter, an unauthenticated attac...
CELEX:32024R3110R(02)
arXiv: Federated Learning Architecture: Data Privacy and System Security Approaches
A new academic paper published on arXiv proposes a federated learning architecture designed to enhance data privacy and system security. While not a regulatory change itself, this publication signa...
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arXiv: When Routes Run Out: Adversarial Co-Learning and Explainable Robustness in Quantum Repeater Networks
This publication from arXiv presents a technical research paper on adversarial co-learning and explainable robustness in quantum repeater networks, which are critical infrastructure components for ...
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arXiv: SFDS: Selective File Disclosure System
This paper, published on arXiv, introduces a technical framework called the Selective File Disclosure System (SFDS), which is designed to allow AI developers to share specific parts of their traini...
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arXiv: Leveraging Interpretable Tsetlin Machine for PDF Malware Detection
A new research paper published on arXiv proposes the use of an interpretable Tsetlin Machine for detecting malware in PDF files. This is not a regulatory change but a technical development in AI-ba...
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arXiv: Blockchain-Linked Auditable Decision Management for Telecom/IoT Fraud-Control Requests
This publication, titled "Blockchain-Linked Auditable Decision Management for Telecom/IoT Fraud-Control Requests," presents a novel framework that integrates blockchain technology with AI-driven de...
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arXiv: Malaika: Understanding Malware through Tri-Grounded Agentic Reasoning
This publication introduces Malaika, a novel AI framework designed to improve malware analysis through a tri-grounded agentic reasoning approach. The paper details how this system uses three distin...
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arXiv: SherAgent: Scaling Attack Investigation in the Wild via LLM-Empowered Iterative Query-Filter Backtracking
This paper, published on arXiv, introduces SherAgent, a novel framework that uses large language models to automate the investigation of cyberattacks. It proposes an iterative query-filter backtrac...
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arXiv: Event Burst Trigger: An Availability Backdoor Attack on Event-Based SNN Object Detection
A new research paper published on arXiv on July 10, 2026, titled "Event Burst Trigger: An Availability Backdoor Attack on Event-Based SNN Object Detection," identifies a novel vulnerability in even...
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arXiv: Privacy Detective: A Narrative Game that Cultivates Student Developers' Privacy Awareness by Harnessing Legal ...
This paper, published on arXiv, introduces a narrative game called Privacy Detective, designed to help student developers build privacy awareness by engaging directly with legal documents like GDPR...
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arXiv: SLBench: Evaluating How LLM Agents Follow Logical Relations in Skills
This paper, SLBench, published on arXiv, introduces a new benchmark for evaluating how large language model (LLM) agents follow logical relationships when executing multi-step tasks. It is not a re...
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