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AI_SAFETY

EU Regulatory Changes

1649 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: A Non-Line-of-Sight, Multi-Modality-based Side-Channel IP Theft Attack on Additive Manufacturing Using Dual Sm...
arXiv: Semantic Leakage and Privacy Preservation in Relay-Assisted Semantic Communications
arXiv: Robocalls: A Worldwide or US-only Problem? Analyzing Spam and Fraud in International Phone Calls
arXiv: All-out Attack: Optimal Block Withholding Under Pay-Per-Share Scheme
arXiv: Detecting Adversarial Evasion Attacks Against Autoencoder-Based Network Intrusion Detection Systems
arXiv: Antaeus: Hunting Repository-Level Logic Vulnerabilities via Context-Grounded LLM Reasoning
arXiv: Toward a Unified Security and Privacy Framework for AI-Native 6G Networks
arXiv: The Binary Tree Mechanism is Optimal for Approximate Differentially Private Continual Counting
arXiv: No Country for Old Privacy: The Evolving Challenges of Anonymity in Bitcoin
arXiv: Forensic-Oriented Intrusion Detection Using Synthetic Network Traffic Data and Explainable Artificial Intellig...
arXiv: SessionBound: Turning Enterprise Task Approval into Budgeted Database Sessions
arXiv: Know Thy Neighbor: Cross-TEE Mutual Attestation
arXiv: Proofs of Ownership for Machine Learning Models
This publication from arXiv introduces a technical framework for establishing proof of ownership for machine learning models, addressing a critical gap in AI governance. The paper proposes cryptogr...
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arXiv: Your Space is My Zone: Demystifying the Security Risks of AI-Powered Applications on Pre-Trained Model Hubs
This publication, "Your Space is My Zone: Demystifying the Security Risks of AI-Powered Applications on Pre-Trained Model Hubs," is a research paper from arXiv that identifies critical security vul...
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arXiv: Quantum Lazy Sampling and Path Recording for Any Group
This publication introduces a novel computational method called Quantum Lazy Sampling and Path Recording for Any Group, which proposes a framework for more efficient quantum algorithm design. While...
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arXiv: Defending Against Harmful Supervision Hidden in Benign Samples
As a senior EU regulatory compliance analyst, I provide the following summary of this publication for compliance professionals. This paper, published on arXiv, introduces a novel vulnerability in ...
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arXiv: Robust secret storage in networks
This is a technical research paper published on arXiv, not a regulatory change. It proposes a new cryptographic method for robust secret storage across distributed networks, focusing on resilience ...
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arXiv: The Spectrum Strikes Back: Infrared POV Attacks on Traffic Sign Classification
A new preprint from arXiv, titled "The Spectrum Strikes Back: Infrared POV Attacks on Traffic Sign Classification," published on June 29, 2026, demonstrates a novel adversarial attack method that e...
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arXiv: On the Internet, Nobody Knows You're an LLM Bot: Unmasking Web Agents with Multi-Layer Fingerprinting
This paper, published on arXiv, introduces a new method for detecting AI-powered web bots, specifically large language model agents, by using multi-layer fingerprinting. The research demonstrates t...
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arXiv: Discard the Dross and Select the Essential: Pre-query Sample Selection for Black-box Membership Inference Attacks
This paper, published on arXiv, presents a new method for conducting membership inference attacks against machine learning models. Membership inference attacks attempt to determine whether a specif...
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