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AI_SAFETY

EU Regulatory Changes

1476 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: Emerging Challenges in Threat Modeling for GenAI-Augmented Systems: A View from the Trenches
This publication is a research paper from arXiv, not a binding regulatory change, but it offers critical guidance for compliance teams navigating the emerging field of generative AI. The paper exam...
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arXiv: Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata
The publication introduces a new technical framework, Encryption-Compatible Clustered Federated Learning via Distributed Expectation-Maximization over Metadata. This is a research paper, not a regu...
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arXiv: Demystifying DRAM Read Disturbance: Bridging the Gap Between Experimental Characterization and Device-Level Mo...
This publication is a technical research paper, not a regulatory change, but it has significant compliance implications for hardware security. The paper presents a new framework for understanding a...
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arXiv: Security of World-Model-Based Embodied AI: A Lifecycle of Threats, Defenses, and Evaluation
A new academic paper, published on arXiv, provides a comprehensive lifecycle analysis of security threats and defenses for world-model-based embodied AI systems. This is not a new regulation, but a...
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arXiv: Secure Aggregation for Privacy-Preserving Federated Learning on Clinical EEG Data
A new research paper proposes a technical framework for applying secure aggregation to federated learning models trained on clinical electroencephalogram (EEG) data. Federated learning allows multi...
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arXiv: Technology-Enhanced Tabletop Exercises for Cybersecurity Education: Lessons Learned
A new research paper, published on arXiv, explores the use of technology-enhanced tabletop exercises for cybersecurity education, with direct implications for AI safety compliance. The study presen...
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arXiv: Piggybacking on Perception: Stealthy Concurrent Audio Prompt Injections against Multimodal LLM Agents
arXiv: Agent Harness Distillation: Inference-Time Harness Extraction and Exploitation in Autonomous Multi-Agent Systems
arXiv: Checking Information Flow in Cloud-based IoT Access Control Policies (Extended Version)
arXiv: Temporal Poisoning: Clean-Label Backdoors via Event Redistribution in SNNs
arXiv: Driving up Inference Energy on SNNs: Per-Sample and Universal Sponge Attacks
arXiv: Safeguards Based on Copyable Context Cannot Provide Reliable Safety for LLMs
arXiv: Benign on Label, Malicious by Design: Clean-Label Dormant-to-Activated Backdoor via Machine Unlearning with Re...
arXiv: Don't Trust the AI Ecosystem: Analyzing Privacy Leakage in Compromised Open-Source Components
arXiv: Adaptive Security at the Edge for 6G-Enabled Healthcare IoT
arXiv: CHARGE: Leveraging CWE Hierarchies for Hardware Security SystemVerilog Assertion Generation
arXiv: Distributed Point Functions and Function Secret Sharing
arXiv: Strategy Phasing of Cyber Attacks on Digital Substations
arXiv: Revisiting the Adversarial Robustness of Graph-Based Traffic Forecasting
arXiv: AnchorMark: Robust Diffusion Watermarking via Latent-Space Rotation Synchrony