AI_SAFETY
EU Regulatory Changes
1501 changes tracked across 24 compliance frameworks including DORA, NIS2, GDPR, EU AI Act, Cyber Resilience Act, and more.
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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
This paper, published on arXiv, introduces a new benchmark called Adaptive Adversaries designed to test the security of large language model (LLM) agents. Unlike previous single-turn tests, this be...
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This paper, published on arXiv, proposes a new technical framework for generating synthetic data that is specifically designed to preserve privacy while maintaining domain-specific utility. It intr...
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A new research paper published on arXiv on July 20, 2026, titled "Self-State Attacks on Self-Hosted AI Agents: How Far Can OS Defenses Go?" examines vulnerabilities in self-hosted AI agents where a...
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This publication introduces a new technical framework, RT-SHCUA, which enables real-time, self-hosted control of unmanned aerial vehicles (UAVs) through an artificial intelligence agent. The system...
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This publication introduces a novel theoretical framework for defending against side-channel attacks on encrypted network traffic, using rate-distortion theory to balance privacy protection with da...
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This paper, published on arXiv, presents a new method for detecting cyberattacks on encrypted OPC UA traffic, a common industrial communication protocol. It demonstrates that even when traffic is e...
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This paper, published on arXiv on July 20, 2026, presents a technical analysis of vulnerabilities in Vision-Language-Action (VLA) models, which are AI systems that process visual and language input...
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arXiv: Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-based Code Generation
A new preprint from arXiv, titled "Insecure Coding Preferences in Long-Term Memory: Security Risks for LLM-based Code Generation," published on 20 July 2026, presents evidence that large language m...
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This publication from arXiv presents a new technique for obfuscating floating-point computations within deep neural network (DNN) binaries using Mixed Boolean-Arithmetic (MBA) obfuscation. The pape...
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