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AI_SAFETY

EU Regulatory Changes

571 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: Pretrained, Frozen, Still Leaking: Auditing Cross-Encoder Attribute Transfer in EEG Foundation Models
This paper, published on arXiv, presents a security audit of foundation models used for electroencephalography (EEG) data. The researchers demonstrate that even when an EEG model is "frozen" (its p...
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arXiv: EnclaveScale: Hardware-Assisted Edge-DP for Secure Data Centre Power Telemetry
This publication introduces EnclaveScale, a hardware-assisted framework designed to enable differential privacy for power telemetry data in data centres. The paper proposes using trusted execution ...
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arXiv: Customization under Fire: Plugin Poisoning in Text-to-Image Ecosystem
A new research paper, titled "Customization under Fire: Plugin Poisoning in Text-to-Image Ecosystem," has been published on arXiv, highlighting a significant security vulnerability in AI-driven tex...
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arXiv: PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees
This paper, PrivCode++: Latent-Conditioned Differentially Private Code Generation for Comprehensive Guarantees, published on arXiv, introduces a new technical framework for generating code with for...
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arXiv: Steganography Without Modification: Hidden Communication via LLM Seeds
This paper, published on arXiv, introduces a novel steganography technique that embeds hidden messages within the outputs of large language models without altering the generated text itself. Instea...
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arXiv: Unveiling Privacy Risks in Multi-modal Large Language Models: Task-specific Vulnerabilities and Mitigation Cha...
This publication is a pre-print research paper from arXiv, not a regulatory change. It analyzes privacy vulnerabilities in multi-modal large language models (MLLMs) that process text, images, and a...
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arXiv: Context-Fractured Decomposition Attacks on Tool-Using LLM Agents: Exploiting Artifact Provenance Gaps
This paper, published on arXiv, identifies a novel vulnerability in large language model agents that use external tools, such as code interpreters or file systems. The attack, called Context-Fractu...
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arXiv: Human-Centred Risk Mitigation for AI-Mediated Information Manipulation: A SOCMINT Framework Based on Informati...
arXiv: A Bell-State Extension of Loop-Back Quantum Key Distribution
arXiv: What the Eyes See, the LLMs Miss: Exploiting Human Perception for Adversarial Text Attacks
arXiv: Observability for Delegated Execution in Agentic AI Systems
arXiv: Parent-Hash DAG: A Cost Analysis of Constant-Time Append for On-Chain Registries
arXiv: Clinically Grounded Privacy Evaluation of Medical LMs
arXiv: Safe-RULE: Safe Reinforcement UnLEarning
arXiv: FuseFSS: Efficient Secure LLM Inference with Function Secret Sharing
arXiv: SecureClaw: Clawing Back Control of LLM Agents
arXiv: Model Poisoning Against Federated Model Adaptation with Chain of Bit-Flips
arXiv: Targeting World Models to Compromise Robot Learning Pipelines
arXiv: Now You (Still) See Me: Detecting Evasive Steganographic Payloads in LLMs
arXiv: Fully Oblivious Differential Privacy for Frequency Estimation in the Augmented Shuffle Model with Trusted Proc...