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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: Cordyceps: Covert Control Attacks on LLMs via Data Poisoning
arXiv: GradSentry: Gradient Spectral Entropy for Backdoor Sample Filtering in Large Language Model Fine-Tuning
arXiv: SEC-bench Pro: Can Language Models Solve Long-Horizon Software Security Tasks?
arXiv: ChainCaps: Composition-Safe Tool-Using Agents via Monotonic Capability Attenuation
arXiv: Open-Weight LLM Fine-Tuning Defenses are Susceptible to Simple Attacks
arXiv: Aligning Provenance with Authorization: A Dual-Graph Defense for LLM Agents
arXiv: Shielded but Lightweight: Building Practical Confidential Containers with ARM CCA
This paper, published on arXiv, presents a technical architecture for deploying confidential containers using ARM’s Confidential Compute Architecture (CCA). It proposes a method to run container wo...
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arXiv: Building an Adversarial Malware Dataset by Family and Type: Generation, Evasion, and Poisoning Evaluation
This publication, dated 25 May 2026, presents a new methodology for creating adversarial malware datasets, specifically designed to test the robustness of AI-based cybersecurity systems. The resear...
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arXiv: Semantic Validation of Packer Identification Tools: Characterization, Repair, and Downstream Impact
This publication, titled "Semantic Validation of Packer Identification Tools," presents a technical analysis of software tools used to detect packed executables—a common technique used by malware t...
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arXiv: Capability and Robustness Cannot Both Be Free: An Information-Theoretic Bound for Vision-Language-Action Models
A new preprint from arXiv, titled "Capability and Robustness Cannot Both Be Free," presents an information-theoretic bound for Vision-Language-Action (VLA) models, which are AI systems that combine...
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arXiv: How Agentic AI Coding Assistants Become the Attacker's Shell
A new preprint from arXiv, titled "How Agentic AI Coding Assistants Become the Attacker's Shell," published on 25 May 2026, presents a significant security analysis of advanced AI coding assistants...
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arXiv: Broken Object Level Authorization in the Wild: An Empirical Taxonomy from 100+ Bug Bounty Disclosures
This publication, a research paper from arXiv, does not represent a formal regulatory change but rather an empirical analysis of a critical security vulnerability pattern. The study examines over 1...
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arXiv: Proof of Useful Attestation: A Consensus Primitive for Attestation-Native Chains
This publication introduces a new consensus mechanism called Proof of Useful Attestation, designed for blockchain networks that prioritize verification of AI-generated content and data integrity. T...
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arXiv: TTPrint: Evidence-Grounded TTP Extraction via Diverge-then-Converge Verification
**Summary for Compliance Professionals** A new research paper, TTPrint, has been published on arXiv proposing a method for extracting Tactics, Techniques, and Procedures (TTPs) from threat intelli...
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arXiv: "What is the Problem Space?" Defining Host-space Adversarial Perturbations against Network Intrusion Detection...
This publication, dated May 25, 2026, introduces a new category of cybersecurity vulnerability specifically targeting Network Intrusion Detection Systems (NIDS). The paper defines "host-space adver...
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arXiv: On Reliability of Efficient Membership Inference Vulnerability Evaluation
This paper, published on arXiv, presents a new evaluation framework for assessing the vulnerability of machine learning models to membership inference attacks. These attacks attempt to determine wh...
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arXiv: SAMark: A Self-Anchored Text Watermarking with Paragraph-Level Paraphrase Robustness
arXiv: Efficient and Privacy-Preserving Distribution Statistics Analytics on Mobile Spatial Data
arXiv: An Efficient and Privacy-Preserving Architecture for Cross-Institutional Collaborative RAG
arXiv: Ecosystem-Driven Privacy Exposure in Mobile Gaming Apps: A Configuration-Aware Empirical Analysis