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AI_SAFETY

EU Regulatory Changes

396 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: SPARD: Defending Harmful Fine-Tuning Attack via Safety Projection with Relevance-Diversity Data Selection
arXiv: Can It Reach the Generator? Investigating the Survival of Prompt-Injection Attacks in Realistic RAG Settings
arXiv: An Empirical Audit of k-NAF Budget Accounting for Anchored Decoding
arXiv: When Think-with-Image Meets Safety: What Determines Multimodal Jailbreak Robustness?
arXiv: Privately Estimating Monotone Statistics in Polynomial Time
arXiv: Symmetry Defeats Auditing
arXiv: Shortest Path Problem with Subnormal Gaussian Fuzzy Costs
This publication, titled "Shortest Path Problem with Subnormal Gaussian Fuzzy Costs," is a theoretical computer science paper from arXiv, not a regulatory change. It proposes a new mathematical mod...
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arXiv: Risk Averse Alert Prioritization for IDS Using Subnormal Gaussian Fuzzy Models
This publication introduces a novel methodology for prioritizing cybersecurity alerts generated by Intrusion Detection Systems (IDS) using a mathematical approach called Subnormal Gaussian Fuzzy Mo...
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arXiv: Landseer: Exploring the Machine Learning Defense Landscape
This publication, titled Landseer: Exploring the Machine Learning Defense Landscape, is a technical research paper from arXiv that maps current adversarial attack and defense methods for machine le...
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arXiv: Do Modern Post-Hoc Watermarking Methods Beat Broken-Arrows?
A new preprint from arXiv, titled "Do Modern Post-Hoc Watermarking Methods Beat Broken-Arrows?" published on May 26, 2026, evaluates the robustness of current AI-generated content watermarking tech...
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arXiv: BAIT: Boundary-Guided Disclosure Escalation via Self-Conditioned Reasoning
This paper, published on arXiv, introduces BAIT, a new technical framework for improving the safety of large language models (AI systems). BAIT stands for Boundary-Guided Disclosure Escalation via ...
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arXiv: On the Hidden Costs of Counterfactual Knowledge Training in LLM Unlearning
This paper, published on arXiv, presents research on a hidden cost associated with a specific technique used to make large language models (LLMs) forget or "unlearn" problematic data, such as copyr...
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arXiv: Lessons from Penetration Tests on Large-Scale Agent Systems
A new research paper, "Lessons from Penetration Tests on Large-Scale Agent Systems," has been published on arXiv, detailing systematic security vulnerabilities found in autonomous AI agent systems....
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arXiv: Prompt Injection Detection is Regime-Dependent: A Deployment-Aware Evaluation with Interpretable Structural Si...
This paper, published on arXiv, presents a new evaluation framework for detecting prompt injection attacks against large language models. The key finding is that no single detection method works un...
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arXiv: The Fault in Our Drafts: Vulnerabilities in RPKI Specification and Software
A new academic paper published on arXiv, titled "The Fault in Our Drafts: Vulnerabilities in RPKI Specification and Software," has identified critical security flaws in the Resource Public Key Infr...
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arXiv: Practical Anonymous Two-Party Gradient Boosting Decision Tree
arXiv: Privacy-Preserving Screening for Record Linkage
arXiv: Secure UAV Swarms in Low-Altitude Wireless Networks: Challenges and Solutions
arXiv: Anonymous YARA Rules Are Not Anonymous
arXiv: Cordon-MAS: Defending RAG against Knowledge Poisoning via Information-Flow Control