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cs.CR updates on arXiv.org

Agentic Vulnerability Reasoning on Windows COM Binaries From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists Undetectable Backdoors in Model Parameters: Hiding Sparse Secrets in High Dimensions When Embedding-Based Defenses Fail: Rethinking Safety in LLM-Based Multi-Agent Systems Token-Efficient Change Detection in LLM APIs Selfie-Capture Dynamics as an Auxiliary Signal Against Deepfakes and Injection Attacks for Mobile Identity Verification Trident: Improving Malware Detection with LLMs and Behavioral Features When Alignment Isn't Enough: Response-Path Attacks on LLM Agents RefusalGuard: Geometry-Preserving Fine-Tuning for Safety in LLMs Checkerboard: A Simple, Effective, Efficient and Learning-free Clean Label Backdoor Attack with Low Poisoning Budget Block-wise Codeword Embedding for Reliable Multi-bit Text Watermarking Secret Stealing Attacks on Local LLM Fine-Tuning through Supply-Chain Model Code Backdoors Enhancing Linux Privilege Escalation Attack Capabilities of Local LLM Agents Defusing the Trigger: Plug-and-Play Defense for Backdoored LLMs via Tail-Risk Intrinsic Geometric Smoothing Evaluating Jailbreaking Vulnerabilities in LLMs Deployed as Assistants for Smart Grid Operations: A Benchmark Against NERC Standards Behavioral Canaries: Auditing Private Retrieved Context Usage in RL Fine-Tuning FlexServe: A Fast and Secure LLM Serving System for Mobile Devices with Flexible Resource Isolation Breaking MCP with Function Hijacking Attacks: Novel Threats for Function Calling and Agentic Models Text Steganography with Dynamic Codebook and Multimodal Large Language Model An AI Agent Execution Environment to Safeguard User Data TwoHamsters: Benchmarking Multi-Concept Compositional Unsafety in Text-to-Image Models Fundamental Limitations of Favorable Privacy-Utility Guarantees for DP-SGD Symbolic Guardrails for Domain-Specific Agents: Stronger Safety and Security Guarantees Without Sacrificing Utility Hardening x402: PII-Safe Agentic Payments via Pre-Execution Metadata Filtering QShield: Securing Neural Networks Against Adversarial Attacks using Quantum Circuits Hijacking Text Heritage: Hiding the Human Signature through Homoglyphic Substitution Like a Hammer, It Can Build, It Can Break: Large Language Model Uses, Perceptions, and Adoption in Cybersecurity Operations on Reddit Private Seeds, Public LLMs: Realistic and Privacy-Preserving Synthetic Data Generation One Word at a Time: Incremental Completion Decomposition Breaks LLM Safety Measuring and Exploiting Contextual Bias in LLM-Assisted Security Code Review
Expert opinions on making GDPR usable
Johanna Johansen · 2023-08-16 · via cs.CR updates on arXiv.org

We present the results of a study done in order to validate concepts and methods that have been introduced in (Johansen and Fischer-Hubner, 2020. "Making GDPR Usable: A Model to Support Usability Evaluations of Privacy." in IFIP AICT 576, 275-291). We use as respondents in our interviews experts working across fields of relevance to these concepts, including law and data protection/privacy, certifications and standardization, and usability (as studied in the field of Human-Computer Interaction). We study the experts' opinions about four new concepts, namely: (i) a definition of Usable Privacy, (ii) 30 Usable Privacy Goals identified as excerpts from the GDPR (European General Data Protection Regulation), (iii) a set of 25 corresponding Usable Privacy Criteria together with their multiple measurable sub-criteria, and (iv) the Usable Privacy Cube model, which puts all these together with the EuroPriSe certification criteria, with the purpose of making explicit several aspects of certification processes such as orderings of criteria, interactions between these, different stakeholder perspectives, and context of use/processing. The expert opinions are varied, example-rich, and forward-looking, which gives a impressive list of open problems where the above four concepts can work as a foundation for further developments. We employed a critical qualitative research, using theory triangulation to analyze the data representing three groups of experts, categorized as 'certifications', 'law', and 'usability', coming both from industry and academia. The results of our analysis show agreement among the experts about the need for evaluations and measuring of usability of privacy in order to allow for exercising data subjects' rights and to evaluate the degree to which data controllers comply with the data protection principles.