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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
A Comprehensive Study of the GeoPass User Authentication ...
2014-08-13 · via cs.CR updates on arXiv.org

Before deploying a new user authentication scheme, it is critical to subject the scheme to comprehensive study. Few works, however, have undertaken such a study. Recently, Thorpe et al. proposed GeoPass, the most promising of a class of user authentication schemes based on geographic locations in online maps. Their study showed very high memorability (97%) and satisfactory resilience against online guessing, which means that GeoPass has compelling features for real-world use. No comprehensive study, however, has been conducted for GeoPass or any other location-based password scheme. In this paper, we present a systematic approach for the detailed evaluation of a password system, which we implement to study GeoPass. We conducted three separate studies to evaluate the suitability of GeoPass for widespread use. First, we performed a field study over two months, in which users in a real-world setting remembered their location-passwords 96% of the time and showed improvement with more login sessions. Second, we conducted a study to test how users would fare with multiple location-passwords and found that users remembered their location-passwords in less than 70% of login sessions, with 40% of login failures due to interference effects. Third, we conducted a study to examine the resilience of GeoPass against shoulder surfing. Our participants played the role of attackers and had an overall success rate of 48%. Based on our results, we suggest suitable applications of GeoPass in its current state and identify aspects of GeoPass that must be improved before widespread deployment could be considered.