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cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
Extended- Force vs Nudge : Comparing Users' Pattern Choic...
Harshal Tupsamudre, Sukanya Vaddepalli, Vijayanand Banahatti, Sa · 2019-12-10 · via cs.CR updates on arXiv.org

Android's 3X3 graphical pattern lock scheme is one of the widely used authentication method on smartphone devices. However, users choose 3X3 patterns from a small subspace of all possible 389,112 patterns. The two recently proposed interfaces, SysPal by Cho et al. and TinPal by the authors, demonstrate that it is possible to influence users 3X3 pattern choices by making small modifications in the existing interface. While SysPal forces users to include one, two or three system-assigned random dots in their pattern, TinPal employs a highlighting mechanism to inform users about the set of reachable dots from the current selected dot. Both interfaces improved the security of 3X3 patterns without affecting usability, but no comparison between SysPal and TinPal was presented. To address this gap, we conduct a new user study with 147 participants and collect patterns on three SysPal interfaces, 1-dot, 2-dot and 3-dot. We also consider original and TinPal patterns collected in our previous user study involving 99 participants. We compare patterns created on five different interfaces, original, TinPal, 1-dot, 2-dot and 3-dot using a range of security and usability metrics including pattern length, stroke length, guessability, recall time and login attempts. Our study results show that participants in the TinPal group created significantly longer and complex patterns than participants in the other four groups. Consequently, the guessing resistance of TinPal patterns was the highest among all groups. Further, we did not find any significant difference in memorability of patterns created in the TinPal group and the other groups.