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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
Personalized Guidelines for Design, Implementation and Ev...
Orvila Sarker, Sherif Haggag, Asangi Jayatilaka, Chelsea Liu · 2023-10-01 · via cs.CR updates on arXiv.org

Background: Current anti-phishing interventions, which typically involve one-size-fits-all solutions, suffer from limitations such as inadequate usability and poor implementation. Human-centric challenges in anti-phishing technologies remain little understood. Research shows a deficiency in the comprehension of end-user preferences, mental states, and cognitive requirements by developers and practitioners involved in the design, implementation, and evaluation of anti-phishing interventions. Aims: This study addresses the current lack of resources and guidelines for the design, implementation and evaluation of anti-phishing interventions, by presenting personalized guidelines to the developers and practitioners. Method: Through an analysis of 53 academic studies and 16 items of grey literature studies, we systematically identified the challenges and recommendations within the anti-phishing interventions, across different practitioner groups and intervention types. Results: We identified 22 dominant factors at the individual, technical, and organizational levels, that affected the effectiveness of anti-phishing interventions and, accordingly, reported 41 guidelines based on the suggestions and recommendations provided in the studies to improve the outcome of anti-phishing interventions. Conclusions: Our dominant factors can help developers and practitioners enhance their understanding of human-centric, technical and organizational issues in anti-phishing interventions. Our customized guidelines can empower developers and practitioners to counteract phishing attacks.