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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
Feasibility Study on CCTV-aware Routing and Navigation fo...
Tuomo Lahtinen, Lauri Sintonen, Hannu Turtiainen, Andrei Costin · 2020-11-17 · via cs.CR updates on arXiv.org

In order to withstand the ever-increasing invasion of privacy by CCTV cameras and technologies, on par CCTV-aware solutions must exist that provide privacy, safety, and cybersecurity features. We argue that a first important step towards such CCTV-aware solutions must be a mapping system that provides both privacy and safety routing and navigation options. To the best of our knowledge, there are no mapping nor navigation systems that support privacy and safety routing options. In this paper, we explore the feasibility of a CCTV-aware routing and navigation solution. The aim of this feasibility exploration is to understand what are the main impacts of CCTV on privacy, and what are the challenges and benefits to building such technology. We evaluate our approach on seven (7) pedestrian walking routes within the downtown area of the city of Jyvaskyla, Finland. We first map a total of 450 CCTV cameras, and then experiment with routing and navigation under several different configurations to coarsely model the possible cameras' parameters and coverage from the real-world. We report two main results. First, our preliminary findings support the overall feasibility of our approach. Second, the results also reveal a data-driven worrying reality for persons wishing to preserve their privacy/anonymity as their main living choice. When modelling cameras at their low performance end, a privacy-preserving route has on average a 1.5x distance increase when compared to generic routing. When modelling cameras at their medium-to-high performance end, a privacy-preserving route has on average a 5.0x distance increase, while in some cases there are no privacy-preserving routes possible at all. These results further support and encourage both global mapping of CCTV cameras and refinements to camera modelling and underlying technology.