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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
Hide and Seek -- Preserving Location Privacy and Utility ...
Alessandro Brighente, Mauro Conti, Savio Sciancalepore · 2022-05-27 · via cs.CR updates on arXiv.org

Due to the frequent unauthorized access by commercial drones to Critical Infrastructures (CIs) such as airports and oil refineries, the US-based Federal Avionics Administration (FAA) recently published a new specification, namely RemoteID. The aforementioned rule mandates that all Unmanned Aerial Vehicles (UAVs) have to broadcast information about their identity and location wirelessly to allow for immediate invasion attribution. However, the enforcement of such a rule poses severe concerns on UAV operators, especially in terms of location privacy and tracking threats, to name a few. Indeed, by simply eavesdropping on the wireless channel, an adversary could know the precise location of the UAV and track it, as well as obtaining sensitive information on path source and destination of the UAV. In this paper, we investigate the trade-off between location privacy and data utility that can be provided to UAVs when obfuscating the broadcasted location through differential privacy techniques. Leveraging the concept of Geo-Indistinguishability (Geo-Ind), already adopted in the context of Location-Based Services (LBS), we show that it is possible to enhance the privacy of the UAVs without preventing CI operators to timely detect unauthorized invasions. In particular, our experiments showed that when the location of an UAV is obfuscated with an average distance of 1.959 km, a carefully designed UAV detection system can detect 97.9% of invasions, with an average detection delay of 303.97 msec. The UAVs have to trade-off such enhanced location privacy with a non-negligible probability of false positives, i.e., being detected as invading while not really invading the no-fly zone. UAVs and CI operators can solve such ambiguous situations later on through the help of the FAA, being this latter the only one that can unveil the actual location of the UAV.