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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
A Biomechatronic Approach to Evaluating the Security of W...
Yas Vaseghi, Behnaz Behara, Mehdi Delrobaei · 2024-06-21 · via cs.CR updates on arXiv.org

The Internet of Medical Things (IoMT) has the potential to revolutionize healthcare by reducing human error and improving patient health. For instance, wearable smart infusion pumps can accurately administer medication and integrate with electronic health records. These pumps can alert healthcare professionals or remote servers when an operation fails, preventing distressing incidents. However, as the number of connected medical devices increases, so does the risk of cyber threats. Wearable medication devices based on IoT attached to patients' bodies are particularly vulnerable to significant cyber threats. Since they are connected to the internet, these devices can be exposed to potential harm, which can disrupt or degrade device performance and harm patients. Therefore, it is crucial to establish secure data authentication for internet-connected medical devices to ensure patient safety and well-being. It is also important to note that the wearability option of such devices might downgrade the computational resources, making them more susceptible to security risks. We propose implementing a security approach for a wearable infusion pump to mitigate cyber threats. We evaluated the proposed architecture with 20, 50, and 100 users for 10 minutes and repeated the evaluation 10 times with two infusion settings, each repeated five times. The desired volumes and rates for the two settings were 2 ml and 4 ml/hr and 5 ml and 5 ml/hr, respectively. The maximum error in infusion rate was measured to be 2.5%. We discuss the practical challenges of implementing such a security-enabled device and suggest initial solutions.