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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
A Study of the Feasibility of Co-located App Attacks agai...
Pallavi Sivakumaran, Jorge Blasco · 2018-08-11 · via cs.CR updates on arXiv.org

Bluetooth Low Energy (BLE) is a fast-growing wireless technology with a large number of potential use cases, particularly in the IoT domain. Increasingly, these use cases require the storage of sensitive user data or critical device controls on the BLE device, as well as the access of this data by an augmentative mobile application. Uncontrolled access to such data could violate user privacy, cause a device to malfunction, or even endanger lives. The BLE standard provides security mechanisms such as pairing and bonding to protect sensitive data such that only authenticated devices can access it. In this paper we show how unauthorized co-located Android applications can access pairing-protected BLE data, without the user's knowledge. We discuss mitigation strategies in terms of the various stakeholders involved in this ecosystem, and argue that at present, the only possible option for securing BLE data is for BLE developers to implement remedial measures in the form of application-layer security between the BLE device and the Android application. We introduce BLECryptracer, a tool for identifying the presence of such application-layer security, and present the results of a large-scale static analysis over 18,900+ BLE-enabled Android applications. Our findings indicate that over 45% of these applications do not implement measures to protect BLE data, and that cryptography is sometimes applied incorrectly in those that do. This implies that a potentially large number of corresponding BLE peripheral devices are vulnerable to unauthorized data access.