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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
BLURtooth: Exploiting Cross-Transport Key Derivation in B...
Daniele Antonioli, Nils Ole Tippenhauer, Kasper Rasmussen, Mathi · 2020-09-25 · via cs.CR updates on arXiv.org

The Bluetooth standard specifies two transports: Bluetooth Classic (BT) for high-throughput wireless services and Bluetooth Low Energy (BLE) for very low-power scenarios. BT and BLE have dedicated pairing protocols and devices have to pair over BT and BLE to use both securely. In 2014, the Bluetooth standard (v4.2) addressed this usability issue by introducing Cross-Transport Key Derivation (CTKD). CTKD allows establishing BT and BLE pairing keys just by pairing over one of the two transports. While CTKD crosses the security boundary between BT and BLE, little is known about the internals of CTKD and its security implications. In this work, we present the first complete description of CTKD obtained by merging the scattered information from the Bluetooth standard with the results from our reverse-engineering experiments. Then, we perform a security evaluation of CTKD and uncover four cross-transport issues in its specification. We leverage these issues to design four standard-compliant attacks on CTKD enabling new ways to exploit Bluetooth (e.g., exploiting BT and BLE by targeting only one of the two). Our attacks work even if the strongest security mechanism for BT and BLE are in place, including Numeric Comparison and Secure Connections. They allow to impersonate, man-in-the-middle, and establish unintended sessions with arbitrary devices. We refer to our attacks as BLUR attacks, as they blur the security boundary between BT and BLE. We provide a low-cost implementation of the BLUR attacks and we successfully evaluate them on 14 devices with 16 unique Bluetooth chips from popular vendors. We discuss the attacks' root causes and present effective countermeasures to fix them. We disclosed our findings and countermeasures to the Bluetooth SIG in May 2020 (CVE-2020-15802), and we reported additional unmitigated issues in May 2021.