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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
iTieProbe: Is Your IoT Setup Secure against (Modern) Evil...
Anand Agrawal, Rajib Ranjan Maiti · 2023-04-24 · via cs.CR updates on arXiv.org

Evil twin attack on Wi-Fi network has been a challenging security problem and several solutions have been proposed to this problem. In general, evil twin attack aims to exfiltrate data, like Wi-Fi and service credentials, from the client devices and considered as a serious threat at MAC layer. IoT devices with its companion apps provides different pairing methods for provisioning. The "SmartConfig Mode", the one proposed by Texas Instrument (TI) and the "Access Point pairing mode (AP mode)" are the most common pairing modes provided by the application developer and vendor of the IoT devices. Especially, AP mode use Wi-Fi connectivity to setup IoT devices where a device activates an access point to which the mobile device running the corresponding mobile application is required to connect. In this paper, we have used evil twin attack as a weapon to test the security posture of IoT devices that use Wi-Fi network to set them up. We have designed, implemented and applied a system, called iTieProbe, that can be used in ethical hacking for discovering certain vulnerabilities during such setup. AP mode successfully completes when the mobile device is able to communicate with the IoT device via a home router over a Wi-Fi network. Our proposed system, iTieProbe, is capable of discovering several serious vulnerabilities in the commercial IoT devices that use AP mode or similar approach. We evaluated iTieProbe's efficacy on 9 IoT devices, like IoT cameras, smart plugs, Echo Dot and smart bulbs, and discovered that several of these IoT devices have certain serious threats, like leaking Wi-Fi credential of home router and creating fake IoT device, during the setup of the IoT devices.