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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
Alexa versus Alexa: Controlling Smart Speakers by Self-Is...
Sergio Esposito, Daniele Sgandurra, Giampaolo Bella · 2022-02-17 · via cs.CR updates on arXiv.org

We present Alexa versus Alexa (AvA), a novel attack that leverages audio files containing voice commands and audio reproduction methods in an offensive fashion, to gain control of Amazon Echo devices for a prolonged amount of time. AvA leverages the fact that Alexa running on an Echo device correctly interprets voice commands originated from audio files even when they are played by the device itself -- i.e., it leverages a command self-issue vulnerability. Hence, AvA removes the necessity of having a rogue speaker in proximity of the victim's Echo, a constraint that many attacks share. With AvA, an attacker can self-issue any permissible command to Echo, controlling it on behalf of the legitimate user. We have verified that, via AvA, attackers can control smart appliances within the household, buy unwanted items, tamper linked calendars and eavesdrop on the user. We also discovered two additional Echo vulnerabilities, which we call Full Volume and Break Tag Chain. The Full Volume increases the self-issue command recognition rate, by doubling it on average, hence allowing attackers to perform additional self-issue commands. Break Tag Chain increases the time a skill can run without user interaction, from eight seconds to more than one hour, hence enabling attackers to setup realistic social engineering scenarios. By exploiting these vulnerabilities, the adversary can self-issue commands that are correctly executed 99% of the times and can keep control of the device for a prolonged amount of time. We reported these vulnerabilities to Amazon via their vulnerability research program, who rated them with a Medium severity score. Finally, to assess limitations of AvA on a larger scale, we provide the results of a survey performed on a study group of 18 users, and we show that most of the limitations against AvA are hardly used in practice.