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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
FirmwareDroid: Security Analysis of the Android Firmware ...
Thomas Sutter · 2021-12-13 · via cs.CR updates on arXiv.org

The Android Open Source Project (AOSP) is probably the most used and customized operating system for smartphones and IoT devices worldwide. Its market share and high adaptability makes Android an interesting operating system for many developers. Nowadays, we use Android firmware in smartphones, TVs, smartwatches, cars, and other devices by various vendors and manufacturers. The sheer amount of customized Android firmware and devices makes it hard for security analysts to detect potentially harmful applications. Another fact is that many vendors include apps from 3rd party developers. Such bloatware usually has more privileges than standard apps and cannot be removed by the user without rooting the device. In recent years several cases were reported where 3rd party developers could include malicious apps into the Android built chain. Media reports claim that pre-installed malware like Chamois and Triade we able to infect several million devices. Such cases demonstrate the need for better strategies for analyzing Android firmware. In our study, we analyze the Android firmware eco-system in various ways. We collected a dataset with several thousand Android firmware archives and show that several terabytes of firmware data are waiting on the web to be analyzed. We develop a web service called FirmwareDroid for analyzing Android firmware archives and pre-installed apps and create a dataset of firmware samples. Focusing on Android apps, we automated the process of extracting and scanning pre-installed apps with state of the art open-source tools. We demonstrate on real data that pre-installed apps are, in fact, a a threat to Android's users, and we can detect several hundred malware samples using scanners like VirusTotal, AndroGuard, and APKiD. With state of the art tools, we could scan more than 900000 apps during our research and give unique insights into Android custom ROMs.