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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 Sink-driven Approach to Detecting Exposed Component Vul...
Daoyuan Wu, Xiapu Luo, Rocky K. C. Chang · 2014-05-24 · via cs.CR updates on arXiv.org

Android apps could expose their components for cooperating with other apps. This convenience, however, makes apps susceptible to the exposed component vulnerability (ECV), in which a dangerous API (commonly known as sink) inside its component can be triggered by other (malicious) apps. In the prior works, detecting these ECVs use a set of sinks pertaining to the ECVs under detection. In this paper, we argue that a more comprehensive and effective approach should start by a systematic selection and classification of vulnerability-specific sinks (VSinks). The set of VSinks is much larger than those used in the previous works. Based on these VSinks, our sink-driven approach can detect different kinds of ECVs in an app in two steps. First, VSinks and their categories are identified through a typical forward reachability analysis. Second, based on each VSink's category, a corresponding detection method is used to identify the ECV via a customized backward dataflow analysis. We also design a semi-auto guided analysis and validation capability for system-only broadcast checking to remove some false positives. We implement our sink-driven approach in a tool called ECVDetector and evaluate it with the top 1K Android apps. Using ECVDetector we successfully identify a total of 49 vulnerable apps across all four ECV categories we have defined. To our knowledge, most of them are previously undisclosed, such as the very popular Go SMS Pro and Clean Master. Moreover, the performance of ECVDetector is high, requiring only 9.257 seconds on average to process each component.