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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
Are PETs (Privacy Enhancing Technologies) Giving Protecti...
Tanusree Sharma, Masooda Bashir · 2020-07-09 · via cs.CR updates on arXiv.org

With smartphone technologies enhanced way of interacting with the world around us, it has also been paving the way for easier access to our private and personal information. This has been amplified by the existence of numerous embedded sensors utilized by millions of apps to users. While mobile apps have positively transformed many aspects of our lives with new functionalities, many of these applications are taking advantage of vast amounts of data, privacy apps, a form of Privacy Enhancing Technology can be an effective privacy management tool for smartphones. To protect against vulnerabilities related to the collection, storage, and sharing of sensitive data, developers are building numerous privacy apps. However, there has been a lack of discretion in this particular area which calls for a proper assessment to understand the far-reaching utilization of these apps among users. During this process we have conducted an evaluation of the most popular privacy apps from our total collection of five hundred and twelve to demonstrate their functionality specific data protections they are claiming to offer, both technologically and conventionally, measuring up to standards. Taking their offered security functionalities as a scale, we conducted forensic experiments to indicate where they are failing to be consistent in maintaining protection. For legitimate validation of security gaps in assessed privacy apps, we have also utilized NIST and OWASP guidelines. We believe this study will be efficacious for continuous improvement and can be considered as a foundation towards a common standard for privacy and security measures for an app's development stage.