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cs.CR updates on arXiv.org

On the Security of Research Artifacts SafeHarbor: Hierarchical Memory-Augmented Guardrail for LLM Agent Safety 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 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
IntegriScreen: Visually Supervising Remote User Interacti...
Ivo Sluganovic, Enis Ulqinaku, Aritra Dhar, Daniele Lain, Srdjan · 2020-11-28 · via cs.CR updates on arXiv.org

Remote services and applications that users access via their local clients (laptops or desktops) usually assume that, following a successful user authentication at the beginning of the session, all subsequent communication reflects the user's intent. However, this is not true if the adversary gains control of the client and can therefore manipulate what the user sees and what is sent to the remote server. To protect the user's communication with the remote server despite a potentially compromised local client, we propose the concept of continuous visual supervision by a second device equipped with a camera. Motivated by the rapid increase of the number of incoming devices with front-facing cameras, such as augmented reality headsets and smart home assistants, we build upon the core idea that the user's actual intended input is what is shown on the client's screen, despite what ends up being sent to the remote server. A statically positioned camera enabled device can, therefore, continuously analyze the client's screen to enforce that the client behaves honestly despite potentially being malicious. We evaluate the present-day feasibility and deployability of this concept by developing a fully functional prototype, running a host of experimental tests on three different mobile devices, and by conducting a user study in which we analyze participants' use of the system during various simulated attacks. Experimental evaluation indeed confirms the feasibility of the concept of visual supervision, given that the system consistently detects over 98% of evaluated attacks, while study participants with little instruction detect the remaining attacks with high probability.