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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
Towards Practical Integrity in the Smart Home with HomeEn...
2021-09-11 · via cs.CR updates on arXiv.org

Home automation in modern smart home platforms is often facilitated using trigger-action routines. While such routines enable flexible automation, they also lead to an instance of the integrity problem in these systems: untrusted third-parties may use platform APIs to modify the abstract home objects (AHOs) that privileged, high-integrity devices such as security cameras rely on (i.e., as triggers), thereby transitively attacking them. As most accesses to AHOs are legitimate, removing the permissions or applying naive information flow controls would not only fail to prevent these problems, but also break useful functionality. Therefore, this paper proposes the alternate approach of home abstraction endorsement, which endorses a proposed change to an AHO by correlating it with certain specific, preceding, environmental changes. We present the HomeEndorser framework, which provides a policy model for specifying endorsement policies for AHOs as changes in device states, relative to their location, and a platform-based reference monitor for mediating all API requests to change AHOs against those device states. We evaluate HomeEndorser on the HomeAssistant platform, finding that we can derive over 1000 policy rules for HomeEndorser to endorse changes to 6 key AHOs, preventing malice and accidents for less than 10% overhead for endorsement check microbenchmarks, and with no false alarms under realistic usage scenarios. In doing so, HomeEndorser lays the first steps towards providing a practical foundation for ensuring that API-induced changes to abstract home objects correlate with the physical realities of the user's environment.