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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
SAFE^d: Self-Attestation For Networks of Heterogeneous Em...
Alessandro Visintin, Flavio Toffalini, Mauro Conti, Jianying Zho · 2019-09-18 · via cs.CR updates on arXiv.org

The Internet of Things (IoT) is an emerging paradigm that allows to set large networks of small and independent devices. To ensure their integrity, practitioners employ so-called Remote Attestation (RA) schemes. Classic RA schemes require a central and powerful entity, called Verifier, that has mainly two duties: (i) it manages the entire process of attestation, and (ii) it contains all the proofs for validating the devices' integrity. However, having a central Verifier makes the network dependent upon an external entity and introduces a single point of failure for security. In this work, we propose SAFE^d: the first RA schema that allows a pair of IoT devices to validate their integrity without relying on an external Verifier. Our approach overcomes previous limitations by spreading the proofs among multiple IoT devices and using novel cryptographic mechanisms to ensure secure communications. Moreover, the entire IoT network can collaboratively isolate tampered devices and recover missing proofs in case of anomalies. We evaluate our schema through an implementation for Raspberry Pi platform and a network simulation. The results show that SAFE^d can detect infected devices and recover up to 99.9% of proofs in case of faults or attacks. Moreover, we managed to protect up to 10K devices with a logarithmic overhead on the network and on the devices' memory.