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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
Evaluating the Red Belly Blockchain
Tyler Crain, Christopher Natoli, Vincent Gramoli · 2018-12-31 · via cs.CR updates on arXiv.org

In this paper, we present the most extensive evaluation of blockchain system to date. To achieve scalability across servers in more than 10 countries located on 4 different continents, we drastically revisited Byzantine fault tolerant blockchains and verification of signatures. The resulting blockchain, called the Red Belly Blockchain (RBBC), commits more than a hundred thousand transactions issued by permissionless nodes. These transactions are grouped into blocks within few seconds through a partially synchronous consensus run by permissioned nodes. It prevents double spending by guaranteeing that a unique block is decided at any given index of the chain in a deterministic way by all participants. We compared the performance of RBBC against traditional Byzantine fault tolerant alternatives and more recent randomized solutions. In the same geo-distributed environment with low-end machines, we noticed two interesting comparisons: (i) the RBBC throughput scales to hundreds of machines whereas the classic 3-step leader-based BFT state machine used by consortium blockchains cannot scale to 40 identically configured nodes; (ii) RBBC guarantees transaction finality in 3 seconds and experiences a third of the latency that randomized-based solutions like HoneyBadgerBFT can offer. This empirical evaluation demonstrates that blockchain scalability can be achieved without sacrificing security.