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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
Recipe: Hardware-Accelerated Replication Protocols
Dimitra Giantsidi, Emmanouil Giortamis, Julian Pritzi, Maurice B · 2025-02-13 · via cs.CR updates on arXiv.org

Replication protocols are essential for distributed systems, ensuring consistency, reliability, and fault tolerance. Traditional Crash Fault Tolerant (CFT) protocols, which assume a fail-stop model, are inadequate for untrusted cloud environments where adversaries or software bugs can cause Byzantine behavior. Byzantine Fault Tolerant (BFT) protocols address these threats but face significant performance, resource overheads, and scalability challenges. This paper introduces Recipe, a novel approach to transforming CFT protocols to operate securely in Byzantine settings without altering their core logic. Recipe rethinks CFT protocols in the context of modern cloud hardware, including many-core servers, RDMA-capable networks, and Trusted Execution Environments (TEEs). The approach leverages these advancements to enhance the security and performance of replication protocols in untrusted cloud environments. Recipe implements two practical security mechanisms, i.e., transferable authentication and non-equivocation, using TEEs and high-performance networking stacks (e.g., RDMA, DPDK). These mechanisms ensure that any CFT protocol can be transformed into a BFT protocol, guaranteeing authenticity and non-equivocation. The Recipe protocol consists of five key components: transferable authentication, initialization, normal operation, view change, and recovery phases. The protocol's correctness is formally verified using Tamarin, a symbolic model checker. Recipe is implemented as a library and applied to transform four widely used CFT protocols-Raft, Chain Replication, ABD, and AllConcur-into Byzantine settings. The results demonstrate up to 24x higher throughput compared to PBFT and 5.9x better performance than state-of-the-art BFT protocols. Additionally, Recipe requires fewer replicas and offers confidentiality, a feature absent in traditional BFT protocols.