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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
Goldfish: No More Attacks on Ethereum?!
Francesco D'Amato, Joachim Neu, Ertem Nusret Tas, David Tse · 2022-09-08 · via cs.CR updates on arXiv.org

The LMD GHOST consensus protocol is a critical component of proof-of-stake Ethereum. In its current form, this protocol is brittle, as evidenced by recent attacks and patching attempts. We propose Goldfish, a new protocol that satisfies key properties required of a drop-in replacement for LMD GHOST: Goldfish is secure in the sleepy model, assuming a majority of the validators follows the protocol. Goldfish is reorg resilient so that honestly produced blocks are guaranteed inclusion in the ledger, and it supports fast confirmation with expected confirmation latency independent of the desired security level. Subsampling validators can improve the communication efficiency of Goldfish, and Goldfish is composable with finality/accountability gadgets. Crucially, Goldfish is structurally similar to LMD GHOST, providing a credible path to adoption in Ethereum. Attacks on LMD GHOST exploit lack of coordination among honest validators, typically provided by a locking mechanism in classical BFT protocols. However, locking requires votes from a quorum of all participants and is not compatible with fluctuating participation. Goldfish is powered by a novel coordination mechanism to synchronize the honest validators' actions. Experiments with our prototype implementation of Goldfish suggest practicality.