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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
Technology Review of Blockchain Data Privacy Solutions
Jack Tanner, Roshaan Khan · 2021-05-04 · via cs.CR updates on arXiv.org

This objective of this report is to review existing enterprise blockchain technologies - EOSIO powered systems, Hyperledger Fabric and Besu, Consensus Quorum, R3 Corda and Ernst and Young's Nightfall - that provide data privacy while leveraging the data integrity benefits of blockchain. By reviewing and comparing how and how well these technologies achieve data privacy, a snapshot is captured of the industry's current best practices and data privacy models. Major enterprise technologies are contrasted in parallel to EOSIO to better understand how EOSIO can evolve to meet the trends seen in enterprise blockchain privacy. The following strategies and trends were generally observed in these technologies: Cryptography: the hashing algorithm was found to be the most used cryptographic primitive in enterprise or changeover privacy solutions. Coordination via on-chain contracts - a common strategy was to use a shared publicly ledger to coordinate data privacy groups and more generally managed identities and access control. Transaction and contract code sharing: there was a variety of different levels of privacy around the business logic (smart contract code) visibility. Some solutions only allowed authorised peers to view code while others made this accessible to everybody that was a member of the shared ledger. Data migrations for data privacy applications: significant challenges exist when using cryptographically stored data in terms of being able to run system upgrades. Multiple blockchain ledgers for data privacy: solutions attempted to create a new private blockchain for every private data relationship which was eventually abandoned in favour of one shared ledger with private data collections/transactions that were anchored to the ledger with a hash in order to improve scaling.