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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
Private, Auditable, and Distributed Ledger for Financial ...
Shaltiel Eloul, Yash Satsangi, Yeoh Wei Zhu, Omar Amer, Georgios · 2025-01-07 · via cs.CR updates on arXiv.org

Distributed ledger technology offers several advantages for banking and finance industry, including efficient transaction processing and cross-party transaction reconciliation. The key challenges for adoption of this technology in financial institutes are (a) the building of a privacy-preserving ledger, (b) supporting auditing and regulatory requirements, and (c) flexibility to adapt to complex use-cases with multiple digital assets and actors. This paper proposes a framework for a private, audit-able, and distributed ledger (PADL) that adapts easily to fundamental use-cases within financial institutes. PADL employs widely-used cryptography schemes combined with zero-knowledge proofs to propose a transaction scheme for a `table' like ledger. It enables fast confidential peer-to-peer multi-asset transactions, and transaction graph anonymity, in a no-trust setup, but with customized privacy. We prove that integrity and anonymity of PADL is secured against a strong threat model. Furthermore, we showcase three fundamental real-life use-cases, namely, an assets exchange ledger, a settlement ledger, and a bond market ledger. Based on these use-cases we show that PADL supports smooth-lined inter-assets auditing while preserving privacy of the participants. For example, we show how a bank can be audited for its liquidity or credit risk without violation of privacy of itself or any other party, or how can PADL ensures honest coupon rate payment in bond market without sharing investors values. Finally, our evaluation shows PADL's advantage in performance against previous relevant schemes.