Related papers: Economic Security of Multiple Shared Security Protocols

Related papers

GAUGE: A Formal Framework for Measuring Cryptographic Security under Heterogeneous Adversary Cost Models [0.0]
Standards bodies report cryptographic security as a single number of bits.<n>Different conventions produce different rankings of cryptographic schemes.<n>GAUGE provides an explicit and auditable framework for reporting cryptographic security under competing cost models.
arXiv Detail & Related papers (2026-09-15T14:55:29Z)
Scalable Composition of Byzantine Agreements under Reorder Attacks [5.485342264567332]
We present the first adversary model that combines party corruption and channel attacks.<n>Based on this model, we establish new security thresholds for Byzantine agreement under parallel and concurrent compositions.<n>We prove the existence of secure protocols for unauthenticated Byzantine agreement under parallel and concurrent composition.
arXiv Detail & Related papers (2026-09-09T02:35:45Z)
Escaping the Diversity Trap in Robotic Manipulation via Anchor-Centric Adaptation [50.23374353859762]
"Maximizing coverage" by collecting diverse, single-shot demonstrations can be self-defeating due to non-vanishing estimation noise.<n>We formalize this phenomenon as a Coverage--Density Trade-off.<n>We propose Anchor-Centric Adaptation (ACA), a two-stage framework that first stabilizes a policy skeleton through repeated demonstrations at core anchors, then expands coverage to high-risk boundaries via teacher-forced error mining and constrained residual updates.
arXiv Detail & Related papers (2026-05-08T07:35:24Z)
AI Security in the Foundation Model Era: A Comprehensive Survey from a Unified Perspective [12.53973720107764]
We propose a closed-loop threat taxonomy that frames model-data interactions along four axes.<n>The resulting four classes of security threats represent distinct but interrelated categories of attacks.<n>Our framework establishes a foundation for developing scalable, transferable, and cross-modal security strategies.
arXiv Detail & Related papers (2026-03-25T22:53:43Z)
Byzantine-Robust and Differentially Private Federated Optimization under Weaker Assumptions [89.52532304099522]
Federated Learning (FL) enables clients to collaboratively train a shared model without centralizing their raw data, offering an inherent level of privacy.<n> gradients and model updates can still leak sensitive information, while malicious servers may mount adversarial attacks such as Byzantine manipulation.<n>These vulnerabilities highlight the need to address differential privacy (DP) and Byzantine robustness within a unified framework.<n>We propose Byz-Clip21-SGD2M, a new algorithm that integrates robust aggregation with double momentum and carefully designed clipping.
arXiv Detail & Related papers (2026-03-24T17:39:09Z)
Attributing and Exploiting Safety Vectors through Global Optimization in Large Language Models [50.91504059485288]
We propose a framework that identifies safety-critical attention heads through global optimization over all heads simultaneously.<n>We develop a novel inference-time white-box jailbreak method that exploits the identified safety vectors through activation repatching.
arXiv Detail & Related papers (2026-01-22T09:32:43Z)
Adversarial Attack-Defense Co-Evolution for LLM Safety Alignment via Tree-Group Dual-Aware Search and Optimization [51.12422886183246]
Large Language Models (LLMs) have developed rapidly in web services, delivering unprecedented capabilities while amplifying societal risks.<n>Existing works tend to focus on either isolated jailbreak attacks or static defenses, neglecting the dynamic interplay between evolving threats and safeguards in real-world web contexts.<n>We propose ACE-Safety, a novel framework that jointly optimize attack and defense models by seamlessly integrating two key innovative procedures.
arXiv Detail & Related papers (2025-11-24T15:23:41Z)
Consensus Sampling for Safer Generative AI [8.93965818386567]
Many approaches to AI safety rely on inspecting model outputs or activations, yet certain risks are inherently undetectable by inspection alone.<n>We propose a complementary, architecture-agnostic approach that enhances safety through the aggregation of multiple generative models.<n>We present a consensus sampling algorithm that, given $k$ models and a prompt, achieves risk competitive with the average risk of the safest $s$ of the $k$ models.
arXiv Detail & Related papers (2025-11-12T17:09:45Z)
Pay for The Second-Best Service: A Game-Theoretic Approach Against Dishonest LLM Providers [18.350214149130267]
This work tackles the issue through the lens of game theory and mechanism design.<n>We are the first to propose a formal economic model for a realistic user-provider ecosystem.
arXiv Detail & Related papers (2025-11-02T08:18:20Z)
MISLEADER: Defending against Model Extraction with Ensembles of Distilled Models [56.09354775405601]
Model extraction attacks aim to replicate the functionality of a black-box model through query access.<n>Most existing defenses presume that attacker queries have out-of-distribution (OOD) samples, enabling them to detect and disrupt suspicious inputs.<n>We propose MISLEADER, a novel defense strategy that does not rely on OOD assumptions.
arXiv Detail & Related papers (2025-06-03T01:37:09Z)
Adversary-Augmented Simulation for Fairness Evaluation and Defense in Hyperledger Fabric [0.0]
This paper presents an adversary model and a simulation framework specifically tailored for analyzing attacks on distributed systems composed of multiple protocols.<n>Our model classifies and constrains adversarial actions based on the assumptions of the target protocols.<n>We apply this framework to analyze fairness properties in a Hyperledger Fabric (HF) blockchain network.
arXiv Detail & Related papers (2025-04-17T08:17:27Z)
$\ extit{Agents Under Siege}$: Breaking Pragmatic Multi-Agent LLM Systems with Optimized Prompt Attacks [32.42704787246349]
Multi-agent Large Language Model (LLM) systems create novel adversarial risks because their behavior depends on communication between agents and decentralized reasoning.<n>In this work, we innovatively focus on attacking pragmatic systems that have constrains such as limited token bandwidth, latency between message delivery, and defense mechanisms.<n>We design a $textitpermutation-invariant adversarial attack$ that optimize prompt distribution across latency and bandwidth-constraint network topologies to bypass distributed safety mechanisms.
arXiv Detail & Related papers (2025-03-31T20:43:56Z)
CoBRA: A Universal Strategyproof Confirmation Protocol for Quorum-based Proof-of-Stake Blockchains [1.5761916307614148]
We present a formal analysis of quorum-based State Machine Replication (SMR) protocols in Proof-of-Stake (PoS) systems under a hybrid threat model comprising honest, Byzantine, and rational validators.<n>Our analysis of traditional quorum-based protocols establishes two fundamental impossibility results: (1) in partially synchronous networks, no quorum-based protocol can achieve SMR when rational and Byzantine validators comprise more than $1/3$ of participants, and (2) in synchronous networks, SMR remains impossible when rational and Byzantine validators comprise $2/3$ or more of participants.
arXiv Detail & Related papers (2025-03-21T01:39:29Z)
Merger-as-a-Stealer: Stealing Targeted PII from Aligned LLMs with Model Merging [49.270050440553575]
We propose textttMerger-as-a-Stealer, a two-stage framework to achieve this attack.<n>First, the attacker fine-tunes a malicious model to force it to respond to any PII-related queries.<n>Second, the attacker inputs direct PII-related queries to the merged model to extract targeted PII.
arXiv Detail & Related papers (2025-02-22T05:34:53Z)
Multichannel Steganography: A Provably Secure Hybrid Steganographic Model for Secure Communication [0.0]
Secret-seeded PRNG drives a lightweight Markov-chain generator to produce contextually plausible cover parameters.<n>Masked bit-vector is imperceptibly embedded into conventional media via a variance-aware least-significant-bit algorithm.<n>We formalize a multichannel adversary model (MC-ATTACK) and prove that, under standard security assumptions, the adversary's distinguishing advantage is negligible.
arXiv Detail & Related papers (2025-01-08T13:58:07Z)
SLIP: Securing LLMs IP Using Weights Decomposition [0.0]
Large language models (LLMs) have recently seen widespread adoption, in both academia and industry. As these models grow, they become valuable intellectual property (IP), reflecting enormous investments by their owners. Current methods to protect models' IP on the edge have limitations in terms of practicality, loss in accuracy, or suitability to requirements. We introduce a novel hybrid inference algorithm, named SLIP, designed to protect edge-deployed models from theft.
arXiv Detail & Related papers (2024-07-15T16:37:55Z)
Maximum Entropy Heterogeneous-Agent Reinforcement Learning [45.377385280485065]
Multi-agent reinforcement learning (MARL) has been shown effective for cooperative games in recent years.<n>We propose a unified framework for learning policies to resolve issues related to sample complexity, training instability, and the risk of converging to a suboptimal Nash Equilibrium.<n>Based on the MaxEnt framework, we propose Heterogeneous-Agent Soft Actor-Critic (HASAC) algorithm.<n>We evaluate HASAC on six benchmarks: Bi-DexHands, Multi-Agent MuJoCo, StarCraft Challenge, Google Research Football, Multi-Agent Particle Environment, and Light Aircraft Game.
arXiv Detail & Related papers (2023-06-19T06:22:02Z)
Resilient Output Consensus Control of Heterogeneous Multi-agent Systems against Byzantine Attacks: A Twin Layer Approach [23.824617731137877]
We study the problem of cooperative control of heterogeneous multi-agent systems (MASs) against Byzantine attacks. Inspired by the concept of Digital Twin, a new hierarchical protocol equipped with a virtual twin layer (TL) is proposed.
arXiv Detail & Related papers (2023-03-22T18:23:21Z)
Defending Variational Autoencoders from Adversarial Attacks with MCMC [74.36233246536459]
Variational autoencoders (VAEs) are deep generative models used in various domains. As previous work has shown, one can easily fool VAEs to produce unexpected latent representations and reconstructions for a visually slightly modified input. Here, we examine several objective functions for adversarial attacks construction, suggest metrics assess the model robustness, and propose a solution.
arXiv Detail & Related papers (2022-03-18T13:25:18Z)
Robustness Certificates for Implicit Neural Networks: A Mixed Monotone Contractive Approach [60.67748036747221]
Implicit neural networks offer competitive performance and reduced memory consumption. They can remain brittle with respect to input adversarial perturbations. This paper proposes a theoretical and computational framework for robustness verification of implicit neural networks.
arXiv Detail & Related papers (2021-12-10T03:08:55Z)
ESCORT: Ethereum Smart COntRacTs Vulnerability Detection using Deep Neural Network and Transfer Learning [80.85273827468063]
Existing machine learning-based vulnerability detection methods are limited and only inspect whether the smart contract is vulnerable. We propose ESCORT, the first Deep Neural Network (DNN)-based vulnerability detection framework for smart contracts. We show that ESCORT achieves an average F1-score of 95% on six vulnerability types and the detection time is 0.02 seconds per contract.
arXiv Detail & Related papers (2021-03-23T15:04:44Z)
Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework [60.981406394238434]
We propose a general framework of adversarial certification with non-Gaussian noise and for more general types of attacks. Our proposed methods achieve better certification results than previous works and provide a new perspective on randomized smoothing certification.
arXiv Detail & Related papers (2020-02-21T07:52:47Z)

This list is automatically generated from the titles and abstracts of the papers in this site.