Daily Arxiv

전 세계에서 발간되는 인공지능 관련 논문을 정리하는 페이지 입니다.
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AC-DiT: Adaptive Coordination Diffusion Transformer for Mobile Manipulation

Self-Guided Process Reward Optimization with Redefined Step-wise Advantage for Process Reinforcement Learning

Crafting Hanzi as Narrative Bridges: An AI Co-Creation Workshop for Elderly Migrants

Distributional Soft Actor-Critic with Diffusion Policy

Skywork-Reward-V2: Scaling Preference Data Curation via Human-AI Synergy

Fast AI Model Splitting over Edge Networks

From Sentences to Sequences: Rethinking Languages in Biological System

MTCNet: Motion and Topology Consistency Guided Learning for Mitral Valve Segmentationin 4D Ultrasound

Horus: A Protocol for Trustless Delegation Under Uncertainty

Mixture of Reasonings: Teach Large Language Models to Reason with Adaptive Strategies

Benchmarking Generalizable Bimanual Manipulation: RoboTwin Dual-Arm Collaboration Challenge at CVPR 2025 MEIS Workshop

Red Teaming for Generative AI, Report on a Copyright-Focused Exercise Completed in an Academic Medical Center

AirV2X: Unified Air-Ground Vehicle-to-Everything Collaboration

Semantic Structure-Aware Generative Attacks for Enhanced Adversarial Transferability

Aligning Frozen LLMs by Reinforcement Learning: An Iterative Reweight-then-Optimize Approach

Distinguishing Predictive and Generative AI in Regulation

AIn't Nothing But a Survey? Using Large Language Models for Coding German Open-Ended Survey Responses on Survey Motivation

Text-Aware Image Restoration with Diffusion Models

How Good LLM-Generated Password Policies Are?

Towards an Explainable Comparison and Alignment of Feature Embeddings

Gradient-Based Model Fingerprinting for LLM Similarity Detection and Family Classification

Empowering Intelligent Low-altitude Economy with Large AI Model Deployment

Incorporating LLMs for Large-Scale Urban Complex Mobility Simulation

Generating Hypotheses of Dynamic Causal Graphs in Neuroscience: Leveraging Generative Factor Models of Observed Time Series

Traveling Across Languages: Benchmarking Cross-Lingual Consistency in Multimodal LLMs

Threat Modeling for AI: The Case for an Asset-Centric Approach

SoccerDiffusion: Toward Learning End-to-End Humanoid Robot Soccer from Gameplay Recordings

PAD: Phase-Amplitude Decoupling Fusion for Multi-Modal Land Cover Classification

Significativity Indices for Agreement Values

Transferrable Surrogates in Expressive Neural Architecture Search Spaces

Privacy-Preserving Operating Room Workflow Analysis using Digital Twins

Uncertainty-Guided Coarse-to-Fine Tumor Segmentation with Anatomy-Aware Post-Processing

CMD-HAR: Cross-Modal Disentanglement for Wearable Human Activity Recognition

Commander-GPT: Fully Unleashing the Sarcasm Detection Capability of Multi-Modal Large Language Models

Understanding-informed Bias Mitigation for Fair CMR Segmentation

HAPI: A Model for Learning Robot Facial Expressions from Human Preferences

MaizeField3D: A Curated 3D Point Cloud and Procedural Model Dataset of Field-Grown Maize from a Diversity Panel

Illuminant and light direction estimation using Wasserstein distance method

Fundamental Limits of Hierarchical Secure Aggregation with Cyclic User Association

LLM-Powered Prediction of Hyperglycemia and Discovery of Behavioral Treatment Pathways from Wearables and Diet

Interleaved Gibbs Diffusion: Generating Discrete-Continuous Data with Implicit Constraints

EquiTabPFN: A Target-Permutation Equivariant Prior Fitted Networks

Circuit-tuning: A Mechanistic Approach for Identifying Parameter Redundancy and Fine-tuning Neural Networks

EigenLoRAx: Recycling Adapters to Find Principal Subspaces for Resource-Efficient Adaptation and Inference

Learning Traffic Anomalies from Generative Models on Real-Time Observations

Enabling Population-Level Parallelism in Tree-Based Genetic Programming for Comprehensive GPU Acceleration

Parameters vs FLOPs: Scaling Laws for Optimal Sparsity for Mixture-of-Experts Language Models

Quantifying the Importance of Data Alignment in Downstream Model Performance

Quantum-enhanced causal discovery for a small number of samples

On Characterizations for Language Generation: Interplay of Hallucinations, Breadth, and Stability

Token Prepending: A Training-Free Approach for Eliciting Better Sentence Embeddings from LLMs

COEF-VQ: Cost-Efficient Video Quality Understanding through a Cascaded Multimodal LLM Framework

GeMID: Generalizable Models for IoT Device Identification

Next-Token Prediction Task Assumes Optimal Data Ordering for LLM Training in Proof Generation

Is Complex Query Answering Really Complex?

Aerial Vision-and-Language Navigation via Semantic-Topo-Metric Representation Guided LLM Reasoning

Offline Reinforcement Learning for Learning to Dispatch for Job Shop Scheduling

Reconsidering the energy efficiency of spiking neural networks

Exploring the Integration of Large Language Models in Industrial Test Maintenance Processes

Sequence-aware Pre-training for Echocardiography Probe Movement Guidance

Anatomical Foundation Models for Brain MRIs

Learning From Crowdsourced Noisy Labels: A Signal Processing Perspective

Quantifying the Cross-sectoral Intersecting Discrepancies within Multiple Groups Using Latent Class Analysis Towards Fairness

Delving into LLM-assisted writing in biomedical publications through excess vocabulary

Towards a Novel Measure of User Trust in XAI Systems

Avoiding Catastrophe in Online Learning by Asking for Help

Improving the Robustness of Distantly-Supervised Named Entity Recognition via Uncertainty-Aware Teacher Learning and Student-Student Collaborative Learning

Beyond Scale: The Diversity Coefficient as a Data Quality Metric for Variability in Natural Language Data

Kernel Density Bayesian Inverse Reinforcement Learning

Embodied AI Agents: Modeling the World

Mind2Web 2: Evaluating Agentic Search with Agent-as-a-Judge

AI Flow: Perspectives, Scenarios, and Approaches

A framework for Conditional Reasoning in Answer Set Programming

Autoformalization in the Era of Large Language Models: A Survey

Agentic AI Process Observability: Discovering Behavioral Variability

Artificial Intelligence Index Report 2025

MAPS: Advancing Multi-Modal Reasoning in Expert-Level Physical Science

XGeM: A Multi-Prompt Foundation Model for Multimodal Medical Data Generation

Direct Preference Optimization Using Sparse Feature-Level Constraints

Unsupervised Cognition

Urban Region Pre-training and Prompting: A Graph-based Approach

Road Graph Generator: Mapping roads at construction sites from GPS data

Point3R: Streaming 3D Reconstruction with Explicit Spatial Pointer Memory

LiteReality: Graphics-Ready 3D Scene Reconstruction from RGB-D Scans

Answer Matching Outperforms Multiple Choice for Language Model Evaluation

Subtyping in DHOL -- Extended preprint

MOTIF: Modular Thinking via Reinforcement Fine-tuning in LLMs

USAD: An Unsupervised Data Augmentation Spatio-Temporal Attention Diffusion Network

DNN-Based Precoding in RIS-Aided mmWave MIMO Systems With Practical Phase Shift

SynapseRoute: An Auto-Route Switching Framework on Dual-State Large Language Model

Self-Correction Bench: Revealing and Addressing the Self-Correction Blind Spot in LLMs

Multi-agent Auditory Scene Analysis

Fast and Simplex: 2-Simplicial Attention in Triton

Synthesizable by Design: A Retrosynthesis-Guided Framework for Molecular Analog Generation

Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics

Early Signs of Steganographic Capabilities in Frontier LLMs

Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks

FairHuman: Boosting Hand and Face Quality in Human Image Generation with Minimum Potential Delay Fairness in Diffusion Models

APT: Adaptive Personalized Training for Diffusion Models with Limited Data

ASDA: Audio Spectrogram Differential Attention Mechanism for Self-Supervised Representation Learning

Threat Modeling for AI: The Case for an Asset-Centric Approach

Created by

Haebom

저자

Jose Sanchez Vicarte, Marcin Spoczynski, Mostafa Elsaid

개요

본 논문은 AI 시스템이 독립적인 애플리케이션에서 깊이 통합된 AI 에이전트로 진화함에 따라 발생하는 새로운 보안 위협에 대응하는 자산 중심의 위협 모델링 방법론을 제시한다. 기존의 상향식 접근 방식과 달리, 하향식 접근 방식을 통해 개발 및 배포에 사용되는 분산 인프라 전반에 걸쳐 기존 및 AI 관련 취약성이 중요한 AI 자산에 미치는 영향을 체계적으로 식별한다. 이 방법론은 기술 도메인 간의 효과적인 의사소통, 타사 AI 구성 요소에 대한 보안 가정의 정량화(구현에 대한 가시성 없이), 특정 제품 컨텍스트와 관련된 AI 기반 취약성의 전체적인 식별을 가능하게 한다. 특히 복잡한 자율 기능을 가진 에이전트 시스템의 보안에 적합하며, 공격이 아닌 자산에 중점을 둠으로써 빠르게 변화하는 위협 환경에 확장 가능하고 점점 더 복잡해지는 분산된 AI 개발 파이프라인을 수용한다.

시사점, 한계점

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시사점:

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자산 중심의 위협 모델링 방법론을 통해 AI 에이전트의 고유한 보안 위협에 효과적으로 대응할 수 있다.

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기술 도메인 간의 효과적인 의사소통 및 타사 AI 구성 요소에 대한 보안 가정의 정량화를 가능하게 한다.

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특정 제품 컨텍스트에 관련된 AI 기반 취약성을 전체적으로 식별할 수 있다.

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빠르게 변화하는 위협 환경에 확장 가능하고 복잡한 분산된 AI 개발 파이프라인을 수용한다.

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한계점:

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제시된 방법론의 실제 적용 및 효과에 대한 실증적 연구가 부족하다.

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새로운 유형의 AI 위협이 지속적으로 등장하는 상황에서, 본 방법론의 장기적인 유효성에 대한 검증이 필요하다.

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자산 정의 및 분류에 대한 명확한 기준 및 절차가 부족할 수 있다.

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복잡한 AI 시스템의 모든 자산을 완벽하게 식별하고 분석하는 데 어려움이 있을 수 있다.

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