Cover Image

Whispers in the Machine: Confidentiality in Agentic Systems

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Evertz, Jonathan, Chlosta, Merlin, Schönherr, Lea, Eisenhofer, Thorsten
Format:	Preprint
Published:	2024
Subjects:	Cryptography and Security Machine Learning
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

Prompt Obfuscation for Large Language Models
by: Pape, David, et al.
Published: (2024)

No More, No Less: Task Alignment in Terminal Agents
by: Mavali, Sina, et al.
Published: (2026)

Adversarial Observations in Weather Forecasting
by: Imgrund, Erik, et al.
Published: (2025)

Learned-Database Systems Security
by: Schuster, Roei, et al.
Published: (2022)

Hardware-Triggered Backdoors
by: Möller, Jonas, et al.
Published: (2026)

Trust Me, I Know This Function: Hijacking LLM Static Analysis using Bias
by: Bernstein, Shir, et al.
Published: (2025)

Machine Learning with Confidential Computing: A Systematization of Knowledge
by: Mo, Fan, et al.
Published: (2022)

HexaCoder: Secure Code Generation via Oracle-Guided Synthetic Training Data
by: Hajipour, Hossein, et al.
Published: (2024)

Confidential Federated Computations
by: Eichner, Hubert, et al.
Published: (2024)

Chasing Shadows: Pitfalls in LLM Security Research
by: Evertz, Jonathan, et al.
Published: (2025)

LLM-based Vulnerability Discovery through the Lens of Code Metrics
by: Weissberg, Felix, et al.
Published: (2025)

Proteus: Preserving Model Confidentiality during Graph Optimizations
by: Gao, Yubo, et al.
Published: (2024)

Agentic Privacy-Preserving Machine Learning
by: Zhang, Mengyu, et al.
Published: (2025)

Tempo: Confidentiality Preservation in Cloud-Based Neural Network Training
by: Xu, Rongwu, et al.
Published: (2024)

Confidential and Protected Disease Classifier using Fully Homomorphic Encryption
by: Malik, Aditya, et al.
Published: (2024)

The Silent Hyperparameter: Quantifying the Impact of Inference Backends on LLM Reproducibility
by: Pape, David, et al.
Published: (2026)

Confidential Computing for Cloud Security: Exploring Hardware based Encryption Using Trusted Execution Environments
by: Agarwal, Dhruv Deepak, et al.
Published: (2025)

Careful Whisper: Attestation for peer-to-peer Confidential Computing networks
by: Kocaoğullar, Ceren, et al.
Published: (2025)

Whispers of Data: Unveiling Label Distributions in Federated Learning Through Virtual Client Simulation
by: Ma, Zhixuan, et al.
Published: (2025)

Whispering Under the Eaves: Protecting User Privacy Against Commercial and LLM-powered Automatic Speech Recognition Systems
by: Jin, Weifei, et al.
Published: (2025)

FairProof : Confidential and Certifiable Fairness for Neural Networks
by: Yadav, Chhavi, et al.
Published: (2024)

ExpProof : Operationalizing Explanations for Confidential Models with ZKPs
by: Yadav, Chhavi, et al.
Published: (2025)

$σ$-zero: Gradient-based Optimization of $\ell_0$-norm Adversarial Examples
by: Cinà, Antonio Emanuele, et al.
Published: (2024)

Sovereign Agentic Loops: Decoupling AI Reasoning from Execution in Real-World Systems
by: He, Jun, et al.
Published: (2026)

Can We Infer Confidential Properties of Training Data from LLMs?
by: Huang, Pengrun, et al.
Published: (2025)

ADR: An Agentic Detection System for Enterprise Agentic AI Security
by: Li, Chenning, et al.
Published: (2026)

Inference-Time Backdoors via Chat Templates: From LLM Supply Chains to Agentic System Compromise
by: Fogel, Ariel, et al.
Published: (2026)

Trust the Process: Zero-Knowledge Machine Learning to Enhance Trust in Generative AI Interactions
by: Ganescu, Bianca-Mihaela, et al.
Published: (2024)

Privacy Side Channels in Machine Learning Systems
by: Debenedetti, Edoardo, et al.
Published: (2023)

A Multiparty Homomorphic Encryption Approach to Confidential Federated Kaplan Meier Survival Analysis
by: Veeraragavan, Narasimha Raghavan, et al.
Published: (2024)

R+R:Understanding Hyperparameter Effects in DP-SGD
by: Morsbach, Felix, et al.
Published: (2024)

Fantômas: Understanding Face Anonymization Reversibility
by: Todt, Julian, et al.
Published: (2022)

Agentic Vulnerability Reasoning on Windows COM Binaries
by: Lee, Hwiwon, et al.
Published: (2026)

A Middle Path for On-Premises LLM Deployment: Preserving Privacy Without Sacrificing Model Confidentiality
by: Huang, Hanbo, et al.
Published: (2024)

Privacy-Preserving Data Sharing in Agriculture: Enforcing Policy Rules for Secure and Confidential Data Synthesis
by: Kotal, Anantaa, et al.
Published: (2023)

SoK: Dataset Copyright Auditing in Machine Learning Systems
by: Du, Linkang, et al.
Published: (2024)

Vulnerabilities in Machine Learning-Based Voice Disorder Detection Systems
by: Perelli, Gianpaolo, et al.
Published: (2024)

Protecting Confidentiality, Privacy and Integrity in Collaborative Learning
by: Chen, Dong, et al.
Published: (2024)

Confidential LLM Inference: Performance and Cost Across CPU and GPU TEEs
by: Chrapek, Marcin, et al.
Published: (2025)

TinyML Security: Exploring Vulnerabilities in Resource-Constrained Machine Learning Systems
by: Huckelberry, Jacob, et al.
Published: (2024)