Locking Machine Learning Models into Hardware
Fuente:
arXiv
Saved in:
| Main Authors: | Clifford, Eleanor, Saravanan, Adhithya, Langford, Harry, Zhang, Cheng, Zhao, Yiren, Mullins, Robert, Shumailov, Ilia, Hayes, Jamie |
|---|---|
| Format: | Preprint |
| Published: |
2024
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
Architectural Neural Backdoors from First Principles
by: Langford, Harry, et al.
Published: (2024)
by: Langford, Harry, et al.
Published: (2024)
Beyond Slow Signs in High-fidelity Model Extraction
by: Foerster, Hanna, et al.
Published: (2024)
by: Foerster, Hanna, et al.
Published: (2024)
Reasoning Introduces New Poisoning Attacks Yet Makes Them More Complicated
by: Foerster, Hanna, et al.
Published: (2025)
by: Foerster, Hanna, et al.
Published: (2025)
ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks
by: Clifford, Eleanor, et al.
Published: (2022)
by: Clifford, Eleanor, et al.
Published: (2022)
Quantamination: Dynamic Quantization Leaks Your Data Across the Batch
by: Foerster, Hanna, et al.
Published: (2026)
by: Foerster, Hanna, et al.
Published: (2026)
Stealing User Prompts from Mixture of Experts
by: Yona, Itay, et al.
Published: (2024)
by: Yona, Itay, et al.
Published: (2024)
Machine Learning needs Better Randomness Standards: Randomised Smoothing and PRNG-based attacks
by: Dahiya, Pranav, et al.
Published: (2023)
by: Dahiya, Pranav, et al.
Published: (2023)
Interpreting the Repeated Token Phenomenon in Large Language Models
by: Yona, Itay, et al.
Published: (2025)
by: Yona, Itay, et al.
Published: (2025)
Buffer Overflow in Mixture of Experts
by: Hayes, Jamie, et al.
Published: (2024)
by: Hayes, Jamie, et al.
Published: (2024)
Trusted Machine Learning Models Unlock Private Inference for Problems Currently Infeasible with Cryptography
by: Shumailov, Ilia, et al.
Published: (2025)
by: Shumailov, Ilia, et al.
Published: (2025)
Architectural Backdoors for Within-Batch Data Stealing and Model Inference Manipulation
by: Küchler, Nicolas, et al.
Published: (2025)
by: Küchler, Nicolas, et al.
Published: (2025)
Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots
by: Vero, Mark, et al.
Published: (2026)
by: Vero, Mark, et al.
Published: (2026)
The Curse of Recursion: Training on Generated Data Makes Models Forget
by: Shumailov, Ilia, et al.
Published: (2023)
by: Shumailov, Ilia, et al.
Published: (2023)
Gradients Look Alike: Sensitivity is Often Overestimated in DP-SGD
by: Thudi, Anvith, et al.
Published: (2023)
by: Thudi, Anvith, et al.
Published: (2023)
Watermarking Needs Input Repetition Masking
by: Khachaturov, David, et al.
Published: (2025)
by: Khachaturov, David, et al.
Published: (2025)
UnUnlearning: Unlearning is not sufficient for content regulation in advanced generative AI
by: Shumailov, Ilia, et al.
Published: (2024)
by: Shumailov, Ilia, et al.
Published: (2024)
Inexact Unlearning Needs More Careful Evaluations to Avoid a False Sense of Privacy
by: Hayes, Jamie, et al.
Published: (2024)
by: Hayes, Jamie, et al.
Published: (2024)
Complexity Matters: Effective Dimensionality as a Measure for Adversarial Robustness
by: Khachaturov, David, et al.
Published: (2024)
by: Khachaturov, David, et al.
Published: (2024)
Cascading Adversarial Bias from Injection to Distillation in Language Models
by: Chaudhari, Harsh, et al.
Published: (2025)
by: Chaudhari, Harsh, et al.
Published: (2025)
Defeating Prompt Injections by Design
by: Debenedetti, Edoardo, et al.
Published: (2025)
by: Debenedetti, Edoardo, et al.
Published: (2025)
Soft Instruction De-escalation Defense
by: Walter, Nils Philipp, et al.
Published: (2025)
by: Walter, Nils Philipp, et al.
Published: (2025)
Gaussian DP for Reporting Differential Privacy Guarantees in Machine Learning
by: Gomez, Juan Felipe, et al.
Published: (2025)
by: Gomez, Juan Felipe, et al.
Published: (2025)
Exploring the limits of strong membership inference attacks on large language models
by: Hayes, Jamie, et al.
Published: (2025)
by: Hayes, Jamie, et al.
Published: (2025)
Machine Learning Models Have a Supply Chain Problem
by: Meiklejohn, Sarah, et al.
Published: (2025)
by: Meiklejohn, Sarah, et al.
Published: (2025)
Decentralized Weather Forecasting via Distributed Machine Learning and Blockchain-Based Model Validation
by: Umar, Rilwan, et al.
Published: (2025)
by: Umar, Rilwan, et al.
Published: (2025)
Thought-Transfer: Indirect Targeted Poisoning Attacks on Chain-of-Thought Reasoning Models
by: Chaudhari, Harsh, et al.
Published: (2026)
by: Chaudhari, Harsh, et al.
Published: (2026)
LLA: Enhancing Security and Privacy for Generative Models with Logic-Locked Accelerators
by: Li, You, et al.
Published: (2025)
by: Li, You, et al.
Published: (2025)
Beyond Laplace and Gaussian: Exploring the Generalized Gaussian Mechanism for Private Machine Learning
by: Rinberg, Roy, et al.
Published: (2025)
by: Rinberg, Roy, et al.
Published: (2025)
Breach By A Thousand Leaks: Unsafe Information Leakage in `Safe' AI Responses
by: Glukhov, David, et al.
Published: (2024)
by: Glukhov, David, et al.
Published: (2024)
SALAD: Systematic Assessment of Machine Unlearning on LLM-Aided Hardware Design
by: Wang, Zeng, et al.
Published: (2025)
by: Wang, Zeng, et al.
Published: (2025)
SEA: Shareable and Explainable Attribution for Query-based Black-box Attacks
by: Gao, Yue, et al.
Published: (2023)
by: Gao, Yue, et al.
Published: (2023)
Uncertainty-Aware Hardware Trojan Detection Using Multimodal Deep Learning
by: Vishwakarma, Rahul, et al.
Published: (2024)
by: Vishwakarma, Rahul, et al.
Published: (2024)
Research on Dynamic Data Flow Anomaly Detection based on Machine Learning
by: Wang, Liyang, et al.
Published: (2024)
by: Wang, Liyang, et al.
Published: (2024)
RMF: A Risk Measurement Framework for Machine Learning Models
by: Schröder, Jan, et al.
Published: (2024)
by: Schröder, Jan, et al.
Published: (2024)
AI-Driven Anonymization: Protecting Personal Data Privacy While Leveraging Machine Learning
by: Yang, Le, et al.
Published: (2024)
by: Yang, Le, et al.
Published: (2024)
Optimistic Verifiable Training by Controlling Hardware Nondeterminism
by: Srivastava, Megha, et al.
Published: (2024)
by: Srivastava, Megha, et al.
Published: (2024)
Beyond the Calibration Point: Mechanism Comparison in Differential Privacy
by: Kaissis, Georgios, et al.
Published: (2024)
by: Kaissis, Georgios, et al.
Published: (2024)
An AI Architecture with the Capability to Classify and Explain Hardware Trojans
by: Whitten, Paul, et al.
Published: (2024)
by: Whitten, Paul, et al.
Published: (2024)
ceLLMate: Sandboxing Browser AI Agents
by: Meng, Luoxi, et al.
Published: (2025)
by: Meng, Luoxi, et al.
Published: (2025)
A Survey of Zero-Knowledge Proof Based Verifiable Machine Learning
by: Peng, Zhizhi, et al.
Published: (2025)
by: Peng, Zhizhi, et al.
Published: (2025)
Similar Items
-
Architectural Neural Backdoors from First Principles
by: Langford, Harry, et al.
Published: (2024) -
Beyond Slow Signs in High-fidelity Model Extraction
by: Foerster, Hanna, et al.
Published: (2024) -
Reasoning Introduces New Poisoning Attacks Yet Makes Them More Complicated
by: Foerster, Hanna, et al.
Published: (2025) -
ImpNet: Imperceptible and blackbox-undetectable backdoors in compiled neural networks
by: Clifford, Eleanor, et al.
Published: (2022) -
Quantamination: Dynamic Quantization Leaks Your Data Across the Batch
by: Foerster, Hanna, et al.
Published: (2026)