Cover Image

Moshi Moshi? A Model Selection Hijacking Adversarial Attack

Fuente: arXiv

Saved in:

Bibliographic Details
Main Authors:	Petrucci, Riccardo, Pajola, Luca, Marchiori, Francesco, Pasa, Luca, conti, Mauro
Format:	Preprint
Published:	2025
Subjects:	Machine Learning Cryptography and Security
Online Access:	Acceder al recurso
Tags:	Add Tag No Tags, Be the first to tag this record!

Similar Items

"All of Me": Mining Users' Attributes from their Public Spotify Playlists
by: Tricomi, Pier Paolo, et al.
Published: (2024)

DUMB and DUMBer: Is Adversarial Training Worth It in the Real World?
by: Marchiori, Francesco, et al.
Published: (2025)

Offensive AI: Enhancing Directory Brute-forcing Attack with the Use of Language Models
by: Castagnaro, Alberto, et al.
Published: (2024)

Model Hijacking Attack in Federated Learning
by: Li, Zheng, et al.
Published: (2024)

From Beats to Breaches:How Offensive AI Infers Sensitive User Information from Playlists
by: Cecconello, Stefano, et al.
Published: (2026)

Exploiting AI for Attacks: On the Interplay between Adversarial AI and Offensive AI
by: Schröer, Saskia Laura, et al.
Published: (2025)

Vera Verto: Multimodal Hijacking Attack
by: Zhang, Minxing, et al.
Published: (2024)

Leaky Batteries: A Novel Set of Side-Channel Attacks on Electric Vehicles
by: Marchiori, Francesco, et al.
Published: (2025)

On the Robustness of Bayesian Neural Networks to Adversarial Attacks
by: Bortolussi, Luca, et al.
Published: (2022)

CANEDERLI: On The Impact of Adversarial Training and Transferability on CAN Intrusion Detection Systems
by: Marchiori, Francesco, et al.
Published: (2024)

Profiling Electric Vehicles via Early Charging Voltage Patterns
by: Marchiori, Francesco, et al.
Published: (2025)

Hijacking Large Language Models via Adversarial In-Context Learning
by: Zhou, Xiangyu, et al.
Published: (2023)

DiffusionHijack: Supply-Chain PRNG Backdoor Attack on Diffusion Models and Quantum Random Number Defense
by: You, Ziyang, et al.
Published: (2026)

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates
by: Angioni, Daniele, et al.
Published: (2024)

Frequency maps reveal the correlation between Adversarial Attacks and Implicit Bias
by: Basile, Lorenzo, et al.
Published: (2023)

Image Hijacks: Adversarial Images can Control Generative Models at Runtime
by: Bailey, Luke, et al.
Published: (2023)

Osmosis Distillation: Model Hijacking with the Fewest Samples
by: Shi, Yuchen, et al.
Published: (2026)

Calibration Attacks: A Comprehensive Study of Adversarial Attacks on Model Confidence
by: Obadinma, Stephen, et al.
Published: (2024)

Inference Attacks on Encrypted Online Voting via Traffic Analysis
by: Belousova, Anastasiia, et al.
Published: (2025)

Evaluating the Robustness of a Production Malware Detection System to Transferable Adversarial Attacks
by: Nasr, Milad, et al.
Published: (2025)

Enhancing Adversarial Attacks via Parameter Adaptive Adversarial Attack
by: Jin, Zhibo, et al.
Published: (2024)

ModSec-AdvLearn: Countering Adversarial SQL Injections with Robust Machine Learning
by: Floris, Giuseppe, et al.
Published: (2023)

The Hidden Threat in Plain Text: Attacking RAG Data Loaders
by: Castagnaro, Alberto, et al.
Published: (2025)

Work-in-Progress: Crash Course: Can (Under Attack) Autonomous Driving Beat Human Drivers?
by: Marchiori, Francesco, et al.
Published: (2024)

AutoRAN: Automated Hijacking of Safety Reasoning in Large Reasoning Models
by: Liang, Jiacheng, et al.
Published: (2025)

FABLE: A Localized, Targeted Adversarial Attack on Weather Forecasting Models
by: Deng, Yue, et al.
Published: (2025)

Exploring Jamming and Hijacking Attacks for Micro Aerial Drones
by: Mekdad, Yassine, et al.
Published: (2024)

ReLATE: Resilient Learner Selection for Multivariate Time-Series Classification Against Adversarial Attacks
by: Kocal, Cagla Ipek, et al.
Published: (2025)

Skill-Inject: Measuring Agent Vulnerability to Skill File Attacks
by: Schmotz, David, et al.
Published: (2026)

Beyond Crash: Hijacking Your Autonomous Vehicle for Fun and Profit
by: Sun, Qi, et al.
Published: (2026)

Robustness Against Adversarial Attacks via Learning Confined Adversarial Polytopes
by: Hamidi, Shayan Mohajer, et al.
Published: (2024)

Generalization Properties of Adversarial Training for $\ell_0$-Bounded Adversarial Attacks
by: Delgosha, Payam, et al.
Published: (2024)

TA3: Testing Against Adversarial Attacks on Machine Learning Models
by: Jin, Yuanzhe, et al.
Published: (2024)

Attack by Unlearning: Unlearning-Induced Adversarial Attacks on Graph Neural Networks
by: Zhang, Jiahao, et al.
Published: (2026)

Temporal Analysis of Adversarial Attacks in Federated Learning
by: Mapakshi, Rohit, et al.
Published: (2025)

Adversarial Contrastive Learning for LLM Quantization Attacks
by: Song, Dinghong, et al.
Published: (2026)

Efficient Adversarial Training in LLMs with Continuous Attacks
by: Xhonneux, Sophie, et al.
Published: (2024)

PROSAC: Provably Safe Certification for Machine Learning Models under Adversarial Attacks
by: Feng, Chen, et al.
Published: (2024)

A Comprehensive Analysis of Adversarial Attacks against Spam Filters
by: Hotoğlu, Esra, et al.
Published: (2025)

Test-time Adversarial Defense with Opposite Adversarial Path and High Attack Time Cost
by: Yeh, Cheng-Han, et al.
Published: (2024)