Copertina

Phantom Transfer: Data-level Defences are Insufficient Against Data Poisoning

Fuente: arXiv

Salvato in:

Dettagli Bibliografici
Autori principali:	Draganov, Andrew, Dur, Tolga H., Bhongade, Anandmayi, Phuong, Mary
Natura:	Preprint
Pubblicazione:	2026
Soggetti:	Cryptography and Security Artificial Intelligence
Accesso online:	Acceder al recurso
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

LLMs Can Covertly Sandbag on Capability Evaluations Against Chain-of-Thought Monitoring
di: Li, Chloe, et al.
Pubblicazione: (2025)

Shadowcast: Stealthy Data Poisoning Attacks Against Vision-Language Models
di: Xu, Yuancheng, et al.
Pubblicazione: (2024)

How Reliable Are AI Attackers Against a Fixed Vulnerable Target? A 400-Run Empirical Study of LLM Penetration Testing Consistency
di: Erdem, Galip Tolga
Pubblicazione: (2026)

Data Poisoning in Deep Learning: A Survey
di: Zhao, Pinlong, et al.
Pubblicazione: (2025)

Defending Against Beta Poisoning Attacks in Machine Learning Models
di: Gulciftci, Nilufer, et al.
Pubblicazione: (2025)

Turning Generative Models Degenerate: The Power of Data Poisoning Attacks
di: Jiang, Shuli, et al.
Pubblicazione: (2024)

When and Where do Data Poisons Attack Textual Inversion?
di: Styborski, Jeremy, et al.
Pubblicazione: (2025)

CBPF: Filtering Poisoned Data Based on Composite Backdoor Attack
di: Xia, Hanfeng, et al.
Pubblicazione: (2024)

Have You Poisoned My Data? Defending Neural Networks against Data Poisoning
di: De Gaspari, Fabio, et al.
Pubblicazione: (2024)

Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks
di: Halloran, John T., et al.
Pubblicazione: (2026)

Through the Stealth Lens: Attention-Aware Defenses Against Poisoning in RAG
di: Choudhary, Sarthak, et al.
Pubblicazione: (2025)

FIDELIS: Blockchain-Enabled Protection Against Poisoning Attacks in Federated Learning
di: Carney, Jane, et al.
Pubblicazione: (2025)

Precision Guided Approach to Mitigate Data Poisoning Attacks in Federated Learning
di: Kumar, K Naveen, et al.
Pubblicazione: (2024)

Vulnerabilities in AI Code Generators: Exploring Targeted Data Poisoning Attacks
di: Cotroneo, Domenico, et al.
Pubblicazione: (2023)

PoisonBench: Assessing Large Language Model Vulnerability to Data Poisoning
di: Fu, Tingchen, et al.
Pubblicazione: (2024)

Scaling Trends for Data Poisoning in LLMs
di: Bowen, Dillon, et al.
Pubblicazione: (2024)

Unraveling the Connections between Privacy and Certified Robustness in Federated Learning Against Poisoning Attacks
di: Xie, Chulin, et al.
Pubblicazione: (2022)

Addressing The Devastating Effects Of Single-Task Data Poisoning In Exemplar-Free Continual Learning
di: Pawlak, Stanisław, et al.
Pubblicazione: (2025)

Data Poisoning Vulnerabilities Across Healthcare AI Architectures: A Security Threat Analysis
di: Abtahi, Farhad, et al.
Pubblicazione: (2025)

Robustness Analysis of Machine Learning Models for IoT Intrusion Detection Under Data Poisoning Attacks
di: Wulnye, Fortunatus Aabangbio, et al.
Pubblicazione: (2026)

Building Better Environments for Autonomous Cyber Defence
di: Hicks, Chris, et al.
Pubblicazione: (2026)

SuperLocalMemory: Privacy-Preserving Multi-Agent Memory with Bayesian Trust Defense Against Memory Poisoning
di: Bhardwaj, Varun Pratap
Pubblicazione: (2026)

Poisoned Acoustics
di: Dahme, Harrison
Pubblicazione: (2026)

The Stronger the Diffusion Model, the Easier the Backdoor: Data Poisoning to Induce Copyright Breaches Without Adjusting Finetuning Pipeline
di: Wang, Haonan, et al.
Pubblicazione: (2024)

PID: Prompt-Independent Data Protection Against Latent Diffusion Models
di: Li, Ang, et al.
Pubblicazione: (2024)

Data to Defense: The Role of Curation in Customizing LLMs Against Jailbreaking Attacks
di: Liu, Xiaoqun, et al.
Pubblicazione: (2024)

Data Poisoning Attacks on Off-Policy Policy Evaluation Methods
di: Lobo, Elita, et al.
Pubblicazione: (2024)

Poison Once, Exploit Forever: Environment-Injected Memory Poisoning Attacks on Web Agents
di: Zou, Wei, et al.
Pubblicazione: (2026)

Reasoning-Style Poisoning of LLM Agents via Stealthy Style Transfer: Process-Level Attacks and Runtime Monitoring in RSV Space
di: Zhou, Xingfu, et al.
Pubblicazione: (2025)

Supply-Chain Poisoning Attacks Against LLM Coding Agent Skill Ecosystems
di: Qu, Yubin, et al.
Pubblicazione: (2026)

Defending Against Weight-Poisoning Backdoor Attacks for Parameter-Efficient Fine-Tuning
di: Zhao, Shuai, et al.
Pubblicazione: (2024)

Persistent Pre-Training Poisoning of LLMs
di: Zhang, Yiming, et al.
Pubblicazione: (2024)

On The Dangers of Poisoned LLMs In Security Automation
di: Karlsen, Patrick, et al.
Pubblicazione: (2025)

FedNIA: Noise-Induced Activation Analysis for Mitigating Data Poisoning in FL
di: Hallaji, Ehsan, et al.
Pubblicazione: (2025)

Concealing Backdoor Model Updates in Federated Learning by Trigger-Optimized Data Poisoning
di: Zhang, Yujie, et al.
Pubblicazione: (2024)

CSC: Turning the Adversary's Poison against Itself
di: Shi, Yuchen, et al.
Pubblicazione: (2026)

Virus Infection Attack on LLMs: Your Poisoning Can Spread "VIA" Synthetic Data
di: Liang, Zi, et al.
Pubblicazione: (2025)

SAFELOC: Overcoming Data Poisoning Attacks in Heterogeneous Federated Machine Learning for Indoor Localization
di: Singampalli, Akhil, et al.
Pubblicazione: (2024)

LoopTrap: Termination Poisoning Attacks on LLM Agents
di: Xu, Huiyu, et al.
Pubblicazione: (2026)

Poison to Detect: Detection of Targeted Overfitting in Federated Learning
di: Mestari, Soumia Zohra El, et al.
Pubblicazione: (2025)