LLMs unlock new paths to monetizing exploits
Fuente:
arXiv
Saved in:
| Main Authors: | Carlini, Nicholas, Nasr, Milad, Debenedetti, Edoardo, Wang, Barry, Choquette-Choo, Christopher A., Ippolito, Daphne, Tramèr, Florian, Jagielski, Matthew |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses
by: Carlini, Nicholas, et al.
Published: (2025)
by: Carlini, Nicholas, et al.
Published: (2025)
Privacy Side Channels in Machine Learning Systems
by: Debenedetti, Edoardo, et al.
Published: (2023)
by: Debenedetti, Edoardo, et al.
Published: (2023)
Are aligned neural networks adversarially aligned?
by: Carlini, Nicholas, et al.
Published: (2023)
by: Carlini, Nicholas, et al.
Published: (2023)
Evading Black-box Classifiers Without Breaking Eggs
by: Debenedetti, Edoardo, et al.
Published: (2023)
by: Debenedetti, Edoardo, et al.
Published: (2023)
Auditing Private Prediction
by: Chadha, Karan, et al.
Published: (2024)
by: Chadha, Karan, et al.
Published: (2024)
Exploring and Mitigating Adversarial Manipulation of Voting-Based Leaderboards
by: Huang, Yangsibo, et al.
Published: (2025)
by: Huang, Yangsibo, et al.
Published: (2025)
Persistent Pre-Training Poisoning of LLMs
by: Zhang, Yiming, et al.
Published: (2024)
by: Zhang, Yiming, et al.
Published: (2024)
Query-Based Adversarial Prompt Generation
by: Hayase, Jonathan, et al.
Published: (2024)
by: Hayase, Jonathan, et al.
Published: (2024)
Poisoning Web-Scale Training Datasets is Practical
by: Carlini, Nicholas, et al.
Published: (2023)
by: Carlini, Nicholas, et al.
Published: (2023)
Adversarial Search Engine Optimization for Large Language Models
by: Nestaas, Fredrik, et al.
Published: (2024)
by: Nestaas, Fredrik, et al.
Published: (2024)
On Evaluating the Durability of Safeguards for Open-Weight LLMs
by: Qi, Xiangyu, et al.
Published: (2024)
by: Qi, Xiangyu, et al.
Published: (2024)
Defeating Prompt Injections by Design
by: Debenedetti, Edoardo, et al.
Published: (2025)
by: Debenedetti, Edoardo, et al.
Published: (2025)
Privacy Auditing of Large Language Models
by: Panda, Ashwinee, et al.
Published: (2025)
by: Panda, Ashwinee, et al.
Published: (2025)
Remote Timing Attacks on Efficient Language Model Inference
by: Carlini, Nicholas, et al.
Published: (2024)
by: Carlini, Nicholas, et al.
Published: (2024)
Large-scale online deanonymization with LLMs
by: Lermen, Simon, et al.
Published: (2026)
by: Lermen, Simon, et al.
Published: (2026)
Position: Considerations for Differentially Private Learning with Large-Scale Public Pretraining
by: Tramèr, Florian, et al.
Published: (2022)
by: Tramèr, Florian, et al.
Published: (2022)
Adversarial Perturbations Cannot Reliably Protect Artists From Generative AI
by: Hönig, Robert, et al.
Published: (2024)
by: Hönig, Robert, et al.
Published: (2024)
The Last Iterate Advantage: Empirical Auditing and Principled Heuristic Analysis of Differentially Private SGD
by: Steinke, Thomas, et al.
Published: (2024)
by: Steinke, Thomas, et al.
Published: (2024)
Stealing Part of a Production Language Model
by: Carlini, Nicholas, et al.
Published: (2024)
by: Carlini, Nicholas, et al.
Published: (2024)
Phantom: General Backdoor Attacks on Retrieval Augmented Language Generation
by: Chaudhari, Harsh, et al.
Published: (2024)
by: Chaudhari, Harsh, et al.
Published: (2024)
Language Models May Verbatim Complete Text They Were Not Explicitly Trained On
by: Liu, Ken Ziyu, et al.
Published: (2025)
by: Liu, Ken Ziyu, et al.
Published: (2025)
AgentDojo: A Dynamic Environment to Evaluate Prompt Injection Attacks and Defenses for LLM Agents
by: Debenedetti, Edoardo, et al.
Published: (2024)
by: Debenedetti, Edoardo, et al.
Published: (2024)
Adversarial ML Problems Are Getting Harder to Solve and to Evaluate
by: Rando, Javier, et al.
Published: (2025)
by: Rando, Javier, et al.
Published: (2025)
Privacy Ripple Effects from Adding or Removing Personal Information in Language Model Training
by: Borkar, Jaydeep, et al.
Published: (2025)
by: Borkar, Jaydeep, et al.
Published: (2025)
SoK: Watermarking for AI-Generated Content
by: Zhao, Xuandong, et al.
Published: (2024)
by: Zhao, Xuandong, 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)
Teach LLMs to Phish: Stealing Private Information from Language Models
by: Panda, Ashwinee, et al.
Published: (2024)
by: Panda, Ashwinee, et al.
Published: (2024)
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)
IH-Challenge: A Training Dataset to Improve Instruction Hierarchy on Frontier LLMs
by: Guo, Chuan, et al.
Published: (2026)
by: Guo, Chuan, et al.
Published: (2026)
Universal Jailbreak Backdoors from Poisoned Human Feedback
by: Rando, Javier, et al.
Published: (2023)
by: Rando, Javier, et al.
Published: (2023)
Optimal Rates for $O(1)$-Smooth DP-SCO with a Single Epoch and Large Batches
by: Choquette-Choo, Christopher A., et al.
Published: (2024)
by: Choquette-Choo, Christopher A., et al.
Published: (2024)
Synthetic Query Generation for Privacy-Preserving Deep Retrieval Systems using Differentially Private Language Models
by: Carranza, Aldo Gael, et al.
Published: (2023)
by: Carranza, Aldo Gael, et al.
Published: (2023)
Can Reasoning Models Obfuscate Reasoning? Stress-Testing Chain-of-Thought Monitorability
by: Zolkowski, Artur, et al.
Published: (2025)
by: Zolkowski, Artur, et al.
Published: (2025)
Gradient-based Jailbreak Images for Multimodal Fusion Models
by: Rando, Javier, et al.
Published: (2024)
by: Rando, Javier, et al.
Published: (2024)
Measuring Non-Adversarial Reproduction of Training Data in Large Language Models
by: Aerni, Michael, et al.
Published: (2024)
by: Aerni, Michael, et al.
Published: (2024)
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)
Detecting Adversarial Fine-tuning with Auditing Agents
by: Egler, Sarah, et al.
Published: (2025)
by: Egler, Sarah, et al.
Published: (2025)
Near Exact Privacy Amplification for Matrix Mechanisms
by: Choquette-Choo, Christopher A., et al.
Published: (2024)
by: Choquette-Choo, Christopher A., et al.
Published: (2024)
Evaluating the Robustness of the "Ensemble Everything Everywhere" Defense
by: Zhang, Jie, et al.
Published: (2024)
by: Zhang, Jie, et al.
Published: (2024)
The Jailbreak Tax: How Useful are Your Jailbreak Outputs?
by: Nikolić, Kristina, et al.
Published: (2025)
by: Nikolić, Kristina, et al.
Published: (2025)
Similar Items
-
AutoAdvExBench: Benchmarking autonomous exploitation of adversarial example defenses
by: Carlini, Nicholas, et al.
Published: (2025) -
Privacy Side Channels in Machine Learning Systems
by: Debenedetti, Edoardo, et al.
Published: (2023) -
Are aligned neural networks adversarially aligned?
by: Carlini, Nicholas, et al.
Published: (2023) -
Evading Black-box Classifiers Without Breaking Eggs
by: Debenedetti, Edoardo, et al.
Published: (2023) -
Auditing Private Prediction
by: Chadha, Karan, et al.
Published: (2024)