115 citations · 308 across the 29 of their papers we have counts for
11 papers · 1 filter
Democratizing AI: Open-source Scalable LLM Training on GPU-based Supercomputers
Siddharth Singh, Prajwal Singhania, Aditya Ranjan +9
Training and fine-tuning large language models (LLMs) with hundreds of billions to trillions of parameters requires tens of thousands of GPUs, and a highly scalable software stack.…
Coercing LLMs to do and reveal (almost) anything
Jonas Geiping, Alex Stein, Manli Shu +3
It has recently been shown that adversarial attacks on large language models (LLMs) can "jailbreak" the model into making harmful statements. In this work, we argue that the spectr…
Baseline Defenses for Adversarial Attacks Against Aligned Language Models
Neel Jain, Avi Schwarzschild, Yuxin Wen +7
As Large Language Models quickly become ubiquitous, it becomes critical to understand their security vulnerabilities. Recent work shows that text optimizers can produce jailbreakin…
On the Reliability of Watermarks for Large Language Models
John Kirchenbauer, Jonas Geiping, Yuxin Wen +7
As LLMs become commonplace, machine-generated text has the potential to flood the internet with spam, social media bots, and valueless content. Watermarking is a simple and effecti…
Tree-Ring Watermarks: Fingerprints for Diffusion Images that are Invisible and Robust
Yuxin Wen, John Kirchenbauer, Jonas Geiping +1
Watermarking the outputs of generative models is a crucial technique for tracing copyright and preventing potential harm from AI-generated content. In this paper, we introduce a no…
Hard Prompts Made Easy: Gradient-Based Discrete Optimization for Prompt Tuning and Discovery
Yuxin Wen, Neel Jain, John Kirchenbauer +3
The strength of modern generative models lies in their ability to be controlled through text-based prompts. Typical "hard" prompts are made from interpretable words and tokens, and…
A Watermark for Large Language Models
John Kirchenbauer, Jonas Geiping, Yuxin Wen +3
Potential harms of large language models can be mitigated by watermarking model output, i.e., embedding signals into generated text that are invisible to humans but algorithmically…
Thinking Two Moves Ahead: Anticipating Other Users Improves Backdoor Attacks in Federated Learning
Yuxin Wen, Jonas Geiping, Liam Fowl +4
Federated learning is particularly susceptible to model poisoning and backdoor attacks because individual users have direct control over the training data and model updates. At the…
Canary in a Coalmine: Better Membership Inference with Ensembled Adversarial Queries
Yuxin Wen, Arpit Bansal, Hamid Kazemi +4
As industrial applications are increasingly automated by machine learning models, enforcing personal data ownership and intellectual property rights requires tracing training data…
Fishing for User Data in Large-Batch Federated Learning via Gradient Magnification
Yuxin Wen, Jonas Geiping, Liam Fowl +2
Federated learning (FL) has rapidly risen in popularity due to its promise of privacy and efficiency. Previous works have exposed privacy vulnerabilities in the FL pipeline by reco…
Decepticons: Corrupted Transformers Breach Privacy in Federated Learning for Language Models
Liam Fowl, Jonas Geiping, Steven Reich +4
A central tenet of Federated learning (FL), which trains models without centralizing user data, is privacy. However, previous work has shown that the gradient updates used in FL ca…