241 citations · 717 across the 23 of their papers we have counts for
23 papers
The Ultimate Cookbook for Invisible Poison: Crafting Subtle Clean-Label Text Backdoors with Style Attributes
Wencong You, Daniel Lowd
Backdoor attacks on text classifiers can cause them to predict a predefined label when a particular "trigger" is present. Prior attacks often rely on triggers that are ungrammatica…
MaPPing Your Model: Assessing the Impact of Adversarial Attacks on LLM-based Programming Assistants
John Heibel, Daniel Lowd
LLM-based programming assistants offer the promise of programming faster but with the risk of introducing more security vulnerabilities. Prior work has studied how LLMs could be ma…
Large Language Models Are Better Adversaries: Exploring Generative Clean-Label Backdoor Attacks Against Text Classifiers
Wencong You, Zayd Hammoudeh, Daniel Lowd
Backdoor attacks manipulate model predictions by inserting innocuous triggers into training and test data. We focus on more realistic and more challenging clean-label attacks where…
Provable Robustness Against a Union of Adversarial Attacks
Zayd Hammoudeh, Daniel Lowd
Sparse or adversarial attacks arbitrarily perturb an unknown subset of the features. robustness analysis is particularly well-suited for heterogeneous (tabular) d…
Training Data Influence Analysis and Estimation: A Survey
Zayd Hammoudeh, Daniel Lowd
Good models require good training data. For overparameterized deep models, the causal relationship between training data and model predictions is increasingly opaque and poorly und…
TCAB: A Large-Scale Text Classification Attack Benchmark
Kalyani Asthana, Zhouhang Xie, Wencong You +4
We introduce the Text Classification Attack Benchmark (TCAB), a dataset for analyzing, understanding, detecting, and labeling adversarial attacks against text classifiers. TCAB inc…