70 citations · 154 across the 8 of their papers we have counts for
8 papers
Incalmo: An Autonomous LLM-assisted System for Red Teaming Multi-Host Networks
Brian Singer, Keane Lucas, Lakshmi Adiga +3
Security operators use red teams to simulate real attackers and proactively find defense gaps. In realistic enterprise settings, this involves executing multi-host network attacks…
Group-based Robustness: A General Framework for Customized Robustness in the Real World
Weiran Lin, Keane Lucas, Neo Eyal +3
Machine-learning models are known to be vulnerable to evasion attacks that perturb model inputs to induce misclassifications. In this work, we identify real-world scenarios where t…
Randomness in ML Defenses Helps Persistent Attackers and Hinders Evaluators
Keane Lucas, Matthew Jagielski, Florian Tramèr +2
It is becoming increasingly imperative to design robust ML defenses. However, recent work has found that many defenses that initially resist state-of-the-art attacks can be broken…
RS-Del: Edit Distance Robustness Certificates for Sequence Classifiers via Randomized Deletion
Zhuoqun Huang, Neil G. Marchant, Keane Lucas +3
Randomized smoothing is a leading approach for constructing classifiers that are certifiably robust against adversarial examples. Existing work on randomized smoothing has focused…
Any-Play: An Intrinsic Augmentation for Zero-Shot Coordination
Keane Lucas, Ross E. Allen
Cooperative artificial intelligence with human or superhuman proficiency in collaborative tasks stands at the frontier of machine learning research. Prior work has tended to evalua…
Constrained Gradient Descent: A Powerful and Principled Evasion Attack Against Neural Networks
Weiran Lin, Keane Lucas, Lujo Bauer +2
We propose new, more efficient targeted white-box attacks against deep neural networks. Our attacks better align with the attacker's goal: (1) tricking a model to assign higher pro…
Malware Makeover: Breaking ML-based Static Analysis by Modifying Executable Bytes
Keane Lucas, Mahmood Sharif, Lujo Bauer +2
Motivated by the transformative impact of deep neural networks (DNNs) in various domains, researchers and anti-virus vendors have proposed DNNs for malware detection from raw bytes…
xBD: A Dataset for Assessing Building Damage from Satellite Imagery
Ritwik Gupta, Richard Hosfelt, Sandra Sajeev +6
We present xBD, a new, large-scale dataset for the advancement of change detection and building damage assessment for humanitarian assistance and disaster recovery research. Natura…