42 citations · 207 across the 76 of their papers we have counts for
11 papers · 1 filter
CSO-LLM: Class Subspace Orthogonalization for Post-Training Backdoor Detection and Trigger Inversion in LLMs
Zhengxing Li, David J. Miller, Guangmingmei Yang +1
While post-training backdoor detection and trigger inversion schemes have been developed for AIs used e.g. for images, there is a paucity of such methods for LLMs. First, the LLM i…
A Study of Backdoors in Instruction Fine-tuned Language Models
Jayaram Raghuram, George Kesidis, David J. Miller
Backdoor data poisoning, inserted within instruction examples used to fine-tune a foundation Large Language Model (LLM) for downstream tasks (\textit{e.g.,} sentiment prediction),…
CEPA: Consensus Embedded Perturbation for Agnostic Detection and Inversion of Backdoors
Guangmingmei Yang, Xi Li, Hang Wang +2
A variety of defenses have been proposed against Trojans planted in (backdoor attacks on) deep neural network (DNN) classifiers. Backdoor-agnostic methods seek to reliably detect a…
Post-Training Detection of Backdoor Attacks for Two-Class and Multi-Attack Scenarios
Zhen Xiang, David J. Miller, George Kesidis
Backdoor attacks (BAs) are an emerging threat to deep neural network classifiers. A victim classifier will predict to an attacker-desired target class whenever a test sample is emb…
Test-Time Detection of Backdoor Triggers for Poisoned Deep Neural Networks
Xi Li, Zhen Xiang, David J. Miller +1
Backdoor (Trojan) attacks are emerging threats against deep neural networks (DNN). A DNN being attacked will predict to an attacker-desired target class whenever a test sample from…
Detecting Backdoor Attacks Against Point Cloud Classifiers
Zhen Xiang, David J. Miller, Siheng Chen +2
Backdoor attacks (BA) are an emerging threat to deep neural network classifiers. A classifier being attacked will predict to the attacker's target class when a test sample from a s…
A Backdoor Attack against 3D Point Cloud Classifiers
Zhen Xiang, David J. Miller, Siheng Chen +2
Vulnerability of 3D point cloud (PC) classifiers has become a grave concern due to the popularity of 3D sensors in safety-critical applications. Existing adversarial attacks agains…
A Mixture Model Based Defense for Data Poisoning Attacks Against Naive Bayes Spam Filters
David J. Miller, Xinyi Hu, Zhen Xiang +1
Naive Bayes spam filters are highly susceptible to data poisoning attacks. Here, known spam sources/blacklisted IPs exploit the fact that their received emails will be treated as (…
Moving-target Defense against Botnet Reconnaissance and an Adversarial Coupon-Collection Model
Neda Nasiriani, Yuquan Shan, George Kesidis +3
We consider a cloud based multiserver system consisting of a set of replica application servers behind a set of proxy (indirection) servers which interact directly with clients ove…
Adversarial Learning: A Critical Review and Active Learning Study
David J. Miller, Xinyi Hu, Zhicong Qiu +1
This papers consists of two parts. The first is a critical review of prior art on adversarial learning, identifying some significant limitations of previous works. The second part…
Numerical Evaluation of Cloud-Side Shuffling Defenses against DDoS Attacks on Proxied Multiserver Systems
Yuquan Shan, George Kesidis, Daniel Fleck +1
We consider a cloud based multiserver system, that may be cloud based, consisting of a set of replica application servers behind a set of proxy (indirection) servers which interact…