5 papers · 1 filter
SCI-Defense: Defending Manipulation Attacks from Generative Engine Optimization
Xucheng Yu, Haibo Jin, Huimin Zeng +1
LLM-based ranking systems are vulnerable to Generative Engine Optimization (GEO) attacks, where adversaries inject semantic signals into product descriptions to artificially boost…
GUARD: Role-playing to Generate Natural-language Jailbreakings to Test Guideline Adherence of Large Language Models
Haibo Jin, Ruoxi Chen, Peiyan Zhang +2
The discovery of "jailbreaks" to bypass safety filters of Large Language Models (LLMs) and harmful responses have encouraged the community to implement safety measures. One major s…
GuardVal: Dynamic Large Language Model Jailbreak Evaluation for Comprehensive Safety Testing
Peiyan Zhang, Haibo Jin, Liying Kang +1
Jailbreak attacks reveal critical vulnerabilities in Large Language Models (LLMs) by causing them to generate harmful or unethical content. Evaluating these threats is particularly…
Robust Prompt Optimization for Defending Language Models Against Jailbreaking Attacks
Andy Zhou, Bo Li, Haohan Wang
Despite advances in AI alignment, large language models (LLMs) remain vulnerable to adversarial attacks or jailbreaking, in which adversaries can modify prompts to induce unwanted…
DistDD: Distributed Data Distillation Aggregation through Gradient Matching
Peiran Wang, Haohan Wang
In this paper, we introduce DistDD, a novel approach within the federated learning framework that reduces the need for repetitive communication by distilling data directly on clien…