Search Results for author:
Found 5 papers, 0 papers with code
A New Era in LLM Security: Exploring Security Concerns in Real-World LLM-based Systems
Large Language Model (LLM) systems are inherently compositional, with individual LLM serving as the core foundation with additional layers of objects such as plugins, sandbox, and so on.
WIPI: A New Web Threat for LLM-Driven Web Agents
To evaluate the effectiveness of the proposed methodology, we conducted extensive experiments using 7 plugin-based ChatGPT Web Agents, 8 Web GPTs, and 3 different open-source Web Agents.
Exploring the Limits of ChatGPT in Software Security Applications
Large language models (LLMs) have undergone rapid evolution and achieved remarkable results in recent times.
DeceptPrompt: Exploiting LLM-driven Code Generation via Adversarial Natural Language Instructions
In this paper, we introduce DeceptPrompt, a novel algorithm that can generate adversarial natural language instructions that drive the Code LLMs to generate functionality correct code with vulnerabilities.
Towards Efficient Data-Centric Robust Machine Learning with Noise-based Augmentation
The data-centric machine learning aims to find effective ways to build appropriate datasets which can improve the performance of AI models.
