6 citations · 8 across the 5 of their papers we have counts for
5 papers
Lessons from Defending Gemini Against Indirect Prompt Injections
Chongyang Shi, Sharon Lin, Shuang Song +11
Gemini is increasingly used to perform tasks on behalf of users, where function-calling and tool-use capabilities enable the model to access user data. Some tools, however, require…
Large Language Models Can Verbatim Reproduce Long Malicious Sequences
Sharon Lin, Krishnamurthy, Dvijotham +4
Backdoor attacks on machine learning models have been extensively studied, primarily within the computer vision domain. Originally, these attacks manipulated classifiers to generat…
Defeating Prompt Injections by Design
Edoardo Debenedetti, Ilia Shumailov, Tianqi Fan +7
Large Language Models (LLMs) are increasingly deployed in agentic systems that interact with an untrusted environment. However, LLM agents are vulnerable to prompt injection attack…
Retrieval-Augmented Generation by Evidence Retroactivity in LLMs
Liang Xiao, Wen Dai, Shuai Chen +4
Retrieval-augmented generation has gained significant attention due to its ability to integrate relevant external knowledge, enhancing the accuracy and reliability of the LLMs' res…
Operationalizing Contextual Integrity in Privacy-Conscious Assistants
Sahra Ghalebikesabi, Eugene Bagdasaryan, Ren Yi +10
Advanced AI assistants combine frontier LLMs and tool access to autonomously perform complex tasks on behalf of users. While the helpfulness of such assistants can increase dramati…