5 citations · 5 across the 3 of their papers we have counts for
8 papers · 1 filter
SecOPD: Mitigating Adaptive Prompt Injections by On-Policy Distillation
Yibo Peng, Long Lian, David Wagner +1
Prompt injection is listed as the \#1 threat to AI agents. When an agent accesses external data from websites, files, or emails, an attacker may inject a prompt into the data, sayi…
Meta SecAlign: A Secure Foundation LLM Against Prompt Injection Attacks
Sizhe Chen, Arman Zharmagambetov, David Wagner +1
Prompt injection attacks, where untrusted data contains an injected prompt to manipulate the system, have been listed as the top security threat to LLM-integrated applications. Mod…
Defending Against Prompt Injection with DataFilter
Yizhu Wang, Sizhe Chen, Raghad Alkhudair +2
When large language model (LLM) agents are increasingly deployed to automate tasks and interact with untrusted external data, prompt injection emerges as a significant security thr…
SeedAIchemy: LLM-Driven Seed Corpus Generation for Fuzzing
Aidan Wen, Norah A. Alzahrani, Jingzhi Jiang +5
We introduce SeedAIchemy, an automated LLM-driven corpus generation tool that makes it easier for developers to implement fuzzing effectively. SeedAIchemy consists of five modules…
Semantic-Aware Parsing for Security Logs
Julien Piet, Vivian Fang, Rishi Khare +4
Security logs are foundational to threat detection and post-incident investigation, yet analysts often struggle to fully leverage them due to their heterogeneity and unstructured n…
Defending Against Prompt Injection With a Few DefensiveTokens
Sizhe Chen, Yizhu Wang, Nicholas Carlini +2
When large language model (LLM) systems interact with external data to perform complex tasks, a new attack, namely prompt injection, becomes a significant threat. By injecting inst…