1 citations · 1 across the 4 of their papers we have counts for
7 papers
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…
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…
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…
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…
SCGAgent: Recreating the Benefits of Reasoning Models for Secure Code Generation with Agentic Workflows
Rebecca Saul, Hao Wang, Koushik Sen +1
Large language models (LLMs) have seen widespread success in code generation tasks for different scenarios, both everyday and professional. However current LLMs, despite producing…
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…