973 citations · 1.2k across the 17 of their papers we have counts for
19 papers
ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software
Xiang Mei, Jordi Del Castillo, Pulkit Singh Singaria +8
Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at t…
From Trace to Line: LLM Agent for Real-World OSS Vulnerability Localization
Haoran Xi, Minghao Shao, Brendan Dolan-Gavitt +2
Large language models show promise for vulnerability discovery, yet prevailing methods inspect code in isolation, struggle with long contexts, and focus on coarse function- or file…
CRAKEN: Cybersecurity LLM Agent with Knowledge-Based Execution
Minghao Shao, Haoran Xi, Nanda Rani +9
Large Language Model (LLM) agents can automate cybersecurity tasks and can adapt to the evolving cybersecurity landscape without re-engineering. While LLM agents have demonstrated…
D-CIPHER: Dynamic Collaborative Intelligent Multi-Agent System with Planner and Heterogeneous Executors for Offensive Security
Meet Udeshi, Minghao Shao, Haoran Xi +9
Large Language Models (LLMs) have been used in cybersecurity such as autonomous security analysis or penetration testing. Capture the Flag (CTF) challenges serve as benchmarks to a…
EnIGMA: Interactive Tools Substantially Assist LM Agents in Finding Security Vulnerabilities
Talor Abramovich, Meet Udeshi, Minghao Shao +13
Although language model (LM) agents have demonstrated increased performance in multiple domains, including coding and web-browsing, their success in cybersecurity has been limited.…
ARVO: Atlas of Reproducible Vulnerabilities for Open-Source Software
Xiang Mei, Jordi Del Castillo, Pulkit Singh Singaria +8
Achieving reproducibility, quantity, and diversity in vulnerability datasets has long been viewed as an inherent three-way trade-off, where improving one dimension often comes at t…
NYU CTF Bench: A Scalable Open-Source Benchmark Dataset for Evaluating LLMs in Offensive Security
Minghao Shao, Sofija Jancheska, Meet Udeshi +10
Large Language Models (LLMs) are being deployed across various domains today. However, their capacity to solve Capture the Flag (CTF) challenges in cybersecurity has not been thoro…
Model Cascading for Code: A Cascaded Black-Box Multi-Model Framework for Cost-Efficient Code Completion with Self-Testing
Boyuan Chen, Mingzhi Zhu, Brendan Dolan-Gavitt +2
The rapid advancement of large language models (LLMs) has significantly improved code completion tasks, yet the trade-off between accuracy and computational cost remains a critical…
An Empirical Evaluation of LLMs for Solving Offensive Security Challenges
Minghao Shao, Boyuan Chen, Sofija Jancheska +4
Capture The Flag (CTF) challenges are puzzles related to computer security scenarios. With the advent of large language models (LLMs), more and more CTF participants are using LLMs…
VeriGen: A Large Language Model for Verilog Code Generation
Shailja Thakur, Baleegh Ahmad, Hammond Pearce +4
In this study, we explore the capability of Large Language Models (LLMs) to automate hardware design by generating high-quality Verilog code, a common language for designing and mo…
(Security) Assertions by Large Language Models
Rahul Kande, Hammond Pearce, Benjamin Tan +4
The security of computer systems typically relies on a hardware root of trust. As vulnerabilities in hardware can have severe implications on a system, there is a need for techniqu…
Benchmarking Large Language Models for Automated Verilog RTL Code Generation
Shailja Thakur, Baleegh Ahmad, Zhenxing Fan +5
Automating hardware design could obviate a significant amount of human error from the engineering process and lead to fewer errors. Verilog is a popular hardware description langua…
Lost at C: A User Study on the Security Implications of Large Language Model Code Assistants
Gustavo Sandoval, Hammond Pearce, Teo Nys +3
Large Language Models (LLMs) such as OpenAI Codex are increasingly being used as AI-based coding assistants. Understanding the impact of these tools on developers' code is paramoun…
Pop Quiz! Can a Large Language Model Help With Reverse Engineering?
Hammond Pearce, Benjamin Tan, Prashanth Krishnamurthy +3
Large language models (such as OpenAI's Codex) have demonstrated impressive zero-shot multi-task capabilities in the software domain, including code explanation. In this work, we e…
Examining Zero-Shot Vulnerability Repair with Large Language Models
Hammond Pearce, Benjamin Tan, Baleegh Ahmad +2
Human developers can produce code with cybersecurity bugs. Can emerging 'smart' code completion tools help repair those bugs? In this work, we examine the use of large language mod…
Asleep at the Keyboard? Assessing the Security of GitHub Copilot's Code Contributions
Hammond Pearce, Baleegh Ahmad, Benjamin Tan +2
There is burgeoning interest in designing AI-based systems to assist humans in designing computing systems, including tools that automatically generate computer code. The most nota…
NNoculation: Catching BadNets in the Wild
Akshaj Kumar Veldanda, Kang Liu, Benjamin Tan +5
This paper proposes a novel two-stage defense (NNoculation) against backdoored neural networks (BadNets) that, repairs a BadNet both pre-deployment and online in response to backdo…
Fine-Pruning: Defending Against Backdooring Attacks on Deep Neural Networks
Kang Liu, Brendan Dolan-Gavitt, Siddharth Garg
Deep neural networks (DNNs) provide excellent performance across a wide range of classification tasks, but their training requires high computational resources and is often outsour…
BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain
Tianyu Gu, Brendan Dolan-Gavitt, Siddharth Garg
Deep learning-based techniques have achieved state-of-the-art performance on a wide variety of recognition and classification tasks. However, these networks are typically computati…