activity
20172026
most citedBadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain

973 citations · 1.2k across the 17 of their papers we have counts for

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Showing 2024Show all

5 papers · 1 filter

cs.AI2024

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.…

cs.CR2024

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…

cs.CR2024★ 3 cited

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…

cs.SE2024

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…

cs.CR2024★ 6 cited

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…