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Muhammad Shafique

13 papers hereh-index 6238 citations13 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author2
  • last author9

Across the 11 of 13 papers where every author was matched, so the position is known.

fields
  • cs.CR5
  • cs.SE3
  • cs.AI1
  • cs.AR1
  • cs.CL1
  • cs.CV1
same name
  • Muhammad Shafique — 65 papers, h 13
  • Muhammad Shafique — 52 papers, h 22
  • Muhammad Shafique — 22 papers, h 10
  • Muhammad Shafique — 18 papers, h 10
  • Muhammad Shafique — 15 papers, h 3
  • Muhammad Shafique — 14 papers, h 4

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20242026
most citedAn Empirical Evaluation of LLMs for Solving Offensive Security Challenges

6 citations · 10 across the 11 of their papers we have counts for

collaborators
Showing 2024Show all

3 papers · 1 filter

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.