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researcher

Qifan Wang

4 papers hereh-index 2194 citations4 works total

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

author position
  • middle author3

Across the 3 of 4 papers where every author was matched, so the position is known.

fields
  • cs.CR2
  • cs.CL1
  • cs.LG1
same name
  • Qifan Wang — 24 papers, h 18
  • Qifan Wang — 21 papers, h 9
  • Qifan Wang — 18 papers, h 9
  • Qifan Wang — 17 papers, h 8
  • Qifan Wang — 12 papers, h 6
  • Qifan Wang — 11 papers, h 7

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
20232026
most citedMART: Improving LLM Safety with Multi-round Automatic Red-Teaming

4 citations · 4 across the 4 of their papers we have counts for

collaborators

4 papers

cs.CR2026

TrEEStealer: Stealing Decision Trees via Enclave Side Channels

Jonas Sander, Anja Rabich, Nick Mahling +5

Today, machine learning is widely applied in sensitive, security-related, and financially lucrative applications. Model extraction attacks undermine current business models where a…

cs.LG2026

CORA: Conformal Risk-Controlled Agents for Safeguarded Mobile GUI Automation

Yushi Feng, Junye Du, Qifan Wang +5

Graphical user interface (GUI) agents powered by vision language models (VLMs) are rapidly moving from passive assistance to autonomous operation. However, this unrestricted action…

cs.CR2025

SIRAJ: Diverse and Efficient Red-Teaming for LLM Agents via Distilled Structured Reasoning

Kaiwen Zhou, Ahmed Elgohary, A S M Iftekhar +1

The ability of LLM agents to plan and invoke tools exposes them to new safety risks, making a comprehensive red-teaming system crucial for discovering vulnerabilities and ensuring…

cs.CL2023★ 4 cited

MART: Improving LLM Safety with Multi-round Automatic Red-Teaming

Suyu Ge, Chunting Zhou, Rui Hou +5

Red-teaming is a common practice for mitigating unsafe behaviors in Large Language Models (LLMs), which involves thoroughly assessing LLMs to identify potential flaws and addressin…

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