◍wovepaper
SearchResearchersInstitutions
Sign in
researcher

Han Liu

8 papers hereh-index 5278 citations14 works total

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

author position
  • first author1
  • middle author7

Across the 8 of 8 papers where every author was matched, so the position is known.

fields
  • cs.CR5
  • cs.SE3
same name
  • Han Liu — 74 papers, h 50
  • Han Liu — 31 papers, h 11
  • Han Liu — 29 papers, h 12
  • Han Liu — 19 papers, h 22
  • Han Liu — 15 papers, h 7
  • Han Liu — 14 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
20242026
most citedStatic Application Security Testing (SAST) Tools for Smart Contracts: How Far Are We?

37 citations · 56 across the 8 of their papers we have counts for

collaborators
Showing 2026Show all

4 papers · 1 filter

cs.CR2026

Knowledge Over Parameters: Evolving Smart Contract Vulnerability Detection

Yuqiang Sun, Han Liu, Ying Li +4

Smart contract vulnerabilities are predominantly logic bugs whose detection requires structured, step-by-step procedural knowledge of attack patterns and contract semantics. Existi…

cs.CR2026

TrajAD: Trajectory Anomaly Detection for Trustworthy LLM Agents

Yibing Liu, Chong Zhang, Zhongyi Han +5

We address the problem of runtime trajectory anomaly detection, a critical capability for enabling trustworthy LLM agents. Current safety measures predominantly focus on static inp…

cs.CR2026

LogicScan: An LLM-driven Framework for Detecting Business Logic Vulnerabilities in Smart Contracts

Jiaqi Gao, Zijian Zhang, Yuqiang Sun +5

Business logic vulnerabilities have become one of the most damaging yet least understood classes of smart contract vulnerabilities. Unlike traditional bugs such as reentrancy or ar…

cs.CR2026

Do Fine-Tuned LLMs Understand Vulnerabilities? An Investigation into the Semantic Trap

Feiyang Huang, Yuqiang Sun, Fan Zhang +3

Large Language Models (LLMs) have shown promising performance in software vulnerability detection, particularly after domain-specific Supervised Fine-Tuning (SFT). However, it rema…

◍wovepaper

Papers, researchers and institutions, woven together.

Explore
  • Search
  • Researchers
  • Institutions
Account
  • Library
  • Chat
Data
  • arXiv.org
  • Semantic Scholar
  • OpenAlex
  • Latest RSS
AboutContactPrivacyDevelopersllms.txtopenapi.json
Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.