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Qianheng Wang

dblp:420/7871 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2025
0009-0000-5619-8384ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Security and privacy · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Network and information security
1 paper
Authentication and access control · 30% Web and mobile security · 23% Blockchain and cryptocurrency security · 23%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Systems and software security
black-box testing
0.912025
BACScan: Automatic Black-Box Detection of Broken-Access-Control Vulnerabilities in Web Applications · CCS 2025
Authentication and access control
broken access control
0.912025
BACScan: Automatic Black-Box Detection of Broken-Access-Control Vulnerabilities in Web Applications · CCS 2025
Blockchain and cryptocurrency security › smart contract security
vulnerability detection
0.912025
BACScan: Automatic Black-Box Detection of Broken-Access-Control Vulnerabilities in Web Applications · CCS 2025
Web and mobile security
web application security
0.912025
BACScan: Automatic Black-Box Detection of Broken-Access-Control Vulnerabilities in Web Applications · CCS 2025
Authentication and access control
authorization
0.312025
BACScan: Automatic Black-Box Detection of Broken-Access-Control Vulnerabilities in Web Applications · CCS 2025

Methods — techniques the papers use, named apart from their topics

feedback-driven oracle · 0.9black-box testing · 0.9
YearPublicationVenuePosition
2025 BACScan: Automatic Black-Box Detection of Broken-Access-Control Vulnerabilities in Web Applications
abstract
Broken-Access-Control (BAC) vulnerabilities have consistently been ranked among the most critical security risks in web applications, occupying the top positions in the OWASP Top 10 over the past several years. These vulnerabilities allow attackers to bypass access control mechanisms and perform unauthorized operations, posing serious security and privacy threats to sensitive business and user data. Despite substantial attention given to BAC vulnerabilities, effective and reliable approaches to detecting these issues remain limited. In this work, we present BACScan, a novel black-box approach to detect BAC vulnerabilities in web applications. Unlike existing response similarity-based oracles that check only unauthorized read accesses, BACScan introduces an innovative feedback-driven oracle, which determines whether unauthorized read or modification operations have occurred by inferring operationally-dependent web pages and analyzing the operational feedback. We evaluated BACScan on 20 real-world applications and successfully identified 89 vulnerabilities, including 54 previously unreported ones, outperforming state-of-the-art tools. We reported all newly identified vulnerabilities to the affected vendors. To date, 35 new CVE IDs have been assigned.
Yuan Zhang 0009, Enhao Li, Wei Meng 0001, Youkun Shi, Qianheng Wang, Chenlin Wang, Min Yang 0002
CCS6