Demo Data from SIGIR 2026 / reviewers in the wild / expert
Jiazhen Zhao
dblp:290/2884
· DBLP profile ↗
1ranked-venue papers
1as first author
1since 2021 · last 2025
0000-0001-5024-1679ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 1 · 1 first-author · 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.
| Software engineering, system software, and programming languages
1 paper |
Program analysis · 77% Programming languages and type systems · 23% | |
| Network and information security
1 paper |
Systems and software security · 100% |
Topics — the 6 heaviest of 6, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Systems and software security › information flow tracking › taint analysis
taint-style vulnerability detection |
0.9 | 1 | 2025 | Yama: Precise Opcode-Based Data Flow Analysis for Detecting PHP Applications Vulnerabilities · IEEE Trans. Inf. Forensics Secur. 2025 |
Systems and software security
vulnerability discovery |
0.9 | 1 | 2025 | Yama: Precise Opcode-Based Data Flow Analysis for Detecting PHP Applications Vulnerabilities · IEEE Trans. Inf. Forensics Secur. 2025 |
Program analysis
data flow analysis |
0.9 | 1 | 2025 | Yama: Precise Opcode-Based Data Flow Analysis for Detecting PHP Applications Vulnerabilities · IEEE Trans. Inf. Forensics Secur. 2025 |
Program analysis › data flow analysis
interprocedural dataflow analysis |
0.9 | 1 | 2025 | Yama: Precise Opcode-Based Data Flow Analysis for Detecting PHP Applications Vulnerabilities · IEEE Trans. Inf. Forensics Secur. 2025 |
Programming languages and type systems › type systems
dynamic typing |
0.3 | 1 | 2025 | Yama: Precise Opcode-Based Data Flow Analysis for Detecting PHP Applications Vulnerabilities · IEEE Trans. Inf. Forensics Secur. 2025 |
Programming languages and type systems
type inference |
0.3 | 1 | 2025 | Yama: Precise Opcode-Based Data Flow Analysis for Detecting PHP Applications Vulnerabilities · IEEE Trans. Inf. Forensics Secur. 2025 |
Methods — techniques the papers use, named apart from their topics
path-sensitive analysis · 1.7opcode parsing · 1.7context-sensitive analysis · 1.7
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | Yama: Precise Opcode-Based Data Flow Analysis for Detecting PHP Applications VulnerabilitiesabstractWeb applications encompass various aspects of daily life, including online shopping, e-learning, and internet banking. Once there is a vulnerability, it can cause severe societal and economic damage. Due to its ease of use, PHP has become the preferred server-side programming language for web applications, making PHP applications a primary target for attackers. Data flow analysis is widely used for vulnerability detection before deploying web applications because of its efficiency. However, the high complexity of the PHP language makes it difficult to achieve precise data flow analysis, resulting in higher rates of false positives and false negatives in vulnerability detection. In this paper, we present Yama, a context-sensitive and path-sensitive interprocedural data flow analysis method for PHP, designed to detect taint-style vulnerabilities in PHP applications. We have found that the precise semantics and clear control flow of PHP opcodes enable data flow analysis to be more precise and efficient. Leveraging this observation, we established parsing rules for PHP opcodes and implemented a precise understanding of PHP program semantics in Yama. This enables Yama to precisely address the high complexity of the PHP language, including type inference, dynamic features, and built-in functions. We evaluated Yama from three dimensions: basic data flow analysis capabilities, complex semantic analysis capabilities, and the ability to discover vulnerabilities in real-world applications, demonstrating Yama’s advancement in vulnerability detection. Specifically, Yama possesses context-sensitive and path-sensitive interprocedural analysis capabilities, achieving a 99.1% true positive rate in complex semantic analysis experiments related to type inference, dynamic features, and built-in functions. It discovered and reported 38 zero-day vulnerabilities across 24 projects on GitHub with over 1,000 stars each, assigning 34 new CVE IDs. We have released the source code of the prototype implementation and the parsing rules for PHP opcodes to facilitate future research. Jiazhen Zhao, Kailong Zhu, Yuliang Lu |
IEEE Trans. Inf. Forensics Secur. | 1 |