EDBT 2026 Demo / reviewers in the wild / expert
Shuangpeng Bai
dblp:294/4945
· DBLP profile ↗
2ranked-venue papers
2as first author
2since 2021 · last 2024
0009-0005-5470-3072ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Security and privacy · 2 · 2 first-author · 2 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 |
Systems and software security · 100% | |
| Software engineering, system software, and programming languages
1 paper |
Operating systems · 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 › vulnerability discovery
fuzzing |
0.8 | 1 | 2024 | CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024 |
Systems and software security › vulnerability discovery › fuzzing
kernel fuzzing |
0.8 | 1 | 2024 | CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024 |
Systems and software security
memory safety |
0.8 | 1 | 2024 | CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024 |
Systems and software security › memory safety › memory corruption
use-after-free |
0.8 | 1 | 2024 | CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024 |
Systems and software security
vulnerability discovery |
0.8 | 1 | 2024 | CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024 |
Operating systems
kernel |
0.2 | 1 | 2024 | CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024 |
Methods — techniques the papers use, named apart from their topics
syscall relation reshaping · 1.5coverage-guided fuzzing · 1.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux KernelabstractKernel use-after-free (UAF) bugs are severe threats to system security due to their complex root causes and high exploitability.We find that 36.1% of recent kernel UAF bugs are caused by improper uses of reference counters, dubbed refcount-related UAF bugs.Current kernel fuzzing tools based on code coverage can detect common memory errors, but none of them is aware of the root cause.As a consequence, they only trigger refcount-related UAF bugs passively and coincidentally, and may miss many deep hidden vulnerabilities.To actively trigger refcount-related UAF bugs, in this paper, we propose CountDown, a novel refcount-guided kernel fuzzer.CountDown collects diverse refcount operations from kernel executions and reshapes syscall relations based on commonly accessed refcounts.When generating user-space programs, CountDown prefers to combine syscalls that ever access the same refcounts, aiming to trigger complex refcount behaviors.It also injects refcountdecreasing and refcount-accessing syscalls to intentionally free the refcounted object and trigger invalid accesses through dangling pointers.We test CountDown on mainstream Linux kernels and compare it with popular fuzzers.On average, our tool can detect 66.1% more UAF bugs and 32.9% more KASAN reports than stateof-the-art tools.CountDown has found nine new kernel memory bugs, where two are fixed and one is confirmed. Shuangpeng Bai, Zhechang Zhang, Hong Hu 0004 |
CCS | 1 |
| 2021 | DSS: Discrepancy-Aware Seed Selection Method for ICS Protocol Fuzzing
Shuangpeng Bai, Hui Wen 0001, Dongliang Fang, Puzhuo Liu, Limin Sun 0001 |
ACNS (2) | 1 |