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Shuangpeng Bai

dblp:294/4945 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Systems and software security › vulnerability discovery
fuzzing
0.812024
CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024
Systems and software security › vulnerability discovery › fuzzing
kernel fuzzing
0.812024
CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024
Systems and software security
memory safety
0.812024
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
0.812024
CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024
Systems and software security
vulnerability discovery
0.812024
CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel · CCS 2024
Operating systems
kernel
0.212024
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
YearPublicationVenuePosition
2024 CountDown: Refcount-guided Fuzzing for Exposing Temporal Memory Errors in Linux Kernel
abstract
Kernel 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
CCS1
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