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Towards Path-Aware Coverage-Guided Fuzzing

delete2026-01-01
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PRE
AI
G
Giacomo Priamo *
D
Daniele Cono D’Elia
M
Mathias Payer
L
Leonardo Querzoni
DOI:10.1109/CGO68049.2026.11395191delete
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Abstract

Abstract

En 中文
Automated fuzz testing is now standard practice, yet key blind spots persist. Coverage-guided fuzzers typically rely on edge coverage as a lightweight proxy for program behavior. However, this metric captures path variations only weakly: it cannot differentiate executions that follow distinct control-flow paths but traverse the same edges-causing many path-dependent bugs to go undetected. Path awareness would offer a richer coverage view but has been considered too costly for fuzzing. We introduce a lightweight method for tracking intraprocedural execution paths, enabling efficient path-aware feedback. This enhances the fuzzer's ability to detect subtle bugs, even in well-tested software. To counter the resulting seed explosion, we evaluate two strategies-culling and opportunistic path-aware fuzzing-that balance precision and throughput. Our findings show that path-aware fuzzing, when properly guided, uncovers more bugs and reveals untapped potential in fuzzing research.
Keywords:
Fuzzing
Coverage feedback
Path profiling

Journal

2
2026 IEEE/ACM INTERNATIONAL SYMPOSIUM ON CODE GENERATION AND OPTIMIZATION, CGO
IF:
0
Papers:
48
Citations:
0

Organization

S
swiss federal institutes of technology domain
Scholars:
9.0W
Papers: 8.0W
Citations: 163
S
sapienza university rome
Scholars:
6.3W
Papers: 4.7W
Citations: 381
Cited Papers

Cited Papers

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Now You See Me
err2018-12-03
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errOAAI
errFranck de Goër; Sanjay Rawat; Dennis Andriesse; Herbert Bos; Roland Groz
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SLIME: program-sensitive energy allocation for fuzzing
err2022-07-18
err0
PREAI
errChenyang Lyu; Hong Liang; Shouling Ji; Xuhong Zhang; Binbin Zhao; Meng Han; Yun Li; Zhe Wang; Wenhai Wang; Raheem Beyah
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