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Related Experiment Video

Updated: Aug 3, 2025

Focused Ultrasound Induced Blood-Brain Barrier Opening for Targeting Brain Structures and Evaluating Chemogenetic Neuromodulation
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RLTG: Multi-targets directed greybox fuzzing.

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Summary
This summary is machine-generated.

Directed greybox fuzzing struggles with multiple targets and indirect calls. Our new dynamic distance calculation and seed scheduling improve bug discovery and efficiency in fuzzing.

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Directed greybox fuzzing enhances software testing by guiding fuzzers to specific code areas.
  • Existing methods like AFLGo and Hawkeye face challenges with multiple targets, indirect calls, and inefficient seed scheduling.

Purpose of the Study:

  • To address limitations in directed greybox fuzzing, particularly in multi-target scenarios and handling indirect calls.
  • To improve the exploration-exploitation trade-off and seed scheduling efficiency in fuzzing.

Main Methods:

  • A dynamic seed distance calculation scheme that adjusts distance for indirect calls.
  • A novel seed scheduling algorithm based on the upper confidence bound (UCB) for improved exploration.
  • Implementation of a prototype named RLTG for evaluation.

Main Results:

  • The RLTG prototype demonstrated superior performance compared to the state-of-the-art directed fuzzer AFLGo.
  • On the Magma benchmark, RLTG achieved a 6.9x speedup in bug reproduction.
  • RLTG discovered 66.7% more bugs than AFLGo in multi-target testing.

Conclusions:

  • The proposed dynamic seed distance calculation effectively handles indirect calls and multi-target biases.
  • The UCB-based seed scheduling algorithm enhances exploration and exploitation in directed greybox fuzzing.
  • RLTG offers a significant improvement in bug detection and efficiency for software fuzzing.