ISC4DGF: Enhancing Directed Grey-box Fuzzing with LLM-Driven Initial Seed Corpus Generation

• URL: http://arxiv.org/abs/2409.14329v1
• Date: Sun, 22 Sep 2024 06:27:28 GMT
• Title: ISC4DGF: Enhancing Directed Grey-box Fuzzing with LLM-Driven Initial Seed Corpus Generation
• Authors: Yijiang Xu, Hongrui Jia, Liguo Chen, Xin Wang, Zhengran Zeng, Yidong Wang, Qing Gao, Jindong Wang, Wei Ye, Shikun Zhang, Zhonghai Wu,
• Abstract summary: directed grey-box fuzzing (DGF) has become essential, focusing on specific vulnerabilities. ISC4DGF generates optimized initial seed corpus for DGF using Large Language Models (LLMs) ISC4DGF achieved a 35.63x speedup and 616.10x fewer target reaches.
• Score: 32.6118621456906
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Fuzz testing is crucial for identifying software vulnerabilities, with coverage-guided grey-box fuzzers like AFL and Angora excelling in broad detection. However, as the need for targeted detection grows, directed grey-box fuzzing (DGF) has become essential, focusing on specific vulnerabilities. The initial seed corpus, which consists of carefully selected input samples that the fuzzer uses as a starting point, is fundamental in determining the paths that the fuzzer explores. A well-designed seed corpus can guide the fuzzer more effectively towards critical areas of the code, improving the efficiency and success of the fuzzing process. Even with its importance, many works concentrate on refining guidance mechanisms while paying less attention to optimizing the initial seed corpus. In this paper, we introduce ISC4DGF, a novel approach to generating optimized initial seed corpus for DGF using Large Language Models (LLMs). By leveraging LLMs' deep software understanding and refined user inputs, ISC4DGF creates precise seed corpus that efficiently trigger specific vulnerabilities. Implemented on AFL and tested against state-of-the-art fuzzers like AFLGo, FairFuzz, and Entropic using the Magma benchmark, ISC4DGF achieved a 35.63x speedup and 616.10x fewer target reaches. Moreover, ISC4DGF focused on more effectively detecting target vulnerabilities, enhancing efficiency while operating with reduced code coverage.

Related papers

• Generator-Based Fuzzers with Type-Based Targeted Mutation [1.4507298892594764]
  In previous work, coverage-guided fuzzers used a mix of static analysis, taint analysis, and constraint-solving approaches to address this problem. In this paper, we introduce a type-based mutation, along with constant string lookup, for Java GBF. Results compared to a baseline GBF tool show an almost 20% average improvement in application coverage, and larger improvements when third-party code is included.
  arXiv  Detail & Related papers  (2024-06-04T07:20:13Z)
• SIRST-5K: Exploring Massive Negatives Synthesis with Self-supervised Learning for Robust Infrared Small Target Detection [53.19618419772467]
  Single-frame infrared small target (SIRST) detection aims to recognize small targets from clutter backgrounds. With the development of Transformer, the scale of SIRST models is constantly increasing. With a rich diversity of infrared small target data, our algorithm significantly improves the model performance and convergence speed.
  arXiv  Detail & Related papers  (2024-03-08T16:14:54Z)
• WGAN-AFL: Seed Generation Augmented Fuzzer with Wasserstein-GAN [25.908940995229248]
  WGAN-AFL is agenerative adversarial network (GAN) that learns features and generates high-quality initial input seeds. We show that WGAN-AFL significantly outperforms the original AFL in terms of code coverage, new paths, and vulnerability discovery.
  arXiv  Detail & Related papers  (2024-01-30T12:20:21Z)
• NLP-based Cross-Layer 5G Vulnerabilities Detection via Fuzzing Generated Run-Time Profiling [4.893416946078254]
  We propose an innovative approach for automatically detecting vulnerabilities, unintended emergent behaviors, and performance degradation in 5G stacks. Piloting on srsRAN, we map the run-time profiling via Logging Information (LogInfo) generated by fuzzing test to a high dimensional metric space first. We further leverage machine learning-based classification algorithms, including Logistic Regression, K-Nearest Neighbors, and Random Forest to categorize the impacts on performance and security attributes.
  arXiv  Detail & Related papers  (2023-05-14T19:07:21Z)
• Efficient Decoder-free Object Detection with Transformers [75.00499377197475]
  Vision transformers (ViTs) are changing the landscape of object detection approaches. We propose a decoder-free fully transformer-based (DFFT) object detector. DFFT_SMALL achieves high efficiency in both training and inference stages.
  arXiv  Detail & Related papers  (2022-06-14T13:22:19Z)
• Efficient Few-Shot Object Detection via Knowledge Inheritance [62.36414544915032]
  Few-shot object detection (FSOD) aims at learning a generic detector that can adapt to unseen tasks with scarce training samples. We present an efficient pretrain-transfer framework (PTF) baseline with no computational increment. We also propose an adaptive length re-scaling (ALR) strategy to alleviate the vector length inconsistency between the predicted novel weights and the pretrained base weights.
  arXiv  Detail & Related papers  (2022-03-23T06:24:31Z)
• Anchor-free Oriented Proposal Generator for Object Detection [59.54125119453818]
  Oriented object detection is a practical and challenging task in remote sensing image interpretation. Nowadays, oriented detectors mostly use horizontal boxes as intermedium to derive oriented boxes from them. We propose a novel Anchor-free Oriented Proposal Generator (AOPG) that abandons the horizontal boxes-related operations from the network architecture.
  arXiv  Detail & Related papers  (2021-10-05T10:45:51Z)
• Camouflaged Object Segmentation with Distraction Mining [23.77915054363188]
  Camouflaged object segmentation (COS) aims to identify objects that are "perfectly" assimilate into their surroundings. We develop a bio-inspired framework, termed Positioning and Focus Network (PFNet), which mimics the process of predation in nature. Our PFNet runs in real-time (72 FPS) and significantly outperforms 18 cutting-edge models on three challenging datasets.
  arXiv  Detail & Related papers  (2021-04-21T11:47:59Z)
• Bayesian Optimization with Machine Learning Algorithms Towards Anomaly Detection [66.05992706105224]
  In this paper, an effective anomaly detection framework is proposed utilizing Bayesian Optimization technique. The performance of the considered algorithms is evaluated using the ISCX 2012 dataset. Experimental results show the effectiveness of the proposed framework in term of accuracy rate, precision, low-false alarm rate, and recall.
  arXiv  Detail & Related papers  (2020-08-05T19:29:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.