FuSeBMC v4: Improving code coverage with smart seeds via BMC, fuzzing and static analysis

• URL: http://arxiv.org/abs/2206.14068v4
• Date: Thu, 18 Apr 2024 12:40:04 GMT
• Title: FuSeBMC v4: Improving code coverage with smart seeds via BMC, fuzzing and static analysis
• Authors: Kaled M. Alshmrany, Mohannad Aldughaim, Ahmed Bhayat, Lucas C. Cordeiro,
• Abstract summary: FuSeBMC v4 is a test generator that synthesizes seeds with useful properties. FuSeBMC works by first analyzing and incrementally injecting goal labels into the given C program.
• Score: 2.792964753261107
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Bounded model checking (BMC) and fuzzing techniques are among the most effective methods for detecting errors and security vulnerabilities in software. However, there are still shortcomings in detecting these errors due to the inability of existent methods to cover large areas in target code. We propose FuSeBMC v4, a test generator that synthesizes seeds with useful properties, that we refer to as smart seeds, to improve the performance of its hybrid fuzzer thereby achieving high C program coverage. FuSeBMC works by first analyzing and incrementally injecting goal labels into the given C program to guide BMC and Evolutionary Fuzzing engines. After that, the engines are employed for an initial period to produce the so-called smart seeds. Finally, the engines are run again, with these smart seeds as starting seeds, in an attempt to achieve maximum code coverage / find bugs. During both seed generation and normal running, coordination between the engines is aided by the Tracer subsystem. This subsystem carries out additional coverage analysis and updates a shared memory with information on goals covered so far. Furthermore, the Tracer evaluates test cases dynamically to convert cases into seeds for subsequent test fuzzing. Thus, the BMC engine can provide the seed that allows the fuzzing engine to bypass complex mathematical guards (e.g., input validation). As a result, we received three awards for participation in the fourth international competition in software testing (Test-Comp 2022), outperforming all state-of-the-art tools in every category, including the coverage category.

Related papers

• FOX: Coverage-guided Fuzzing as Online Stochastic Control [13.3158115776899]
  Fuzzing is an effective technique for discovering software vulnerabilities by generating random test inputs executing them against the target program. This paper addresses the limitations of existing coverage-guided fuzzers, focusing on the scheduler and mutator components. We present FOX, a proof-of-concept implementation of our control-theoretic approach, and compare it to industry-standard fuzzers.
  arXiv  Detail & Related papers  (2024-06-06T21:21:05Z)
• Fuzzing BusyBox: Leveraging LLM and Crash Reuse for Embedded Bug Unearthing [2.4287247817521096]
  Vulnerabilities in BusyBox can have far-reaching consequences. The study revealed the prevalence of older BusyBox versions in real-world embedded products. We introduce two techniques to fortify software testing.
  arXiv  Detail & Related papers  (2024-03-06T17:57:03Z)
• Zero-Shot Detection of Machine-Generated Codes [83.0342513054389]
  This work proposes a training-free approach for the detection of LLMs-generated codes. We find that existing training-based or zero-shot text detectors are ineffective in detecting code. Our method exhibits robustness against revision attacks and generalizes well to Java codes.
  arXiv  Detail & Related papers  (2023-10-08T10:08:21Z)
• Think Twice before Driving: Towards Scalable Decoders for End-to-End Autonomous Driving [74.28510044056706]
  Existing methods usually adopt the decoupled encoder-decoder paradigm. In this work, we aim to alleviate the problem by two principles. We first predict a coarse-grained future position and action based on the encoder features. Then, conditioned on the position and action, the future scene is imagined to check the ramification if we drive accordingly.
  arXiv  Detail & Related papers  (2023-05-10T15:22:02Z)
• General Cutting Planes for Bound-Propagation-Based Neural Network Verification [144.7290035694459]
  We generalize the bound propagation procedure to allow the addition of arbitrary cutting plane constraints. We find that MIP solvers can generate high-quality cutting planes for strengthening bound-propagation-based verifiers. Our method is the first verifier that can completely solve the oval20 benchmark and verify twice as many instances on the oval21 benchmark.
  arXiv  Detail & Related papers  (2022-08-11T10:31:28Z)
• Masked Autoencoders for Generic Event Boundary Detection CVPR'2022 Kinetics-GEBD Challenge [11.823891739821443]
  Generic Event Boundary Detection (GEBD) tasks aim at detecting generic, taxonomy-free event boundaries that segment a whole video into chunks. In this paper, we apply Masked Autoencoders to improve algorithm performance on the GEBD tasks. With our approach, we achieved 85.94% on the F1-score on the Kinetics-GEBD test set, which improved the F1-score by 2.31% compared to the winner of the 2021 Kinetics-GEBD Challenge.
  arXiv  Detail & Related papers  (2022-06-17T08:10:27Z)
• Knowledge Removal in Sampling-based Bayesian Inference [86.14397783398711]
  When single data deletion requests come, companies may need to delete the whole models learned with massive resources. Existing works propose methods to remove knowledge learned from data for explicitly parameterized models. In this paper, we propose the first machine unlearning algorithm for MCMC.
  arXiv  Detail & Related papers  (2022-03-24T10:03:01Z)
• FuSeBMC v.4: Smart Seed Generation for Hybrid Fuzzing [0.9379652654427957]
  FuSeBMC is a test generator for finding security vulnerabilities in C programs. This paper introduces a new version that utilizes both engines to produce smart seeds. We significantly increased our code coverage score from last year, outperforming all tools that participated in this year's competition in every single category.
  arXiv  Detail & Related papers  (2021-12-20T15:41:57Z)
• Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection [67.53296659361598]
  adversarial EXEmples can bypass machine learning-based detection by perturbing relatively few input bytes. We develop a unifying framework that does not only encompass and generalize previous attacks against machine-learning models, but also includes three novel attacks. These attacks, named Full DOS, Extend and Shift, inject the adversarial payload by respectively manipulating the DOS header, extending it, and shifting the content of the first section.
  arXiv  Detail & Related papers  (2020-08-17T07:16:57Z)
• MEUZZ: Smart Seed Scheduling for Hybrid Fuzzing [21.318110758739675]
  Machine learning-Enhanced hybrid fUZZing system (MEUZZ) MEUZZ determines which new seeds are expected to produce better fuzzing yields based on the knowledge learned from past seed scheduling decisions. Results: MEUZZ significantly outperforms the state-of-the-art grey-box and hybrid fuzzers.
  arXiv  Detail & Related papers  (2020-02-20T05:02:25Z)
• EHSOD: CAM-Guided End-to-end Hybrid-Supervised Object Detection with Cascade Refinement [53.69674636044927]
  We present EHSOD, an end-to-end hybrid-supervised object detection system. It can be trained in one shot on both fully and weakly-annotated data. It achieves comparable results on multiple object detection benchmarks with only 30% fully-annotated data.
  arXiv  Detail & Related papers  (2020-02-18T08:04:58Z)

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.