Approximate Bayesian Computation As An Informed Fuzzing-Inference System (2404.04303v2)
Abstract: The power of fuzz testing lies in its random, often brute-force, generation and execution of inputs to trigger unexpected behaviors and vulnerabilities in software applications. However, given the reality of infinite possible input sequences, pursuing all test combinations would not only be computationally expensive, but practically impossible. Approximate Bayesian Computation (ABC), a form of Bayesian simulation, represents a novel, probabilistic approach to addressing this problem. The parameter space for working with these types of problems is effectively infinite, and the application of these techniques is untested in relevant literature. We use a relaxed, manual implementation of two ABC methods, a Sequential Monte Carlo (SMC) simulation, and a Markov Chain Monte Carlo (MCMC) simulation. We found promising results with the SMC posterior and mixed results with MCMC posterior distributions on our white-box fuzz-test function.
- M. Sunnåker, A. Busetto, E. Numminen, J. Corander, M. Foll, and C. Dessimoz, “Approximate bayesian computation,” PLoS computational biology, vol. 9, no. 1, p. e1002803, 2013.
- J. Lintusaari, M. U. Gutmann, R. Dutta, S. Kaski, and J. Corander, “Fundamentals and Recent Developments in Approximate Bayesian Computation,” Systematic Biology, vol. 66, pp. e66–e82, 09 2016.
- M. Boehme, C. Cadar, and A. ROYCHOUDHURY, “Fuzzing: Challenges and reflections,” IEEE Software, vol. 38, no. 3, pp. 79–86, 2021.
- “Libuzzer – a library for coverage-guided fuzz testing.,” 2023. https://llvm.org/docs/LibFuzzer.html.
- C. Barrett, L. de Moura, and A. Stump, “Smt-comp: Satisfiability modulo theories competition,” in Computer Aided Verification (K. Etessami and S. K. Rajamani, eds.), (Berlin, Heidelberg), pp. 20–23, Springer Berlin Heidelberg, 2005.
- A. Paszke, S. Gross, F. Massa, A. Lerer, J. Bradbury, G. Chanan, T. Killeen, Z. Lin, N. Gimelshein, L. Antiga, A. Desmaison, A. Kopf, E. Yang, Z. DeVito, M. Raison, A. Tejani, S. Chilamkurthy, B. Steiner, L. Fang, J. Bai, and S. Chintala, “Pytorch: An imperative style, high-performance deep learning library,” in Advances in Neural Information Processing Systems 32, pp. 8024–8035, Curran Associates, Inc., 2019.
- P. Boonstoppel, C. Cadar, and D. Engler, “Rwset: Attacking path explosion in constraint-based test generation,” in Tools and Algorithms for the Construction and Analysis of Systems (C. R. Ramakrishnan and J. Rehof, eds.), (Berlin, Heidelberg), pp. 351–366, Springer Berlin Heidelberg, 2008.
Sponsor
Paper Prompts
Sign up for free to create and run prompts on this paper using GPT-5.
Top Community Prompts
Collections
Sign up for free to add this paper to one or more collections.