BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer
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| Main Authors: | , , , , |
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| Format: | Preprint |
| Published: |
2023
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| _version_ | 1866914961559650304 |
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| author | Jha, Piyush Scott, Joseph Ganeshna, Jaya Sriram Singh, Mudit Ganesh, Vijay |
| author_facet | Jha, Piyush Scott, Joseph Ganeshna, Jaya Sriram Singh, Mudit Ganesh, Vijay |
| contents | We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL) based fuzzer aimed at finding security vulnerabilities for Web applications. BertRLFuzzer works as follows: given a set of seed inputs, the fuzzer performs grammar-adhering and attack-provoking mutation operations on them to generate candidate attack vectors. The key insight of BertRLFuzzer is the use of RL with a BERT model as an agent to guide the fuzzer to efficiently learn grammar-adhering and attack-provoking mutation operators. In order to establish the efficacy of BertRLFuzzer we compare it against a total of 13 black box and white box fuzzers over a benchmark of 9 victim websites with over 16K LOC. We observed a significant improvement relative to the nearest competing tool in terms of time to first attack (54% less), new vulnerabilities found (17 new vulnerabilities), and attack rate (4.4% more attack vectors generated). |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2305_12534 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer Jha, Piyush Scott, Joseph Ganeshna, Jaya Sriram Singh, Mudit Ganesh, Vijay Software Engineering Cryptography and Security Machine Learning We present a novel tool BertRLFuzzer, a BERT and Reinforcement Learning (RL) based fuzzer aimed at finding security vulnerabilities for Web applications. BertRLFuzzer works as follows: given a set of seed inputs, the fuzzer performs grammar-adhering and attack-provoking mutation operations on them to generate candidate attack vectors. The key insight of BertRLFuzzer is the use of RL with a BERT model as an agent to guide the fuzzer to efficiently learn grammar-adhering and attack-provoking mutation operators. In order to establish the efficacy of BertRLFuzzer we compare it against a total of 13 black box and white box fuzzers over a benchmark of 9 victim websites with over 16K LOC. We observed a significant improvement relative to the nearest competing tool in terms of time to first attack (54% less), new vulnerabilities found (17 new vulnerabilities), and attack rate (4.4% more attack vectors generated). |
| title | BertRLFuzzer: A BERT and Reinforcement Learning Based Fuzzer |
| topic | Software Engineering Cryptography and Security Machine Learning |
| url | https://arxiv.org/abs/2305.12534 |