fuzzing
Resource usage fuzzing samples and related data. Contains samples from Pythoin, random data, GPT-3.5, GPT-4, Gemini-1.0, Claude Instant, and Claude Opus. These samples are generated for 50 Python functions. Also included are resource measures for CPU time, instruction count, function calls, peak RAM usage, final RAM allocated, and coverage. These values were collected on an isolated system and account for interference from other processes.
- Categories:
73 Views
This dataset comprises over 38,000 seed inputs generated from a range of Large Language Models (LLMs), including ChatGPT-3.5, ChatGPT-4, Claude-Opus, Claude-Instant, and Gemini Pro 1.0, specifically designed for the application in fuzzing Python functions. These seeds were produced as part of a study evaluating the utility of LLMs in automating the creation of effective fuzzing inputs, a method crucial for uncovering software defects in the Python programming environment where traditional methods show limitations.
- Categories:
182 Views
This dataset was collected from an industrial control system running the Modbus protocol. It is used to train a deep adversarial learning model. This model is used to generate fuzzing data in the same format as the real one. The data is a sequence of hexadecimal numbers. The followed generated data is produced by the already trained model.
- Categories:
713 Views