Large Language Models are Few-shot Generators: Proposing Hybrid Prompt Algorithm To Generate Webshell Escape Samples
CoRR(2024)
摘要
The frequent occurrence of cyber-attacks has made webshell attacks and
defense gradually become a research hotspot in the field of network security.
However, the lack of publicly available benchmark datasets and the
over-reliance on manually defined rules for webshell escape sample generation
have slowed down the progress of research related to webshell escape sample
generation strategies and artificial intelligence-based webshell detection
algorithms. To address the drawbacks of weak webshell sample escape
capabilities, the lack of webshell datasets with complex malicious features,
and to promote the development of webshell detection technology, we propose the
Hybrid Prompt algorithm for webshell escape sample generation with the help of
large language models. As a prompt algorithm specifically developed for
webshell sample generation, the Hybrid Prompt algorithm not only combines
various prompt ideas including Chain of Thought, Tree of Thought, but also
incorporates various components such as webshell hierarchical module and
few-shot example to facilitate the LLM in learning and reasoning webshell
escape strategies. Experimental results show that the Hybrid Prompt algorithm
can work with multiple LLMs with excellent code reasoning ability to generate
high-quality webshell samples with high Escape Rate (88.61
VIRUSTOTAL detection engine) and Survival Rate (54.98
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