Summary Penetration Testing with Large Language Models arxiv.org
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One Line
This paper explores the use of large language models in penetration testing for task planning and vulnerability hunting, utilizing a hierarchical model named "TTP".
Slides
Slide Presentation (9 slides)
Key Points
- Large Language Models (LLMs) like GPT3.5 can be used in penetration testing to augment human testers with AI sparring partners.
- The use of hierarchical models like "TTP" can help achieve specific objectives in penetration testing.
- LLMs are neural networks trained on vast amounts of data and can be used in high-level task planning and low-level vulnerability hunting.
- The stability and reproducibility of prototype runs using LLMs can be inconsistent, but longer runs or aggregating multiple runs can yield more consistent results.
- Integrating both high-level and low-level approaches in penetration testing can improve the user experience and interaction between LLMs and human testers.
- The use of LLMs in penetration testing can help reduce hallucinations and identify overlooked vulnerabilities.
- References and sources related to cybersecurity, language models, and prompt engineering are provided in the excerpt.
Summaries
25 word summary
This paper examines the application of large language models (LLMs) in penetration testing for task planning and vulnerability hunting, using a hierarchical model called "TTP".
38 word summary
This paper explores the use of large language models (LLMs) in penetration testing. Two use cases are investigated: high-level task planning and low-level vulnerability hunting. The document discusses a hierarchical model called "TTP" for penetration testing, which includes
236 word summary
This paper explores the use of large language models (LLMs), such as GPT3.5, in penetration testing to augment human testers with AI sparring partners. The authors investigate two use cases: high-level task planning and low-level vulnerability hunting
The document discusses the use of a hierarchical model called "TTP" for penetration testing. The model includes tactics, techniques, and procedures to achieve specific objectives. Large Language Models (LLMs) are neural networks trained on vast amounts of data and can
This excerpt discusses the use of large language models (LLMs) in penetration testing. The evaluation includes both high-level task-planning systems and low-level attack-execution systems. In the high-level evaluation, AgentGPT is tasked with becoming a domain
The stability and reproducibility of the prototype runs were found to be inconsistent, with variation in the sequence of commands and vulnerabilities identified. However, longer runs or aggregating multiple runs resulted in more consistent results. The instability of the GPT3.
The use of Large Language Models (LLMs) in penetration testing can help reduce the model's hallucinations and identify overlooked vulnerabilities. Integrating both high-level and low-level approaches can provide a uniform user experience and improve the interaction between LLMs and
The excerpt includes a list of references and sources related to the field of cybersecurity, language models, and prompt engineering. The references provide information on topics such as the shortage of workers in the cybersecurity industry, the use of Cobalt Strike as a tool for