Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots
📰 ArXiv cs.AI
Learn how to evaluate LLM-powered HTTP honeypots using Honeyval, a comprehensive framework for assessing their effectiveness against cyber attacks
Action Steps
- Build a Honeyval framework using LLMs and HTTP honeypots to evaluate their effectiveness
- Configure the framework to simulate various cyber attack scenarios
- Test the framework using real-world attack data to assess its performance
- Apply the evaluation results to improve the LLM-powered honeypot's defense capabilities
- Compare the performance of different LLM-powered honeypots using the Honeyval framework
Who Needs to Know This
Security researchers and developers of LLM-powered honeypots can benefit from this framework to evaluate and improve their systems' performance and defense capabilities
Key Insight
💡 Honeyval provides a unified evaluation framework for LLM-powered honeypots, enabling defenders to construct high-interaction honeypots with low system security risks
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🚀 Introducing Honeyval: a comprehensive evaluation framework for LLM-powered HTTP honeypots 🚀
Key Takeaways
Learn how to evaluate LLM-powered HTTP honeypots using Honeyval, a comprehensive framework for assessing their effectiveness against cyber attacks
Full Article
Title: Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots
Abstract:
arXiv:2605.29963v1 Announce Type: cross Abstract: Honeypots are decoy systems mimicking real system components designed to defend against cyber attacks. Recently, LLMs increasingly serve as simulation backbones for honeypots. They enable defenders to construct high-interaction honeypots with low system security risks. However, LLM-powered honeypot development lacks a unified evaluation framework. Most evaluations consist of measuring response similarity on fixed commands, manual testing, or real
Abstract:
arXiv:2605.29963v1 Announce Type: cross Abstract: Honeypots are decoy systems mimicking real system components designed to defend against cyber attacks. Recently, LLMs increasingly serve as simulation backbones for honeypots. They enable defenders to construct high-interaction honeypots with low system security risks. However, LLM-powered honeypot development lacks a unified evaluation framework. Most evaluations consist of measuring response similarity on fixed commands, manual testing, or real
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