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-world deployment. These methods are often not scalable for development, reproducible across evaluat
The increasing sophistication and adoption of LLMs in cybersecurity applications necessitate robust and standardized evaluation frameworks to ensure their efficacy and security. This research addresses a critical gap emerging from accelerated LLM integration into defensive technologies.
This development allows for better-defined security postures against evolving cyber threats, standardizing the defensive utility of LLM-powered honeypots and preventing misallocation of resources on ineffective solutions. For strategic readers, it highlights the maturation of AI-driven cybersecurity tools and the importance of verifiable performance.
The introduction of a unified evaluation framework for LLM-powered honeypots transforms how these critical defensive tools are developed, deployed, and trusted, moving from ad-hoc assessments to standardized, scalable, and reproducible testing methodologies. This enhances defensive capabilities against sophisticated cyber threats.
- · Cybersecurity companies
- · Organizations using honeypots
- · Developers of defensive AI models
- · Cyber attackers
- · Vendors of unverified cybersecurity solutions
Improved detection and mitigation of cyber threats by more effective LLM-powered honeypots.
Increased trust and adoption of AI-driven defensive cybersecurity solutions across various sectors.
A potential arms race where attackers use AI to bypass AI-powered defenses, leading to continuous innovation in both offensive and defensive AI.
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Read at arXiv cs.LG