Honeyval: A Comprehensive Evaluation Framework for LLM-powered HTTP Honeypots

Researchers introduce Honeyval, a comprehensive evaluation framework for testing LLM-powered HTTP honeypots against AI-driven attackers. The framework addresses scalability and reproducibility gaps in existing honeypot evaluations, revealing that LLM-based honeypots substantially outperform rule-based systems in engagement duration while remaining difficult to detect, though trade-offs exist between interaction length and detection evasion.

Honeyval represents a critical advancement in cybersecurity evaluation methodology, addressing a gap between rapid LLM adoption in defensive systems and the lack of standardized assessment protocols. Traditional honeypot testing relies on manual evaluation or fixed command response similarity measurements, approaches that fail to capture the complexity of real-world adversarial interactions. This framework grounds evaluation in 16 backend applications and employs AI hacking agents as attackers, creating reproducible scenarios that better simulate practical threats than existing methods.

The research emerges as organizations increasingly explore LLM-powered security tools to reduce deployment costs while maintaining defensive capabilities. LLMs offer advantages for honeypot development by enabling high-interaction systems with minimal underlying infrastructure risk, a significant benefit for resource-constrained security teams. However, without rigorous evaluation frameworks, widespread adoption risks deploying systems with unknown capabilities and limitations.

Honeyval's findings carry practical implications for security teams and AI developers. The discovery that LLM honeypots maintain longer interaction times than rule-based alternatives while remaining undetected by frontier models suggests viable cost-efficiency gains. Simultaneously, the identified trade-offs between engagement duration and detection risk indicate that honeypot configuration requires careful consideration of defensive objectives. Organizations cannot assume one LLM configuration suits all threat scenarios.

Looking ahead, standardized evaluation frameworks like Honeyval will likely become essential for validating AI-powered security tools before production deployment. As LLMs become commodity components in defensive infrastructure, rigorous benchmarking separates genuinely effective systems from marketing narratives. The framework's adaptability across configurations positions it as a foundation for ongoing research into counter-offensive capabilities and attacker-honeypot dynamics.

• →Honeyval provides the first standardized evaluation framework for LLM-powered honeypots, addressing reproducibility and scalability gaps in existing testing methods.
• →LLM-powered honeypots achieve substantially longer attacker interactions compared to rule-based systems while remaining difficult for frontier AI models to detect.
• →Trade-offs exist between honeypot interaction duration and detection vulnerability, requiring careful configuration based on specific defensive objectives.
• →The framework uses AI hacking agents as attackers and grounds evaluation in 16 backend applications, creating more realistic adversarial scenarios.
• →Cost efficiency advantages of LLM honeypots persist against agentic attackers, making them viable for resource-constrained security operations.