AI-powered honeypots: Turning the tables on malicious AI agents

Just as AI brings time-saving advantages to our lives, it brings similar advantages to threat actors. We can take the advantage back. This blog shows how generative AI can be used to rapidly deploy adaptive honeypot systems.

Generative AI allows defenders to instantly create diverse honeypots, like Linux shells or Internet of Things (IoT) devices, using simple text prompts. This makes deploying complex, convincing deceptive environments much easier and more scalable than traditional methods.   AI-driven attacks often prioritize speed over stealth, making them highly vulnerable to being tricked by these simulated systems. This is critical because it allows defenders to catch and study automated threats that might otherwise overwhelm human teams.

  This method shifts the strategy from merely detecting attacks to actively manipulating and misleading threat actors. Organizations can safely observe attacker methodologies in real-time within a controlled "hall of mirrors. "  Ultimately, by exploiting the inherent lack of awareness in AI agents, defenders can level the playing field and turn an attacker's automation into a liability. Just as AI brings time-saving advantages to our lives, it brings similar advantages to threat actors.

The laborious, time-consuming tasks of finding potentially vulnerable systems, identifying their vulnerabilities, and executing exploit code can be automated and orchestrated using AI.   Clearly, these new capabilities put defenders at a disadvantage, as they expose new vulnerabilities for the threat actor. Attackers seek to minimize exposure.  The more that a defender knows about a potential attack, the better they can prepare to repel or detect an attack.  Using AI-orchestrated tooling to gain access to systems trades stealth for capability.

That trade-off increases attacker visibility, and increased visibility is something defenders can exploit. AI systems do not possess awareness. They generate plausible responses within a given context and set of inputs. As such they can be tricked or fooled into responding inappropriately through prompt injection or into interacting with systems that are not what they appear to be.   Honeypot systems have long been deployed as a method for gathering information about malicious activities.

 There are many software projects providing honeypots which can be installed and configured. However, the advent of generative AI systems provides us with the possibility to use AI to masquerade as vulnerable systems and allowing them to be deployed widely and with minimal effort.   In this post, I show how generative AI can be used to rapidly deploy adaptive honeypot systems.

  Getting started The implementation consists of three components: a listener that will accept network connections, a simulated vulnerability that will grant access to the attacker once triggered, and an AI framework that will respond to the attacker’s instructions.