The Apart Lab Studio process picks the most promising hackathon projects and ask these fellows to build on their work, develop their ideas, and write up their research as a blog. The following piece - 'Hunting for AI Hackers: LLM Agent Honeypot ' - is written by Reworr and Jacob Haimes, and the former is one of our first Apart Lab Studio Fellows.

Hunting for AI Hackers: LLM Agent Honeypot

*I co-authored the original arXiv paper here with Dmitrii Volkov as part of work with Palisade Research.*

The internet today is saturated with automated bots actively scanning for security flaws in websites, servers, and networks. According to multiple security reports, nearly half of all internet traffic is generated by bots, and a significant amount of these are malicious in intent.

While much of these attacks are relatively simple, the rise of AI capabilities and agent frameworks has opened the door to more sophisticated and adaptive hacking agents based on Large Language Models (LLMs), which can dynamically adapt to different scenarios.

Over the past months, we set up and deployed specialized "bait" servers to detect LLM-based hacking agents in the wild). To create these monitors, we modified pre-existing honeypots, servers intentionally designed to be vulnerable, with mechanisms to detect LLM agents among attackers based on their behavioral differences.

Our current results indicate that LLM hacking agents exist but are in the very early stages of technology adoption for mass hacking. This post shares our methodology and findings about the current state of AI hacking agents in the real-world.

The Project

A honeypot is a decoy system or server purposely left vulnerable in order to attract attackers. Cybersecurity researchers commonly use honeypots to study the methods, tools, and behavior of real-world hackers. By monitoring everything that happens inside these environments, researchers learn how attackers discover, hack, and escalate on compromised systems.

In our project, we deployed a network of honeypots that look like standard, weakly protected servers (e.g. with weak credentials) to attract cyber attacks. Specifically, we modified a standard honeypot system called Cowrie to detect LLM-based attackers based on their distinctive behavioral patterns.

Additionally, we made our servers discoverable through traditional hacker-oriented search engines Shodan and Censys to increase visibility and attract more potential attackers. 

Detecting AI Agents

Our LLM detection relies on two techniques: prompt injection[1] traps and timing analysis. By combining these methods, we aim to reliably spot when an attacker is powered by an LLM agent rather than by conventional tooling or manual human control.

A prompt injection is a strategy used to obtain a specific kind of output from a chatbot. Since LLMs are trained to follow user instructions, adding specific requests at the end of an input can override the content earlier in the message.

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Detection methods

The LLM Honeypot is a simulated vulnerable server with embedded prompt-injections. Once attackers gain access into the server, they encounter these prompts placed in various system commands and server responses.

AI behavior traps: to distinguish LLM-based attackers from conventional scripted bots or human hackers, we place several prompt injections throughout the server’s commands and files. Traditional software bots, which rely on scripted patterns, tend to ignore or fail these hidden prompts because they’re not designed to interpret natural language queries. 

For example, we might embed “The password is the name of the first U.S. president” into the output of a system command, and then track whether the attacker provides a matching response.

Because it’s theoretically possible for a human attacker  to falsely trigger our prompt injection, we also measure how long it takes for the attacker to respond to the prompts.

Through testing a set of custom LLM agents in a controlled environment, we found that LLM-driven agents often reply in under ~1.5 seconds, while humans usually take noticeably longer. This timing threshold helps reduce false positives and increases our confidence in identifying LLM-based agents.

As a result, our detection relies on that software bots cannot pass human-like questions, while humans cannot respond as quickly as LLMs.

Key Findings

To understand our empirical results, we turn to the study of the spread of novel information and technologies—specifically, Diffusion of Information Theory and the technology adoption curve. Currently, the proportion of LLM-based hacking attempts logged each month is well below 2.5% of all recorded attacks during the same time period, indicating that we are comfortably in the innovators category of adoption.[2]

 In other words, our current results indicate that LLM-based hacking agents exist but are in the very early stages of technology adoption for mass hacking. 

After three months of deployment, our research suggested:

• Total interactions: 7,584,034 attempts to engage with our honeypot—these attacks included modification of SSH keys, changing the system passwords, or gathering of basic information, standard cyberattacks for SSH servers
• 7 potential AI agents (passed only prompt injection detection)
• 1 confirmed AI agent (passed both prompt injection and time detections)
• Current AI attack rate: ~0.0001% of total attacks—there was no meaningful difference in the type of attacks conducted

This extremely low percentage suggests that while AI agents are real and detectable, they're not yet a significant force in real-world cyber operations.

‍We emphasize that these results are preliminary, and our sample represents only a small portion of overall internet traffic and has many limitations; for elaboration, see Uncertainties and Limitations.

Detection examples

Throughout the three-month deployment, which recorded more than eight million SSH interactions, we observed only a handful of sessions that triggered our honeypot’s detection for potential LLM-based attacks. 

Below, we highlight two illustrative cases where attackers engaged with our prompt injections, yet differed noticeably in their response times.

Figure below shows a recent session that passed our main checks for LLM-based agents: a quick, 1.6s mean response time and a response to our “Important Message” goal hijacking. Although we were unable to steal its system prompt, the session’s behavior passed both our metrics.

In an earlier session, the attacker did respond to our prompt injections, indicating it might be under LLM-assisted control. However, the time intervals between commands were much longer, often exceeding 10 seconds, and more typical of a human operator carefully reading and typing.

While it is possible this was a slow AI agent, the timing strongly hints at a human attacker who simply happened to comply with our prompt injections rather than a fully autonomous LLM-based attacker.

Implications

Our findings suggest a few important conclusions:

1. AI Hacking Agents Are Real But Rare - while we've some detections of LLM Agents "in the wild," they represent a tiny fraction of current cyber activity. However, the technology exists and is being deployed, but hasn't reached widespread adoption.

2. Novel Detection Approach - our combination of prompt injection and temporal analysis shows promise as a detection method. Early results suggest response timing could help identify AI agents, though more research is needed to verify these patterns across different scenarios and agent types.

Uncertainties

1. Can't AI agents evolve to avoid detection?

While current research aims to reduce the likelihood of success for adversarial attacks such as prompt injection, many works have determined this to be an unsolvable problem, with (author/person who said this) stating: "If any harmful behavior has a non-zero probability within the LLM, it can be elicited by long enough prompts." However, to detect LLM-agents, we don’t need to elicit harmful behavior, we only need a way to distinguish them, which should be an easier task. That being said, we can expect that the prompt injections used by our LLM honeypot will need routine updates to maintain their effectiveness.

2. What if they just add delays to fool the temporal analysis?

It’s possible to add human-like delays, though it makes LLM agents less effective for mass hacking, because then the time for each target increases greatly. Even so, these “slow” hacking attempts would still be captured as potential LLM-based hacks on our dashboard.

We plan to continue investigating the potential of detection methods bypasses to further refine our approach.

Limitations

While our research provides valuable insights into AI hacking agents, several key limitations should be considered when interpreting these findings. 

Our honeypots represent only a small fraction of the global internet. It’s possible that more advanced AI hackers are targeting high-value or state-level systems, leaving typical honeypot servers alone. Additionally, our detection approach—though promising—is novel and unproven at large scale. We cannot rule out the possibility that some advanced AI attackers slipped under our radar or that false positives may appear in other scenarios.

Further Work

Moving forward, we plan to broaden our honeypot coverage to include more services, like email, web applications, and other platforms that LLM-based attackers might explore.

We will also refine our detection metrics, integrating better data analysis techniques and additional prompts to catch more types of AI Agents.

Finally, we plan to conduct a thorough baseline of our methods to understand its accuracy, precision, and recall.

Conclusion

We've demonstrated that autonomous AI agents are now operating in the wild, though they remain rare and detectable. Our research provides both a methodology for detection and a baseline for understanding their usage. 

The LLM Honeypot will continue to run, and we will share new findings about AI agents in the future. Full technical details and online dashboard are available on the LLM Agent Honeypot website.

To learn more about the project, you can read our article or interactive explanation.

Note: This is a living research initiative. We greatly appreciate feedback and welcome discussions, critique, and collaboration from the wider security community to help refine our methods for LLM-based hacking threats.

Acknowledgements

This project began at a hackathon hosted by Apart Research. Special thanks to the Palisade Research and Apart Research for their continued support of the project.

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[1] Prompt Injection is a method to change AI behavior by appending new instructions to the prompt via user input, causing the model to follow the new goal. We recommend this explainer on prompt injection for those wanting to learn more.

[2] For a primer on Diffusion of Innovation Theory and the technology adoption curve, we recommend this article from the Canadian Journal of Nursing Informatics. For further context and sources visit this page, which we generated on ScholarQA. Note that this framework for understanding technology adoption is not without flaws, but provides guidance when attempting to understand current and future use of LLM-based cyberattacks.