Automated Generation of Deceptive Honeytokens using Large Language Models

Core Concepts

Large Language Models can be effectively utilized to automatically generate a wide variety of convincing honeytokens, which can serve as deceptive security mechanisms to detect and deter cyber attacks.

Abstract

The paper investigates the use of Large Language Models (LLMs) for the automated generation of honeytokens, which are false pieces of information designed to lure and expose unauthorized access attempts.
The authors first identified 7 different types of honeytokens that can be generated, including configuration files, databases, log files, and honeywords (fake passwords). They then developed a modular approach to construct prompts for LLMs, using 4 distinct building blocks: generator instructions, user input, special instructions, and output format.
To quantitatively evaluate the performance, the authors focused on two honeytoken types - robots.txt files and honeywords. For robots.txt, they compared the generated files against a dataset of the top 1000 websites, assessing factors like the number of allow/disallow entries and path segments. For honeywords, they used a tool to measure the flatness (distinguishability) of the generated passwords compared to real ones.
The results show that LLMs, particularly GPT-3.5 and GPT-4, are capable of generating a wide variety of convincing honeytokens across different domains. The authors found that the optimal prompt structure varied across different LLMs, and that honeywords generated by GPT-3.5 were less distinguishable from real passwords compared to previous methods.
Overall, the work demonstrates the potential of leveraging generic LLMs to automatically create deceptive honeytokens, which can enhance cyber security defenses by luring and exposing attackers.

Stats

The robots.txt files of the top 1000 websites had an average of 10.27 ± 35.13 allow entries and 76.35 ± 228.98 disallow entries.
The honeywords generated by GPT-3.5 had a 15.15% success rate in being distinguished from real passwords, compared to 29.29% for previous methods.

Quotes

"The findings of this work demonstrate that generic LLMs are capable of creating a wide array of honeytokens using the presented prompt structures."
"Honeywords generated by GPT-3.5 were found to be less distinguishable from real passwords compared to previous methods of automated honeyword generation."

Key Insights Distilled From

Act as a Honeytoken Generator! An Investigation into Honeytoken Generation with Large Language Models

by Daniel Reti,... at arxiv.org 04-26-2024

https://arxiv.org/pdf/2404.16118.pdf

Deeper Inquiries

How can the honeytoken generation process be further automated and scaled to create large-scale deception systems?

The honeytoken generation process can be further automated and scaled by leveraging the capabilities of Large Language Models (LLMs) to generate a wide variety of honeytokens. One approach to automate and scale the process is to develop specialized prompts and building blocks that can be easily customized for different types of honeytokens. By creating a systematic approach to prompt generation, it becomes possible to efficiently generate a large number of honeytokens across various categories such as configuration files, databases, log files, and more.
Additionally, the use of automation tools and scripts can streamline the process of generating honeytokens in bulk. These tools can interact with LLMs to generate multiple honeytokens simultaneously, saving time and effort in the creation of deceptive content. Furthermore, the automation process can be integrated with existing security systems to automatically deploy honeytokens across networks and systems, creating a comprehensive deception strategy at scale.
To scale the generation of honeytokens, it is essential to continuously refine and optimize the prompts and building blocks based on feedback and evaluation results. By iteratively improving the prompt structures and incorporating new data sources and training datasets, the quality and diversity of generated honeytokens can be enhanced. This iterative approach ensures that the deception system remains effective and adaptive to evolving threats.

What are the potential ethical and legal considerations around the use of LLMs for generating deceptive content?

The use of Large Language Models (LLMs) for generating deceptive content, such as honeytokens, raises several ethical and legal considerations that need to be carefully addressed. Some of the key considerations include:

Misinformation and Deception: Using LLMs to create deceptive content can potentially mislead individuals and organizations, leading to unintended consequences. It is essential to ensure that the generated content is used responsibly and ethically to prevent harm or misinformation.

Privacy and Data Protection: Generating deceptive content using LLMs may involve processing sensitive or personal data. It is crucial to adhere to data protection regulations and ensure that privacy rights are respected when creating and deploying honeytokens.

Transparency and Accountability: The use of LLMs for deception should be transparent, and the generated content should be clearly identified as artificial or deceptive. Organizations using honeytokens should be accountable for the consequences of deploying deceptive content.

Security Implications: Deceptive content created using LLMs should not compromise the security of systems or networks. It is important to conduct thorough testing and validation to ensure that honeytokens do not inadvertently expose vulnerabilities or create security risks.

Regulatory Compliance: Organizations using LLMs for generating deceptive content must comply with relevant laws and regulations governing cybersecurity, data protection, and deceptive practices. It is essential to ensure that the use of honeytokens aligns with legal requirements and industry standards.

Bias and Fairness: LLMs may exhibit biases in their outputs based on the training data they have been exposed to. Organizations should be mindful of potential biases in the generated content and take steps to mitigate any discriminatory or unfair outcomes.

Addressing these ethical and legal considerations requires a comprehensive approach that prioritizes transparency, accountability, data protection, and compliance with regulations to ensure the responsible use of LLMs for generating deceptive content.

How can the techniques developed in this work be applied to other security domains beyond honeytokens, such as generating synthetic network traffic or creating decoy systems?

The techniques developed in this work for generating honeytokens using Large Language Models (LLMs) can be applied to other security domains beyond honeytokens, such as generating synthetic network traffic or creating decoy systems. Some ways in which these techniques can be extended to other security domains include:

Synthetic Network Traffic Generation: By adapting the prompt structures and building blocks used for honeytoken generation, LLMs can be trained to generate synthetic network traffic patterns. This synthetic traffic can be used to simulate various types of network activities, such as communication between devices, data transfers, and network protocols, to test the resilience of network security systems.

Decoy System Creation: LLMs can be utilized to create decoy systems that mimic real production environments to deceive potential attackers. Prompt structures can be designed to generate fake system configurations, log files, user accounts, and other components of a decoy system. These decoy systems can be deployed alongside actual systems to divert and confuse attackers, providing an additional layer of defense.

Phishing Campaign Generation: LLMs can be trained to generate phishing emails, websites, and social media posts to simulate phishing attacks. By developing specialized prompts and building blocks for phishing content, organizations can proactively test their employees' awareness and response to phishing attempts, enhancing cybersecurity awareness and training.

Malware Analysis and Generation: LLMs can be used to analyze and generate malware samples for research and cybersecurity testing purposes. By feeding malware-related prompts to LLMs, organizations can gain insights into malware behavior, characteristics, and potential vulnerabilities, aiding in the development of effective malware detection and prevention strategies.

Overall, the techniques developed for honeytoken generation can be adapted and extended to various security domains to enhance cybersecurity defenses, test security systems, and improve incident response capabilities. By leveraging the power of LLMs, organizations can create sophisticated and realistic security scenarios to bolster their overall cybersecurity posture.