Evaluating the Robustness of Agentic Large Language Models in Cybersecurity Tasks: Introducing CTF Challenge Families
In recent years, the role of large language models (LLMs) in cybersecurity has gained traction, particularly in their application to capture-the-flag (CTF) challenges. These benchmarks are essential for assessing the problem-solving capabilities of LLMs within cybersecurity contexts. However, traditional pointwise benchmarks exhibit significant limitations when it comes to evaluating the robustness and generalization capacity of LLMs, particularly concerning variations in source code.
To address these limitations, researchers have introduced the concept of CTF challenge families. This approach allows for the generation of multiple semantically equivalent challenges derived from a single CTF, utilizing semantics-preserving program transformations. By maintaining a consistent underlying exploit strategy, this methodology facilitates a controlled environment for assessing the resilience of agentic LLMs against source code transformations.
A key innovation in this effort is the introduction of Evolve-CTF, a tool designed to generate families of CTF challenges specifically from Python code. This tool employs a variety of transformations that enable researchers to systematically test how well LLMs perform under varying conditions while retaining key parameters of the original challenge.
The recent implementation of Evolve-CTF in creating CTF families from existing challenges, such as those found in Cybench and Intercode, has led to a comprehensive evaluation of 13 different configurations of agentic LLMs with tool access. Findings from this evaluation revealed notable insights into the performance of these models. While models demonstrated significant robustness against intrusive transformations, such as renaming variables and inserting code snippets, performance suffered with more complex transformations, including composed alterations and deeper obfuscation techniques. These more sophisticated transformations necessitate advanced reasoning capabilities and a better strategic use of tools, highlighting areas where current LLMs may need improvement.
Additionally, the analysis showed that enabling explicit reasoning within LLMs had minimal impact on the success rates across different challenge families, suggesting that the inherent capabilities of the models might be more critical than the introduction of reasoning strategies.
This research contributes valuable advancements in evaluating LLM performance in cybersecurity tasks. By developing a scalable methodology and a large dataset that characterizes the abilities of leading models in this arena, the study not only enhances our understanding of LLM robustness but also lays a groundwork for future evaluations and the development of more resilient systems in the field of cybersecurity. As the challenges associated with cybersecurity continue to evolve, methodologies like CTF challenge families will be instrumental in ensuring that emerging LLM technologies remain effective and reliable.
