Amar Zanashir | Searcharxiv

arXiv2025

Construction and Evaluation of LLM-based agents for Semi-Autonomous penetration testing

With the emergence of high-performance large language models (LLMs) such as GPT, Claude, and Gemini, the autonomous and semi-autonomous execution of tasks has significantly advanced across various domains. However, in highly specialized fields such as cybersecurity, full autonomy remains a challenge. This difficulty primarily stems from the limitations of LLMs in reasoning capabilities and domain-specific knowledge. We propose a system that semi-autonomously executes complex cybersecurity workflows by employing multiple LLMs modules to formulate attack strategies, generate commands, and analyze results, thereby addressing the aforementioned challenges. In our experiments using Hack The Box virtual machines, we confirmed that our system can autonomously construct attack strategies, issue appropriate commands, and automate certain processes, thereby reducing the need for manual intervention.

cs.CR↗

arXiv2024

ResBit: Residual Bit Vector for Categorical Values

One-hot vectors, a common method for representing discrete/categorical data, in machine learning are widely used because of their simplicity and intuitiveness. However, one-hot vectors suffer from a linear increase in dimensionality, posing computational and memory challenges, especially when dealing with datasets containing numerous categories. In this paper, we focus on tabular data generation, and reveal the multinomial diffusion faces the mode collapse phenomenon when the cardinality is high. Moreover, due to the limitations of one-hot vectors, the training phase takes time longer in such a situation. To address these issues, we propose Residual Bit Vectors (ResBit), a technique for densely representing categorical data. ResBit is an extension of analog bits and overcomes limitations of analog bits when applied to tabular data generation. Our experiments demonstrate that ResBit not only accelerates training but also maintains performance when compared with the situations before applying ResBit. Furthermore, our results indicate that many existing methods struggle with high-cardinality data, underscoring the need for lower-dimensional representations, such as ResBit and latent vectors.

cs.LG↗