Advancements in large language models (LLMs) are revolutionizing interactive
game design, enabling dynamic plotlines and interactions between players and
non-player characters (NPCs). However, LLMs may exhibit flaws such as
hallucinations, forgetfulness, or misinterpretations of prompts, causing
logical inconsistencies and unexpected deviations from intended designs.
Automated techniques for detecting such game bugs are still lacking. To address
this, we propose a systematic LLM-based method for automatically identifying
such bugs from player game logs, eliminating the need for collecting additional
data such as post-play surveys. Applied to a text-based game DejaBoom!, our
approach effectively identifies bugs inherent in LLM-powered interactive games,
surpassing unstructured LLM-powered bug-catching methods and filling the gap in
automated detection of logical and design flaws.

通过基于大型语言模型的方法，我们提出了一种自动识别互动游戏中逻辑和设计缺陷的系统化方法，该方法应用于文本游戏 DejaBoom! 上能够高效地识别大型语言模型驱动的互动游戏中的缺陷，并填补了自动检测逻辑和设计缺陷的空白。

基于 LLM 的文本游戏自动错误检测

Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs

A key challenge associated with Kubernetes configuration files (KCFs) is that
they are often highly complex and error-prone, leading to security
vulnerabilities and operational setbacks. Rule-based (RB) tools for KCF
misconfiguration detection rely on static rule sets, making them inherently
limited and unable to detect newly-discovered misconfigurations. RB tools also
suffer from misdetection, since mistakes are likely when coding the detection
rules. Recent methods for detecting and remediating KCF misconfigurations are
limited in terms of their scalability and detection coverage, or due to the
fact that they have high expertise requirements and do not offer automated
remediation along with misconfiguration detection. Novel approaches that employ
LLMs in their pipeline rely on API-based, general-purpose, and mainly
commercial models. Thus, they pose security challenges, have inconsistent
classification performance, and can be costly. In this paper, we propose
GenKubeSec, a comprehensive and adaptive, LLM-based method, which, in addition
to detecting a wide variety of KCF misconfigurations, also identifies the exact
location of the misconfigurations and provides detailed reasoning about them,
along with suggested remediation. When empirically compared with three
industry-standard RB tools, GenKubeSec achieved equivalent precision (0.990)
and superior recall (0.999). When a random sample of KCFs was examined by a
Kubernetes security expert, GenKubeSec's explanations as to misconfiguration
localization, reasoning and remediation were 100% correct, informative and
useful. To facilitate further advancements in this domain, we share the unique
dataset we collected, a unified misconfiguration index we developed for label
standardization, our experimentation code, and GenKubeSec itself as an
open-source tool.

Kubernetes 配置文件（KCF）的一个关键挑战是其通常非常复杂且容易出错，导致安全漏洞和操作上的障碍。本文介绍了一种基于 LLM（语言模型）的名为 GenKubeSec 的全面自适应方法，它不仅能检测各种 KCF 的配置错误，还能确定错误位置并提供详细的解释和建议的纠正措施。与三种行业标准的 RB（基于规则）工具相比，GenKubeSec 的精确度相当（0.990）且召回率更高（0.999）。