AI-based code generators have gained a fundamental role in assisting
developers in writing software starting from natural language (NL). However,
since these large language models are trained on massive volumes of data
collected from unreliable online sources (e.g., GitHub, Hugging Face), AI
models become an easy target for data poisoning attacks, in which an attacker
corrupts the training data by injecting a small amount of poison into it, i.e.,
astutely crafted malicious samples. In this position paper, we address the
security of AI code generators by identifying a novel data poisoning attack
that results in the generation of vulnerable code. Next, we devise an extensive
evaluation of how these attacks impact state-of-the-art models for code
generation. Lastly, we discuss potential solutions to overcome this threat.

基于人工智能的代码生成器在帮助开发人员从自然语言中编写软件方面起到了重要作用。本文提出了一种新颖的数据污染攻击，其影响是生成易受攻击的代码。我们对这些攻击对代码生成的最新模型的影响进行了广泛评估，并讨论了潜在的解决方案。

AI 生成代码的安全隐患：通过取消修复代码来破坏程序

Poisoning Programs by Un-Repairing Code: Security Concerns of  AI-generated Code

This systematic literature review comprehensively examines the application of
Large Language Models (LLMs) in forecasting and anomaly detection, highlighting
the current state of research, inherent challenges, and prospective future
directions. LLMs have demonstrated significant potential in parsing and
analyzing extensive datasets to identify patterns, predict future events, and
detect anomalous behavior across various domains. However, this review
identifies several critical challenges that impede their broader adoption and
effectiveness, including the reliance on vast historical datasets, issues with
generalizability across different contexts, the phenomenon of model
hallucinations, limitations within the models' knowledge boundaries, and the
substantial computational resources required. Through detailed analysis, this
review discusses potential solutions and strategies to overcome these
obstacles, such as integrating multimodal data, advancements in learning
methodologies, and emphasizing model explainability and computational
efficiency. Moreover, this review outlines critical trends that are likely to
shape the evolution of LLMs in these fields, including the push toward
real-time processing, the importance of sustainable modeling practices, and the
value of interdisciplinary collaboration. Conclusively, this review underscores
the transformative impact LLMs could have on forecasting and anomaly detection
while emphasizing the need for continuous innovation, ethical considerations,
and practical solutions to realize their full potential.

通过系统性文献综述，全面考察了在预测和异常检测中使用大规模语言模型（LLMs）的应用，突出了现有研究的现状、固有挑战和未来发展方向。LLMs 已经在解析和分析大规模数据集以识别模式、预测未来事件和检测异常行为等各个领域展现了巨大潜力。然而，该综述指出了一些关键挑战，如依赖于大量历史数据集、在不同背景中的泛化问题、模型产生幻觉的现象、模型知识范围的局限性以及需要大量计算资源等，这些挑战阻碍了它们更广泛的应用和有效性。通过详细分析，本综述讨论了克服这些障碍的潜在解决方案和策略，如整合多模态数据，学习方法的进步以及强调模型可解释性和计算效率。此外，该综述还概述了可能影响 LLMs 在这些领域中发展的关键趋势，包括朝向实时处理、可持续建模实践的重要性和跨学科合作的价值。最后，该综述强调了 LLMs 对预测和异常检测可能产生的转变性影响，同时强调了持续创新、道德考虑和实际解决方案的需求以实现其全部潜力。