Foundation Model Resource Library for Anomaly Detection: A Comprehensive Review Integrating Large Language Models and Multimodal Technologies

This article introduces the open-source resource library Awesome-Anomaly-Detection-Foundation-Models maintained by mala-lab, which integrates research papers and tool resources for anomaly detection based on large language models (LLM), vision-language models (VLM), graph foundation models, and time-series foundation models, providing a one-stop reference for researchers and engineers. Traditional anomaly detection relies on domain-specific labeled data and dedicated model architectures, making cross-domain transfer difficult; the rise of foundation models has driven a paradigm shift in this field from dedicated small models to general-purpose large models.

Anomaly detection is a core challenge in machine learning, widely applied in scenarios such as industrial quality inspection, cybersecurity, financial risk control, and medical diagnosis. Traditional methods rely on domain-specific labeled data and dedicated model architectures, making cross-domain transfer difficult. In recent years, the rise of foundation models like LLM and VLM has driven a paradigm shift in the anomaly detection field from dedicated small models to general-purpose large models. This resource library systematically organizes the latest research results of using various foundation models for anomaly detection, providing a reference guide for researchers and practitioners.

The resource library adopts the Awesome List format, classified by model type and application scenario, covering four core directions:

1. Large Language Model Applications: Used for text anomaly detection, log analysis anomaly detection, and prompt engineering to enhance traditional methods;
2. Vision-Language Model Multimodal Detection: Zero-shot/few-shot anomaly localization, anomaly detection based on natural language descriptions;
3. Graph Foundation Model Structural Anomaly Detection: Graph/node/edge-level anomaly detection, including graph Transformer and graph self-supervised learning methods;
4. Time-Series Foundation Models: Anomaly detection based on Transformer architectures (e.g., Informer, Autoformer) and large-scale time-series models (e.g., TimeGPT, Moirai).

By sorting through the content of the resource library, we find the technical trends in the anomaly detection field:

1. From Discriminative to Generative: Traditional discriminative methods like One-Class SVM are now replaced by more generative paradigms (reconstruction error/likelihood estimation) for anomaly identification;
2. Zero-Shot and Few-Shot Capabilities: Relying on pre-trained knowledge and cross-modal alignment to reduce dependence on domain-labeled data;
3. Multimodal Fusion: Combining visual and text data, structured and unstructured data to improve detection accuracy and interpretability;
4. Enhanced Interpretability: Providing natural language explanations to facilitate decision understanding in high-risk scenarios (e.g., medical diagnosis, financial risk control).

Value of the resource library for various roles:

• Researchers: Quickly understand the latest progress, find baseline methods and evaluation metrics, and avoid reinventing the wheel;
• Algorithm Engineers: Locate technical routes according to business scenarios (image/text/graph structure/time series), and refer to open-source implementations to accelerate prototype development;
• Product Managers and Decision Makers: Understand the boundaries of technical capabilities and development trends to support technology selection and product planning.

The Awesome-Anomaly-Detection-Foundation-Models resource library marks the entry of the anomaly detection field into a new era driven by foundation models. The integrated application of multiple models reshapes the anomaly detection technology stack and application paradigm, providing a systematic entry point for researchers and engineers. We look forward to the continuous improvement of foundation model capabilities and the maturity of domain adaptation technologies, which will promote the implementation of anomaly detection in more scenarios and support the intelligent transformation of various industries.