AIJack: Let's Hijack AI! Security and Privacy Risk Simulator for Machine Learning
This paper introduces AIJack, an open-source library designed to assess security and privacy risks associated with the training and deployment of machine learning models. Amid the growing interest in big data and AI, advancements in machine learning research and business are accelerating. However, recent studies reveal potential threats, such as the theft of training data and the manipulation of models by malicious attackers. Therefore, a comprehensive understanding of machine learning's security and privacy vulnerabilities is crucial for the safe integration of machine learning into real-world products. AIJack aims to address this need by providing a library with various attack and defense methods through a unified API. The library is publicly available on GitHub (https://github.com/Koukyosyumei/AIJack).
Code (1)
Methods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
HijackRAG: Hijacking Attacks against Retrieval-Augmented Large Language Models
Retrieval-Augmented Generation (RAG) systems enhance large language models (LLMs) by integrating external knowledge, making them adaptable and cost-effective for various applications. However, the growing reliance on the…
RAGRetrievalRetrieval-augmented GenerationF-RBA: A Federated Learning-based Framework for Risk-based Authentication
The proliferation of Internet services has led to an increasing need to protect private data. User authentication serves as a crucial mechanism to ensure data security. Although robust authentication forms the cornerston…
Anomaly DetectionFeature EngineeringFederated LearningUnsupervised Anomaly DetectionQuantifying Conversation Drift in MCP via Latent Polytope
The Model Context Protocol (MCP) enhances large language models (LLMs) by integrating external tools, enabling dynamic aggregation of real-time data to improve task execution. However, its non-isolated execution context …
Feature Space Hijacking Attacks against Differentially Private Split Learning
Split learning and differential privacy are technologies with growing potential to help with privacy-compliant advanced analytics on distributed datasets. Attacks against split learning are an important evaluation tool a…
Dimensionality ReductionSnatchML: Hijacking ML models without Training Access
Model hijacking can cause significant accountability and security risks since the owner of a hijacked model can be framed for having their model offer illegal or unethical services. Prior works consider model hijacking a…