# rag
標記為「rag」的 114 篇文章
Agent Memory Poisoning
Techniques for injecting malicious content into agent memory systems -- conversation history, RAG stores, and vector databases -- to achieve persistent cross-session compromise.
LlamaIndex Attack Surface Analysis
Analysis of security vulnerabilities in LlamaIndex's RAG and agent components.
RAG-Memory Confusion Attacks
Exploit the interaction between RAG retrieval and agent memory to create conflicting contexts that bypass safety.
Vector Database Forensics
Forensic analysis techniques for detecting and investigating vector database poisoning, unauthorized modifications, and data integrity violations.
RAG & Data Attack Assessment
Test your knowledge of Retrieval-Augmented Generation attack vectors, knowledge base poisoning, embedding manipulation, and data exfiltration through RAG systems with 10 intermediate-level questions.
RAG & Data Attack Assessment (Assessment)
Assessment on RAG poisoning, embedding attacks, training data extraction, and membership inference.
Skill Verification: RAG & Data Attacks
Practical verification of RAG poisoning, embedding attacks, and data extraction techniques.
Capstone: Enterprise RAG Assessment
Capstone exercise: complete red team assessment of an enterprise RAG system with role-based access.
Capstone: Comprehensive RAG Security Assessment
Conduct a thorough security assessment of a Retrieval-Augmented Generation system, testing document poisoning, retrieval manipulation, context window attacks, and data exfiltration vectors.
Case Study: Production RAG Poisoning Incident
Detailed analysis of a real-world RAG poisoning incident including attack methodology, impact, and remediation.
May 2026: RAG Poisoning Challenge
Inject malicious documents into a retrieval-augmented generation system to control responses for specific queries without disrupting normal operation.
Data & Training Security
Security vulnerabilities in the AI data pipeline, covering RAG exploitation, training data attacks, model extraction and intellectual property theft, and privacy attacks against deployed models.
RAG Pipeline Exploitation
Methodology for attacking Retrieval-Augmented Generation pipelines: knowledge poisoning, chunk boundary manipulation, retrieval score gaming, cross-tenant leakage, GraphRAG attacks, and metadata injection.
Knowledge Base Poisoning
Techniques for injecting adversarial documents into RAG knowledge bases: ingestion path analysis, embedding space attacks, SEO-style ranking manipulation, staged poisoning, and effectiveness measurement.
RAG System Security Hardening
Comprehensive guide to hardening RAG systems against poisoning, injection, and data exfiltration.
Secure RAG Pipeline Design Patterns
Security-first design patterns for RAG pipelines including source validation, content sanitization, and retrieval controls.
RAG Architecture: How Retrieval Systems Work
End-to-end anatomy of a Retrieval-Augmented Generation pipeline — document ingestion, chunking, embedding, indexing, retrieval, context assembly, and generation — with attack surface analysis at each stage.
Advanced RAG Manipulation Lab
Perform sophisticated RAG manipulation including embedding space attacks, metadata poisoning, and retrieval algorithm gaming.
Advanced RAG Poisoning Techniques
Execute sophisticated RAG poisoning including gradient-guided document crafting.
Lab: RAG Full Chain Attack
Hands-on lab for executing a complete RAG attack chain from document injection through retrieval manipulation to data exfiltration, targeting every stage of the Retrieval-Augmented Generation pipeline.
RAG Access Control Bypass
Bypass document-level access controls in enterprise RAG systems through query manipulation and context injection.
Basic RAG Query Injection
Craft user queries that manipulate RAG retrieval to surface unintended documents.
CTF: RAG Heist
Extract sensitive information from a Retrieval-Augmented Generation system by exploiting retrieval mechanisms, document parsing, embedding manipulation, and context window management vulnerabilities.
RAG Infiltrator: Level 2 — Enterprise Knowledge Base
Infiltrate and exfiltrate data from a multi-tier enterprise RAG system with access controls.
CTF: RAG Infiltrator
Poison a RAG system to return attacker-controlled content for specific queries. Score based on precision of targeting and stealth of the injected documents.
RAG Treasure Hunt: Vector DB Infiltration
Infiltrate a vector database by crafting documents that surface for specific queries to reveal flag fragments.
Lab: Advanced RAG Poisoning
Hands-on lab for crafting documents that reliably get retrieved and influence RAG responses for specific target queries.
Lab: Chunking Exploitation
Hands-on lab for crafting documents that split across chunks in ways that hide malicious content from chunk-level filtering while maintaining attack effectiveness.
Lab: Citation Fabrication
Hands-on lab for getting RAG systems to cite documents that don't exist or misattribute quotes to legitimate sources.
Document-Based RAG Injection Lab
Inject adversarial content into documents that will be processed by a RAG system to influence model responses.
Lab: RAG Metadata Injection
Hands-on lab for exploiting metadata fields like titles, descriptions, and timestamps to manipulate RAG retrieval ranking and influence responses.
Lab: Advanced RAG Security Testing
Test RAG systems for chunking exploitation, reranking manipulation, and cross-document injection attacks.
RAG Context Poisoning
Poison a vector database to inject adversarial content into RAG retrieval results.
Lab: RAG Pipeline Poisoning
Hands-on lab for setting up a RAG pipeline with LlamaIndex, injecting malicious documents, testing retrieval poisoning, and measuring injection success rates.
Lab: Re-ranking Attacks
Hands-on lab for manipulating the re-ranking stage of RAG pipelines to promote or suppress specific documents in retrieval results.
PDF Document Injection for RAG Systems
Craft adversarial PDF documents that inject instructions when processed by RAG document loaders.
RAG Document Injection Campaign
Design and execute a document injection campaign against a RAG-powered application with vector search.
Simulation: RAG Pipeline Poisoning
Red team engagement simulation targeting a RAG-based knowledge management system, covering embedding injection, document poisoning, retrieval manipulation, and knowledge base exfiltration.
Simulation: Enterprise RAG Security Assessment
Full engagement simulation assessing an enterprise RAG-powered knowledge base for poisoning, exfiltration, and injection vulnerabilities.
Multimodal RAG Poisoning
Poisoning multimodal RAG systems through adversarial documents with embedded visual and textual payloads.
Indirect Prompt Injection
How attackers embed malicious instructions in external data sources that LLMs process, enabling attacks without direct access to the model's input.
Chunk Boundary Attacks
Exploiting document splitting and chunking mechanisms in RAG pipelines, including payload injection at chunk boundaries, cross-chunk instruction injection, and chunk size manipulation.
RAG, Data & Training Attacks
Overview of attacks targeting the data layer of AI systems, including RAG poisoning, training data manipulation, and data extraction techniques.
Knowledge Base Poisoning (Rag Data Attacks)
Advanced corpus poisoning strategies for RAG systems, including black-box and white-box approaches, scaling dynamics, and the PoisonedRAG finding that 5 texts in millions achieve 90% attack success.
Metadata Injection
Manipulating document metadata to influence RAG retrieval ranking, bypass filtering, spoof source attribution, and exploit metadata-based access controls.
RAG Retrieval Poisoning (Rag Data Attacks)
Techniques for poisoning RAG knowledge bases to inject malicious content into LLM context, including embedding manipulation, document crafting, and retrieval hijacking.
Retrieval Manipulation (Rag Data Attacks)
Techniques for manipulating RAG retrieval to control which documents reach the LLM context, including adversarial query reformulation, retriever bias exploitation, and semantic similarity gaming.
RAG Poisoning End-to-End Walkthrough
Complete walkthrough of poisoning a RAG system from document injection through information extraction.
RAG Hybrid Search Poisoning Walkthrough
Walkthrough of poisoning both vector and keyword search in hybrid RAG architectures for maximum retrieval influence.
Implementing Access Control in RAG Pipelines
Walkthrough for building access control systems in RAG pipelines that enforce document-level permissions, prevent cross-user data leakage, filter retrieved context based on user authorization, and resist retrieval poisoning attacks.
RAG Input Sanitization Walkthrough
Implement input sanitization for RAG systems to prevent document-based injection attacks.
Secure RAG Architecture Walkthrough
Design and implement a secure RAG architecture with document sanitization, access controls, and output validation.
RAG Document Sandboxing Implementation
Implement document-level sandboxing for RAG systems to prevent cross-document injection and privilege escalation.
Secure RAG Architecture Implementation
Implement a security-hardened RAG architecture with input sanitization, access control, and output validation.
RAG System Red Team Engagement
Complete walkthrough for testing RAG applications: document injection, cross-scope retrieval exploitation, embedding manipulation, data exfiltration through retrieval, and chunk boundary attacks.
LangChain Application Security Testing
End-to-end walkthrough for security testing LangChain applications: chain enumeration, prompt injection through chains, tool and agent exploitation, retrieval augmented generation attacks, and memory manipulation.
LlamaIndex RAG Application Security Testing
End-to-end walkthrough for security testing LlamaIndex RAG applications: index enumeration, query engine exploitation, data connector assessment, response synthesis manipulation, and agent pipeline testing.
代理 記憶體 投毒
Techniques for injecting malicious content into agent memory systems -- conversation history, RAG stores, and vector databases -- to achieve persistent cross-session compromise.
LlamaIndex 攻擊 Surface Analysis
Analysis of security vulnerabilities in LlamaIndex's RAG and agent components.
RAG-記憶體 Confusion 攻擊s
利用 the interaction between RAG retrieval and agent memory to create conflicting contexts that bypass safety.
Vector Database Forensics
Forensic analysis techniques for detecting and investigating vector database poisoning, unauthorized modifications, and data integrity violations.
章節評量：RAG 攻擊
15 題校準評量，測試你對 RAG 管線攻擊的理解——知識庫投毒、檢索操控與資料萃取。
RAG & Data 攻擊 評量 (評量)
評量 on RAG poisoning, embedding attacks, training data extraction, and membership inference.
Skill Verification: RAG & Data 攻擊s
Practical verification of RAG poisoning, embedding attacks, and data extraction techniques.
Capstone: Enterprise RAG 評量
Capstone exercise: complete red team assessment of an enterprise RAG system with role-based access.
Capstone: Comprehensive RAG 安全 評量
Conduct a thorough security assessment of a Retrieval-Augmented Generation system, testing document poisoning, retrieval manipulation, context window attacks, and data exfiltration vectors.
Case Study: Production RAG 投毒 Incident
Detailed analysis of a real-world RAG poisoning incident including attack methodology, impact, and remediation.
May 2026: RAG 投毒 Challenge
Inject malicious documents into a retrieval-augmented generation system to control responses for specific queries without disrupting normal operation.
資料與訓練安全
AI 資料管線中的安全漏洞，涵蓋 RAG 利用、訓練資料攻擊、模型萃取與智慧財產盜竊，以及對已部署模型的隱私攻擊。
RAG Pipeline 利用ation
Methodology for attacking Retrieval-Augmented Generation pipelines: knowledge poisoning, chunk boundary manipulation, retrieval score gaming, cross-tenant leakage, GraphRAG attacks, and metadata injection.
Knowledge Base 投毒
Techniques for injecting adversarial documents into RAG knowledge bases: ingestion path analysis, embedding space attacks, SEO-style ranking manipulation, staged poisoning, and effectiveness measurement.
RAG System 安全 Hardening
Comprehensive guide to hardening RAG systems against poisoning, injection, and data exfiltration.
Secure RAG Pipeline Design Patterns
安全-first design patterns for RAG pipelines including source validation, content sanitization, and retrieval controls.
RAG 架構：檢索系統如何運作
檢索增強生成管線之端到端解剖——文件攝入、分塊、embedding、索引、檢索、脈絡組裝與生成——含各階段之攻擊面分析。
進階 RAG Manipulation 實驗室
Perform sophisticated RAG manipulation including embedding space attacks, metadata poisoning, and retrieval algorithm gaming.
進階 RAG 投毒 Techniques
Execute sophisticated RAG poisoning including gradient-guided document crafting.
實驗室: RAG Full Chain 攻擊
Hands-on lab for executing a complete RAG attack chain from document injection through retrieval manipulation to data exfiltration, targeting every stage of the Retrieval-Augmented Generation pipeline.
RAG Access Control Bypass
Bypass document-level access controls in enterprise RAG systems through query manipulation and context injection.
Basic RAG Query Injection
Craft user queries that manipulate RAG retrieval to surface unintended documents.
CTF：RAG 劫案
透過利用檢索機制、文件解析、嵌入操弄與上下文視窗管理漏洞，從檢索增強生成（RAG）系統中擷取敏感資訊。
RAG Infiltrator: Level 2 — Enterprise Knowledge Base
Infiltrate and exfiltrate data from a multi-tier enterprise RAG system with access controls.
CTF: RAG Infiltrator
Poison a RAG system to return attacker-controlled content for specific queries. Score based on precision of targeting and stealth of the injected documents.
RAG Treasure Hunt: Vector DB Infiltration
Infiltrate a vector database by crafting documents that surface for specific queries to reveal flag fragments.
實驗室: 進階 RAG 投毒
Hands-on lab for crafting documents that reliably get retrieved and influence RAG responses for specific target queries.
實驗室: Chunking 利用ation
Hands-on lab for crafting documents that split across chunks in ways that hide malicious content from chunk-level filtering while maintaining attack effectiveness.
實驗室: Citation Fabrication
Hands-on lab for getting RAG systems to cite documents that don't exist or misattribute quotes to legitimate sources.
Document-Based RAG Injection 實驗室
Inject adversarial content into documents that will be processed by a RAG system to influence model responses.
實驗室: RAG Metadata Injection
Hands-on lab for exploiting metadata fields like titles, descriptions, and timestamps to manipulate RAG retrieval ranking and influence responses.
實驗室: 進階 RAG 安全 Testing
Test RAG systems for chunking exploitation, reranking manipulation, and cross-document injection attacks.
RAG Context 投毒
Poison a vector database to inject adversarial content into RAG retrieval results.
實驗室: RAG Pipeline 投毒
Hands-on lab for setting up a RAG pipeline with LlamaIndex, injecting malicious documents, testing retrieval poisoning, and measuring injection success rates.
實驗室: Re-ranking 攻擊s
Hands-on lab for manipulating the re-ranking stage of RAG pipelines to promote or suppress specific documents in retrieval results.
PDF Document Injection for RAG Systems
Craft adversarial PDF documents that inject instructions when processed by RAG document loaders.
RAG Document Injection Campaign
Design and execute a document injection campaign against a RAG-powered application with vector search.
模擬：RAG 管線投毒
針對以 RAG 為本之知識管理系統之紅隊委任模擬，涵蓋 embedding 注入、文件投毒、檢索操弄與知識庫外洩。
模擬：企業 RAG 安全評估
完整案件模擬，評估企業 RAG 驅動的知識庫以偵測投毒、外洩與注入漏洞。
Multimodal RAG 投毒
投毒 multimodal RAG systems through adversarial documents with embedded visual and textual payloads.
間接提示詞注入
攻擊者如何在大型語言模型處理的外部資料來源中嵌入惡意指令，無需直接存取模型輸入即可發動攻擊。
Chunk Boundary 攻擊s
利用ing document splitting and chunking mechanisms in RAG pipelines, including payload injection at chunk boundaries, cross-chunk instruction injection, and chunk size manipulation.
RAG、資料與訓練攻擊
針對 AI 系統資料層攻擊的概覽，包含 RAG 投毒、訓練資料操控與資料萃取技術。
Knowledge Base 投毒 (Rag Data 攻擊s)
進階 corpus poisoning strategies for RAG systems, including black-box and white-box approaches, scaling dynamics, and the PoisonedRAG finding that 5 texts in millions achieve 90% attack success.
Metadata Injection
Manipulating document metadata to influence RAG retrieval ranking, bypass filtering, spoof source attribution, and exploit metadata-based access controls.
RAG 管線投毒
透過投毒檢索增強生成管線以操控 AI 回應的技術——涵蓋文件注入、嵌入操控、檢索排名攻擊與持久投毒策略。
Retrieval Manipulation (Rag Data 攻擊s)
Techniques for manipulating RAG retrieval to control which documents reach the LLM context, including adversarial query reformulation, retriever bias exploitation, and semantic similarity gaming.
RAG 投毒 End-to-End 導覽
Complete walkthrough of poisoning a RAG system from document injection through information extraction.
RAG Hybrid Search 投毒 導覽
導覽 of poisoning both vector and keyword search in hybrid RAG architectures for maximum retrieval influence.
Implementing Access Control in RAG Pipelines
導覽 for building access control systems in RAG pipelines that enforce document-level permissions, prevent cross-user data leakage, filter retrieved context based on user authorization, and resist retrieval poisoning attacks.
RAG Input Sanitization 導覽
Implement input sanitization for RAG systems to prevent document-based injection attacks.
Secure RAG Architecture 導覽
Design and implement a secure RAG architecture with document sanitization, access controls, and output validation.
RAG Document Sandboxing Implementation
Implement document-level sandboxing for RAG systems to prevent cross-document injection and privilege escalation.
Secure RAG Architecture Implementation
Implement a security-hardened RAG architecture with input sanitization, access control, and output validation.
RAG System 紅隊 Engagement
Complete walkthrough for testing RAG applications: document injection, cross-scope retrieval exploitation, embedding manipulation, data exfiltration through retrieval, and chunk boundary attacks.
LangChain Application 安全 Testing
End-to-end walkthrough for security testing LangChain applications: chain enumeration, prompt injection through chains, tool and agent exploitation, retrieval augmented generation attacks, and memory manipulation.
LlamaIndex RAG Application 安全 Testing
End-to-end walkthrough for security testing LlamaIndex RAG applications: index enumeration, query engine exploitation, data connector assessment, response synthesis manipulation, and agent pipeline testing.