# retrieval
標記為「retrieval」的 34 篇文章
Memory Priority and Relevance Manipulation
Manipulating memory retrieval ranking and priority scores to surface adversarial memories over legitimate ones.
Memory Retrieval Poisoning
Manipulating memory retrieval mechanisms to surface adversarial context during agent reasoning.
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.
May 2026: RAG Poisoning Challenge
Inject malicious documents into a retrieval-augmented generation system to control responses for specific queries without disrupting normal operation.
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.
Dense Retrieval Adversarial Attacks
Adversarial attacks against dense retrieval models used in RAG and search systems.
Embedding Poisoning Techniques
Techniques for poisoning embedding spaces to manipulate retrieval and similarity search.
RAG Retrieval Poisoning
Poisoning document collections to manipulate what gets retrieved by RAG systems, enabling indirect prompt injection at scale.
RAG Retrieval Security
Security of RAG retrieval pipelines from an embedding perspective: how retrieval can be manipulated through poisoned chunks, chunking boundary exploitation, and re-ranking attacks.
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.
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 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.
Embedding Collision Attack Walkthrough
Craft documents that collide in embedding space with target queries to hijack RAG retrieval results.
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 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.
DSPy Pipeline Security Testing
End-to-end walkthrough for security testing DSPy optimized LLM pipelines: module enumeration, signature exploitation, optimizer manipulation, retrieval module assessment, and compiled prompt analysis.
記憶體 Priority and Relevance Manipulation
Manipulating memory retrieval ranking and priority scores to surface adversarial memories over legitimate ones.
記憶體 Retrieval 投毒
Manipulating memory retrieval mechanisms to surface adversarial context during agent reasoning.
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.
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.
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.
Dense Retrieval Adversarial 攻擊s
Adversarial attacks against dense retrieval models used in RAG and search systems.
Embedding 投毒 Techniques
Techniques for poisoning embedding spaces to manipulate retrieval and similarity search.
RAG Retrieval 投毒
投毒 document collections to manipulate what gets retrieved by RAG systems, enabling indirect prompt injection at scale.
RAG 檢索安全
從嵌入向量觀點看 RAG 檢索管線的安全性：檢索如何透過投毒區塊、區塊邊界利用與重排序攻擊被操控。
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。
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 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.
Embedding Collision 攻擊 導覽
Craft documents that collide in embedding space with target queries to hijack RAG retrieval results.
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 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.
DSPy Pipeline 安全 Testing
End-to-end walkthrough for security testing DSPy optimized LLM pipelines: module enumeration, signature exploitation, optimizer manipulation, retrieval module assessment, and compiled prompt analysis.