The Hidden Threat in Plain Text: Attacking RAG Data Loaders - arxiv.org/pdf/2507.05093 Large Language Models (LLMs) have transformed human–machine interaction since ChatGPT’s 2022 debut, with Retrieval-Augmented Generation (RAG) emerging as a key framework that enhances LLM outputs by integrating external knowledge. However, RAG’s reliance on ingesting external documents introduces new vulnerabilities. This paper exposes a critical security gap at the data loading stage, where malicious actors can stealthily corrupt RAG pipelines by exploiting document ingestion. We propose a taxonomy of 9 knowledge-based poisoning attacks and introduce two novel threat vectors—Content Obfuscation and Content Injection—targeting common formats (DOCX, HTML, PDF). Using an automated toolkit implementing 19 stealthy injection techniques, we test five popular data loaders, finding a 74.4% attack success rate across 357 scenarios. We further validate these threats on six end-to-end RAG systems—including white-box pipelines and black-box services like NotebookLM and OpenAI Assistants— demonstrating high success rates and critical vulnerabilities that bypass filters and silently compromise output integrity. Our results emphasize the urgent need to secure the document ingestion process in RAG systems against covert content manipulations. #RAGSecurity #LLMVulnerabilities #DataLoaderAttacks #ContentInjection #ContentObfuscation #AIThreats #PoisoningAttacks #SecureRAG #LLMSecurity #AdversarialAI #PromptInjection #DocumentIngestion #AITrust #StealthyAttacks #NotebookLM #OpenAIAssistants #RAGPipeline #AIIntegrity #GenerativeAI #AIForensics

AI-Driven Security Research: Weekly Highlights 🔍 This week’s 21 studies cover advancements in content injection attacks, multistage network attacks, LLM-generated code analysis, constitutional classifiers, energy loss in RLHF, hallucination vulnerabilities, and various adversarial and privacy-related challenges in large language models, curated by Brandon Dixon. 🎩 Illusions of Relevance: Using Content Injection Attacks to Deceive Retrievers, Rerankers, and LLM Judges arxiv.org/pdf/2501.18536v1.p… 🌐 On the Feasibility of Using LLMs to Execute Multistage Network Attacks arxiv.org/pdf/2501.16466v1.p… 🦄 Comparing Human and LLM Generated Code: The Jury is Still Out! arxiv.org/pdf/2501.16857v1.p… 🏛 Constitutional Classifiers: Defending against Universal Jailbreaks across Thousands of Hours of Red Teaming arxiv.org/pdf/2501.18837v1.p… 💡 The Energy Loss Phenomenon in RLHF: A New Perspective on Mitigating Reward Hacking arxiv.org/pdf/2501.19358v1.p… 👻 Importing Phantoms: Measuring LLM Package Hallucination Vulnerabilities arxiv.org/pdf/2501.19012v1.p… Other Interesting Research 📜 Exploring Potential Prompt Injection Attacks in Federated Military LLMs and Their Mitigation arxiv.org/pdf/2501.18416v1.p… 🔑 Jailbreaking LLMs’ Safeguard with Universal Magic Words for Text Embedding Models arxiv.org/pdf/2501.18280v1.p… 🛡 Smoothed Embeddings for Robust Language Models arxiv.org/pdf/2501.16497v1.p… 🔬 ASTRAL: Automated Safety Testing of Large Language Models arxiv.org/pdf/2501.17132v1.p… 🧪 Virus: Harmful Fine-tuning Attack for Large Language Models Bypassing Guardrail Moderation arxiv.org/pdf/2501.17433v1.p… 💡 Challenges in Ensuring AI Safety in DeepSeek-R1 Models: The Shortcomings of Reinforcement Learning Strategies arxiv.org/pdf/2501.17030v1.p… 🛠 TORCHLIGHT: Shedding LIGHT on Real-World Attacks on Cloudless IoT Devices Concealed within the Tor Network arxiv.org/pdf/2501.16784v1.p… ⚖️ Targeting Alignment: Extracting Safety Classifiers of Aligned LLMs arxiv.org/pdf/2501.16534v1.p… 🧩 xJailbreak: Representation Space Guided Reinforcement Learning for Interpretable LLM Jailbreaking arxiv.org/pdf/2501.16727v2.p… 🔍 RICoTA: Red-teaming of In-the-wild Conversation with Test Attempts arxiv.org/pdf/2501.17715v1.p… 📜 LLMs can be Fooled into Labelling a Document as Relevant arxiv.org/pdf/2501.17969v1.p… 💡 RL-based Query Rewriting with Distilled LLM for online E-Commerce Systems arxiv.org/pdf/2501.18056v1.p… 🚗 LLM-attacker: Enhancing Closed-loop Adversarial Scenario Generation for Autonomous Driving with Large Language Models arxiv.org/pdf/2501.15850v1.p… 📝 FDLLM: A Text Fingerprint Detection Method for LLMs in Multi-Language, Multi-Domain Black-Box Environments arxiv.org/pdf/2501.16029v1.p… 🔒 Differentially Private Steering for Large Language Model Alignment arxiv.org/pdf/2501.18532v1.p… 🛡 Panacea: Mitigating Harmful Fine-tuning for Large Language Models via Post-fine-tuning Perturbation arxiv.org/pdf/2501.18100v1.p… 📈 Improving Network Threat Detection by Knowledge Graph, Large Language Model, and Imbalanced Learning arxiv.org/pdf/2501.16393v1.p… 🏛 Indiana Jones: There Are Always Some Useful Ancient Relics arxiv.org/pdf/2501.18628v1.p… #ContentInjection #LLMSecurity #AIAdversarialAttacks #NetworkSecurity #AIModels #JailbreakDefense #AIPrivacy #RewardHacking #RLHF #LLMGeneratedCode #AIinSecurity #LLMHallucination #FederatedAI #AIandCybersecurity #ModelAlignment #AITrust #AIinHealthcare #AutonomousDriving #AIThreatDetection #AIModels #AIinEcommerce #AIinIoT #SecurityTesting #AIinEthics #AIModelDefense

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