This page provides the security targets of negative impacts “Adverse impact on human perception and judgment caused by AI misinformation” in the external influence aspect in the AI Security Map, as well as the attacks and factors that cause them, and the corresponding defense methods and countermeasures.
Security target
- Consumer
Attack or cause
- Integrity violation
- Explainability violation
- Degradation of output fairness
- Degradation of accuracy
- Degradation of controllability
- Reliability violation
- Poisoning attack against RAG
Defensive method or countermeasure
- Data curation
- RAG
- XAI (Explainable AI)
- Detection of hallucination
- Uncertainty quantification
- Education and follow-up
References
Poisoning attack against RAG
- Poisoning Retrieval Corpora by Injecting Adversarial Passages, 2023
- BadRAG: Identifying Vulnerabilities in Retrieval Augmented Generation of Large Language Models, 2024
- PoisonedRAG: Knowledge Corruption Attacks to Retrieval-Augmented Generation of Large Language Models, 2024
- Human-Imperceptible Retrieval Poisoning Attacks in LLM-Powered Applications, 2024
- Poison-RAG: Adversarial Data Poisoning Attacks on Retrieval-Augmented Generation in Recommender Systems, 2025
Data curation
RAG
- Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks, 2020
- REALM: Retrieval-Augmented Language Model Pre-Training, 2020
- In-Context Retrieval-Augmented Language Models, 2023
- Active Retrieval Augmented Generation, 2023
- Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflection, 2023
- Query Rewriting for Retrieval-Augmented Large Language Models, 2023
- Knowledge-Augmented Language Model Prompting for Zero-Shot Knowledge Graph Question Answering, 2023
- Generate rather than Retrieve: Large Language Models are Strong Context Generators, 2023
- Enhancing Retrieval-Augmented Large Language Models with Iterative Retrieval-Generation Synergy, 2023
- From Local to Global: A Graph RAG Approach to Query-Focused Summarization, 2024
Search for other references related to “RAG” in the literature database
XAI (Explainable AI)
- Visualizing and Understanding Convolutional Networks, 2014
- Deep Inside Convolutional Networks: Visualising Image Classification Models and Saliency Maps, 2014
- Understanding Deep Image Representations by Inverting Them, 2014
- “Why Should I Trust You?”: Explaining the Predictions of Any Classifier, 2016
- A Unified Approach to Interpreting Model Predictions, 2017
- Learning Important Features Through Propagating Activation Differences, 2017
- Understanding Black-box Predictions via Influence Functions, 2017
- Interpretable Explanations of Black Boxes by Meaningful Perturbation, 2017
- Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV), 2018
- Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization, 2019
Education and follow-up
- What Students Can Learn About Artificial Intelligence — Recommendations for K-12 Computing Education, 2022
- Learning to Prompt in the Classroom to Understand AI Limits: A pilot study, 2023
- Evaluating the Effectiveness of LLMs in Introductory Computer Science Education: A Semester-Long Field Study, 2024
- The Essentials of AI for Life and Society: An AI Literacy Course for the University Community, 2025