AI Security Digest — May 31, 2026
A digest covering the first in-the-wild LLM agent attacks, focusing on RAG pipeline injection and multi-agent system jailbreaks.
·News Digest·4 min readLLM SecurityRAG SecurityAgent SecurityAdversarial ML
Topic: RAG Security
14 articles in this topic.
This topic page curates research-focused writing on RAG Security, with an emphasis on practical security implications, reproducible observations, and implementation-aware takeaways. Instead of isolated summaries, the collection is organized to help you connect attack techniques, defensive controls, and evaluation criteria across multiple papers and project write-ups.
Across 14 articles, this cluster highlights how RAG Security appears in real workflows and where teams commonly miss risk boundaries. The coverage includes news digest, trend report, research paper, project, paper review and connects this theme with adjacent areas such as LLM Security, Agent Security, Adversarial ML, so you can move from conceptual understanding to deployable engineering decisions.
This page is maintained as a high-signal index for RAG Security. Use it to follow newer articles first, then branch into adjacent topics and defensive patterns that repeatedly appear across projects and paper reviews.
Related Topics
What You Will Find Here
- Related directions: LLM Security, Agent Security, Adversarial ML.
- Start with: AI Security Digest — May 31, 2026 and This Week in AI Security — May 31, 2026.
- Use this page as a hub for internal links when publishing future posts in the same area.
This Week in AI Security — May 31, 2026
A weekly roundup of AI security research focusing on the shift from static defenses to dynamic runtime containment for autonomous agents.
·Trend Report·5 min readLLM SecurityRAG SecurityAgent SecurityAdversarial ML
AI Security Digest — May 24, 2026
The dominant theme this week is the collapse of static, post-hoc alignment defenses under the pressure of dynamic, meta-optimizing exploit engines and the subsequent shift toward native, data-free mod
·News Digest·9 min readLLM SecurityRAG SecurityData PoisoningAdversarial ML
This Week in AI Security — May 24, 2026
This week, the AI security research community signaled a decisive pivot from static, prompt-response safety paradigms to the volatile, high-stakes realm of agentic autonomy and complex system integrat
·Trend Report·12 min readLLM SecurityRAG SecurityAgent SecurityData PoisoningAdversarial ML
AI Security Digest — April 22, 2026
The unifying theme of this week's AI security landscape is the critical transition from superficial, syntax-level filtering to deep, state-aware behavioral defenses across both agentic workflows and s
·News Digest·11 min readLLM SecurityRAG SecurityAgent SecurityData PoisoningAI SafetyCode Security
AI Security Digest — April 21, 2026
The dominant security theme today is the structural breakdown of boundaries between reasoning engines and executive environments, transitioning the primary threat vector from semantic prompt manipulat
·News Digest·10 min readLLM SecurityRAG SecurityAgent SecurityAI SafetyPrivacyCode SecurityWatermarkingDeepfakes & Biometrics
AI Security Digest — April 11, 2026
The single dominant theme in this week’s landscape is the systemic collapse of static, input-boundary defense paradigms as adversarial exploits pivot to dynamic, multi-agent cascading injections and v
·News Digest·13 min readLLM SecurityRAG SecurityAgent SecurityAdversarial ML
AI Security Digest — April 07, 2026
The current AI security landscape is defined by a critical architectural shift: as autonomous agent ecosystems transition from stateless chat interfaces to persistent, multi-tool environments, the tra
·News Digest·8 min readLLM SecurityRAG SecurityAgent SecurityData PoisoningInfrastructure Security
AI Security Digest — April 05, 2026
The transition of Large Language Models (LLMs) from static chat interfaces to autonomous, multi-agent frameworks has transformed the AI threat landscape, rendering standard input-filtering guardrails
·News Digest·9 min readLLM SecurityRAG SecurityAgent SecurityAdversarial MLInfrastructure Security
Trends in Attacks and Defenses against Retrieval-Augmented Generation (RAG) Systems
A comprehensive survey of security vulnerabilities in RAG systems, classifying adversarial attacks by component—data poisoning, retrieval poisoning, and prompt manipulation—and examining emerging defense strategies.
·Research Paper·9 min readLLM SecurityRAG SecurityData Poisoning
Visualizing RAG Security: A Deep Dive with RAG-Vis Playground
An interactive journey through the fundamentals of Retrieval-Augmented Generation, its security vulnerabilities, and state-of-the-art defense mechanisms.
·Project·3 min readLLM SecurityRAG SecurityData PoisoningTools & Visualization
GASLITE: Poisoning Dense Embedding-based Retrieval Systems
An analysis of GASLITE, a novel attack that poisons dense embedding-based retrieval systems by crafting adversarial passages that appear in top-k results for targeted queries, achieving up to 100% success with minimal corpus contamination.
·Paper Review·10 min readRAG SecurityData PoisoningAdversarial ML
Rescuing the Unpoisoned: Efficient Defense against Knowledge Corruption Attacks on RAG Systems
RAGDefender is a lightweight, efficient defense mechanism designed to protect Retrieval-Augmented Generation (RAG) systems from knowledge corruption attacks
·Research Paper·6 min readLLM SecurityRAG SecurityAdversarial ML
Membership Inference Attacks on Retrieval-Augmented Generation: A Comprehensive Survey
A comprehensive analysis of membership inference attacks against RAG systems, examining three state-of-the-art approaches: RAG-MIA, S²MIA, and MBA, along with their defenses and limitations.
·Paper Review·7 min readLLM SecurityRAG SecurityPrivacy