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Updated 7/21/2026, database is current

Language Model Security Database

959 research findings · 1077 evaluated models

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51 entries

Matches every word across titles, descriptions, sources, affected systems, and models.

LLM Memory Poisoning Attack

Updated 12/28/2024

A vulnerability in Retrieval-Augmented Generation (RAG)-based Large Language Model (LLM) agents allows attackers to inject malicious demonstrations into the agent's memory or knowledge base. By crafting a carefully optimized trigger, an attacker can manipulate the agent's retrieval mechanism to preferentially retrieve these poisoned demonstrations, causing the agent to produce adversarial outputs or take malicious actions even when seemingly benign prompts are used. The attack, termed…

Agentpoison: Red-teaming llm agents via poisoning memory or knowledge bases
Affects: GPT-2, GPT-3.5 Turbo, Llama 3 70B +2 more

Source: arXiv

AI agents
RAG
Poisoning
Black-box

Research methodology

Entries summarize publicly available primary-source security research. Model names reflect only systems explicitly evaluated by the cited paper, and measurements are research-reported unless independent verification is stated.