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Last analyzed 9/9/2026

Language Model Security Database

985 research findings · 1123 evaluated models

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2 entries

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

Published 1/1/2026
Analyzed 2/21/2026

A vulnerability exists in Large Language Model (LLM) deployments and multi-agent systems where an autonomous attacker agent can systematically extract hidden system prompts through self-evolving interaction strategies. The vulnerability leverages a "JustAsk" framework which utilizes Upper Confidence Bound (UCB) exploration to dynamically select and refine attack vectors from a hierarchical taxonomy of 14 atomic skills (e.g., structural formatting, authority appeals) and 14 multi-turn…

Just Ask: Curious Code Agents Reveal System Prompts in Frontier LLMs
Evaluated models: o1, Llama 3.1 70B Hanami X1, Phi-4 +38 more

Source: arXiv

Published 12/1/2024
Analyzed 3/19/2025

A vulnerability in LLM-based agents, dubbed AI Agent Injection (AI²), allows attackers to hijack the agent's actions by manipulating the agent's memory retrieval mechanism. The attack involves two main steps: (1) Stealing action-aware knowledge from the agent's memory using crafted adversarial queries targeting the retriever module and (2) Generating Trojan prompts consisting of a Trojan string and hijacking instructions. The Trojan string is designed to manipulate the retriever into…

Towards Action Hijacking of Large Language Model-based Agent
Evaluated models: Alpaca, BERT, GPT-3 +3 more

Source: arXiv

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.