Last analyzed Sep 9, 2026

Language Model Security Database

Explore primary-source AI security research, evaluated models, attack techniques, and defensive evidence.

985 research findings · 1119 evaluated models

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

Mobile Agent Channel Subversion

Published Oct 31, 2025Analyzed Dec 30, 2025Source: arXiv

Mobile LLM-based agents (including Mobile-Agent-E, AppAgent, AutoDroid, and others) are vulnerable to indirect prompt injection attacks delivered via untrusted third-party mobile channels, such as in-app advertisements, system notifications, and embedded webviews. These agents utilize Multimodal Large Language Models (MLLMs) to perceive the device state via screenshots or accessibility trees. The vulnerability exists because the agents concatenate the user's prompt ($p$) with the environmental…

Measuring the Security of Mobile LLM Agents under Adversarial Prompts from Untrusted Third-Party Channels

Evaluated models: GPT-3.5 Turbo, GPT-4 Turbo, GPT-4o+1 more

GPT-3.5 Turbo
GPT-4 Turbo
GPT-4o
GPT-4.1 Mini

Agent System Orchestration Hijack

Published Mar 15, 2025Analyzed Jan 14, 2026Source: arXiv

Multi-agent systems (MAS) utilizing Large Language Model (LLM) orchestration are vulnerable to control-flow hijacking via indirect prompt injection, leading to Remote Code Execution (RCE). This vulnerability arises when a sub-agent (e.g., a file surfer or web surfer) processes untrusted input containing adversarial metadata, such as simulated error messages or administrative instructions. The sub-agent faithfully reproduces this adversarial content in its report to the orchestrator agent. The…

Multi-agent systems execute arbitrary malicious code

Evaluated models: GPT-4o, GPT-4o Mini, Gemini 1.5 Pro+1 more

GPT-4o
GPT-4o Mini
Gemini 1.5 Pro
Gemini 1.5 Flash

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.

Discovery and publication are separate: new papers enter a candidate queue, then require human review and explicit approval before publication. Updates follow that review process, not a guaranteed daily publishing schedule. “Last analyzed” is the newest analysis date among published entries; it does not mean that all recent research has been covered.