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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Multimodal Prompt Injection

Published Sep 7, 2025Analyzed Dec 9, 2025Source: arXiv

Large Language Models (LLMs), including GPT-4o, LLaMA-3, and GPT-3.5-Turbo, are vulnerable to multimodal prompt injection attacks. These models fail to distinguish between system-level instructions and user-provided content within the context window. Attackers can exploit this by embedding malicious instructions in direct text, indirect sources (such as third-party webpages or PDFs), or visual inputs (images). Successful exploitation results in the model prioritizing the injected adversarial…

Multimodal Prompt Injection Attacks: Risks and Defenses for Modern LLMs

Evaluated models: GPT-3.5, GPT-4o, Llama 3 8B+1 more

GPT-3.5
GPT-4o
Llama 3 8B
Mistral Large 24B

LLM Multi-Agent IP Leakage

Published May 18, 2025Analyzed May 31, 2025Source: arXiv

Large Language Model (LLM)-based Multi-Agent Systems (MAS) are vulnerable to intellectual property (IP) leakage attacks. An attacker with black-box access (only interacting via the public API) can craft adversarial queries that propagate through the MAS, extracting sensitive information such as system prompts, task instructions, tool specifications, number of agents, and system topology.

IP Leakage Attacks Targeting LLM-Based Multi-Agent Systems

Evaluated models: GPT-4o, GPT-4o Mini, Llama 3.1 70B+2 more

GPT-4o
GPT-4o Mini
Llama 3.1 70B
Llama 3.1 8B
Qwen 2.5 72B

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.