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Last analyzed Sep 9, 2026

Language Model Security Database

985 research findings · 1119 evaluated models

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1 entry

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

Multimodal Prompt Injection

Published Analyzed Source: 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
Application layer
Prompt layer
Injection
Jailbreak

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.