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October 12, 2025Open Access

Multimodal Prompt Injection Attacks: Risks and Defenses for Modern LLMs

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Authors

AYAndrew YeoDCDaiwoo Choi

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Overview

Experiments reveal vulnerabilities to prompt injection in large language models, highlighting the need for effective defenses.

Key Points

  • Prompt injection attacks expose exploitable weaknesses in large language models, necessitating improved security measures.
  • Tests on eight commercial models showed that input normalization can enhance defenses against vulnerabilities.
  • Comparative analysis revealed that Claude 3 exhibited greater robustness against prompt injection attacks.
  • Four categories of attacks were examined, underscoring the multifaceted nature of security risks for large language models.

Cite This Study

Yeo et al. (2025) studied this question.

synapsesocial.com/papers/68ebffcfdef9fcb308ff2554https://doi.org/10.48550/arxiv.2509.05883
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Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Multimodal Prompt Injection: A Systematic Evaluation of Cross-Channel Attack Vectors Against LLM Safety Defenses2026
  2. 2An Empirical Benchmark and Security Evaluation of Prompt Injection Attacks in LLM Systems2026
  3. 3Automatic and Universal Prompt Injection Attacks against Large Language Models2024 · 10 citations
  4. 4Prompt injection and Data Exfiltration Attacks in Large Language Model Applications: Detection and Mitigation Framework2026
  5. 5Prompt injection and Data Exfiltration Attacks in Large Language Model Applications: Detection and Mitigation Framework2026