Securing the Prompt Pipeline: A Systematic Review of Defense Mechanisms Against Prompt-Based Attacks in LLM Agents

Abstract

Current language model deployments face growing security challenges from prompt-based attacks, including jailbreaks, direct and indirect prompt injection, and instruction hijacking, which often evade traditional rule-based safeguards. As these models are increasingly integrated into agent-based systems, retrieval pipelines, and tool-driven workflows, such attacks exploit their natural language interfaces to bypass safety constraints and manipulate system behavior, in some cases leading to data l

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@article{mourad2026securing,
  title = {{Securing the Prompt Pipeline: A Systematic Review of Defense Mechanisms Against Prompt-Based Attacks in LLM Agents}},
  author = {Sana Mourad and E. E. Abdallah and Mohammad Ababneh},
  year = {2026},
  month = aug,
  journal = {Electronics},
  doi = {10.3390/electronics15163700},
  url = {https://www.semanticscholar.org/paper/2a6dee66de468ab63743a038cfc5017017f1af73}
}