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Beyond the Benchmark: Innovative Defenses Against Prompt Injection Attacks

Authors: Safwan Shaheer, G. M. Refatul Islam, Mohammad Rafid Hamid, Tahsin Zaman Jilan

Published: 2025-12-18

arXiv ID: 2512.16307v1

Added to Library: 2026-01-07 10:09 UTC

Red Teaming

📄 Abstract

In this fast-evolving area of LLMs, our paper discusses the significant security risk presented by prompt injection attacks. It focuses on small open-sourced models, specifically the LLaMA family of models. We introduce novel defense mechanisms capable of generating automatic defenses and systematically evaluate said generated defenses against a comprehensive set of benchmarked attacks. Thus, we empirically demonstrated the improvement proposed by our approach in mitigating goal-hijacking vulnerabilities in LLMs. Our work recognizes the increasing relevance of small open-sourced LLMs and their potential for broad deployments on edge devices, aligning with future trends in LLM applications. We contribute to the greater ecosystem of open-source LLMs and their security in the following: (1) assessing present prompt-based defenses against the latest attacks, (2) introducing a new framework using a seed defense (Chain Of Thoughts) to refine the defense prompts iteratively, and (3) showing significant improvements in detecting goal hijacking attacks. Out strategies significantly reduce the success rates of the attacks and false detection rates while at the same time effectively detecting goal-hijacking capabilities, paving the way for more secure and efficient deployments of small and open-source LLMs in resource-constrained environments.

🔍 Key Points

  • Novel defense mechanisms against prompt injection attacks in LLMs, specifically targeting the LLaMA models.
  • Introduction of an iterative framework (Chain Of Thoughts) for generating and refining defense prompts, enhancing robustness against goal-hijacking while minimizing usability loss.
  • Empirical evaluation demonstrating significant improvements in attacking success rates, false detection rates, and overall robustness of defenses.
  • Recognition of the growing importance of small open-sourced LLMs for deployment in resource-constrained environments, emphasizing their unique security challenges.
  • Contribution to the open-source ecosystem by laying foundational work for future defenses and promoting security awareness in LLM applications.

💡 Why This Paper Matters

This paper is highly relevant as it addresses a critical security vulnerability in large language models through innovative and empirical approaches. By focusing on small open-source models and their functional applicability in real-world scenarios, the proposed defenses not only enhance security but also ensure usability—crucial for widespread deployment. The insights and methodologies presented can significantly influence how LLMs evolve toward safer interactions in diverse applications, underpinning a more secure landscape for AI technologies.

🎯 Why It's Interesting for AI Security Researchers

The paper is of crucial interest to AI security researchers as it highlights emergent threats in LLMs, particularly prompt injection attacks, while offering practical defenses. It challenges existing paradigms of security in AI by proposing a novel framework that balances threat mitigation with model performance. The empirical results provide a compelling foundation for further research in robust LLM security mechanisms, making it a valuable resource for advancing AI safety methodologies.

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