Prompt Injection Detection is Regime-Dependent: A Deployment-Aware Evaluation with Interpretable Structural Signals
arXiv:2605.26999v1 Announce Type: new Abstract: Prompt injection poses a critical threat to the safe deployment of large language models, yet existing detection approaches are typically evaluated under limited settings that do not reflect real-world operating constraints. In this work, we present a deployment-aware evaluation of prompt injection detection using a multi-model and multi-regime experimental framework. We compare lexical, semantic, structural, and transformer-based detectors across multiple out-of-distribution settings, repeated data splits, and both ranking and thresholded deploy
The proliferation of Large Language Models (LLMs) in various applications necessitates robust security measures, making prompt injection detection a critical and immediate concern.
This research provides a more realistic and comprehensive evaluation framework for prompt injection detection, moving beyond limited settings to reflect real-world deployment challenges.
The understanding of prompt injection detection effectiveness is refined, emphasizing the regime-dependent nature and the need for deployment-aware evaluation rather than isolated testing.
- · AI security researchers
- · LLM developers
- · Enterprises deploying LLMs
- · Attackers exploiting prompt injection
- · Insecure LLM applications
- · Naive detection methods
Improved security and reliability of LLM deployments due to better detection mechanisms.
Increased trust in AI applications, accelerating their integration into sensitive workflows.
The development of highly adaptive and context-aware security layers for future AI systems, moving towards proactive threat neutralization.
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Read at arXiv cs.CL