Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection
Large language models (LLMs) are increasingly deployed in production systems, raising concerns about their exposure to adversarial manipulation through prompt injection and jailbreak attacks. Classifier-based guardrails, such as Prompt Guard 2, are widely used as a first line of defense against such attacks, but their internal decision logic is largely opaque to both defenders and attackers. This paper presents an exploratory case study that applies explainable artificial intelligence (XAI) techniques to analyze how Prompt Guard 2 distinguishes malicious from benign prompts. We conduct four experiments to probe this question empirically. Guided by Vanilla Gradient and SHAP attributions, w...
| Authors | Fernando Outeda, Gustavo Betarte, Juan Diego Campo et al. |
|---|---|
| Primary category | cs.CR |
| Published | 21 September 2026 |
| Last updated at source | 21 September 2026 |
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