Decoding Guardrails: XAI-Guided Perturbation Analysis of Prompt Injection Detection
A paper analyzes XAI techniques to evaluate Prompt Guard 2's prompt injection detection, finding that explanation methods may reduce the cost of adversarial bypasses.
A paper explores how XAI techniques analyze Prompt Guard 2's ability to distinguish malicious from benign prompts, finding that explanation methods may lower the cost of adversarial bypasses.
- Exploration of XAI techniques to analyze Prompt Guard 2's prompt injection detection
- Findings show that explanation methods may lower cost of adversarial bypasses
- Discusses implications for classifier-based guardrail design and evaluation
- Products
- Prompt Guard 2
- AI models
- Prompt Guard 2
From the original article
1 min read at arxiv.orgLarge 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.
By Fernando Outeda · arxiv.org
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