A Sentence Relation-Based Approach to Sanitizing Malicious Instructions
Retrieval-augmented generation and tool-integrated LLM agents increasingly depend on external textual sources. This reliance broadens the available attack surface, allowing adversaries to insert malicious instructions that trigger unintended model behaviors. Current defensive measures often utilize LLM-based detectors to filter such content, but these approaches remain vulnerable to optimization-based attacks. Additionally, training-based methods frequently fail to generalize to novel data distributions. To resolve these issues, we introduce SONAR, a prompt sanitization framework that identifies and removes injected content using metrics from natural language inference. Specifically, SONA...
| Authors | Soumil Datta, Melissa Umble, Daniel S. Brown et al. |
|---|---|
| Primary category | cs.CR |
| Published | 1 May 2026 |
| Last updated at source | 1 May 2026 |
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