SlotGuard system prevents LLM agents from leaking private data

Researchers have developed SlotGuard, a novel system designed to prevent Large Language Model (LLM) agents from inadvertently revealing sensitive local context and credentials. Unlike previous methods, SlotGuard creates a local transcript boundary that masks private data while preserving agent performance. It achieves this by rewriting structural bindings into typed slots, replacing secrets with synthetic values, and using a session graph to manage cross-turn references. This approach effectively eliminates credential leakage and significantly reduces the exposure of sensitive characters, maintaining high task success rates across various LLM models. AI

IMPACT Enhances LLM agent security by preventing sensitive data leakage, crucial for enterprise adoption.

RANK_REASON Academic paper detailing a new method for LLM agent security. [lever_c_demoted from research: ic=1 ai=1.0]

Read on arXiv cs.CL →

• API keys
• arXiv
• Hugging Face
• LLM agents
• SlotGuard

• safety
• paper
• product

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →