SlotGuard: A Privacy Layer for LLM Agent Transcripts That Preserves Performance
Researchers introduce SlotGuard, a privacy layer for LLM agent transcripts that replaces sensitive data with format-preserving synthetic values while maintaining agent performance. In controlled experiments, SlotGuard removed all annotated structurally sensitive characters and reduced credential leakage to 0% across test cases, with minimal impact on task success rates. The method addresses shortcomings of existing redaction techniques, such as missing embedded references or over-redacting benign data, by using typed slots and session graphs to preserve transcript structure and context.
Why it matters: This approach offers a practical solution for protecting private data and credentials in LLM agent transcripts without sacrificing agent effectiveness.
Full story at: arXiv Cryptography and Security ↗