An Evaluation of Data Leakage Risks in Tool-Using LLM Agents in Realistic Scenarios
AI agents are increasingly being adopted in enterprise and personal settings with access to emails, databases, documents, and other tools where they can read, update, and disseminate sensitive information. Much of prior research on data leakage risks in agents has focused on adversarial data exfiltration through prompt injections and jailbreaks. However, sensitive information may also be exposed during non-adversarial use, creating leakage risks even when users issue benign requests. We report a joint evaluation by the Singapore AI Safety Institute and the Korea AI Safety Institute examining agent data leakage in 12 realistic, non-adversarial tasks spanning customer support, DevOps, web a...
| Authors | Hankyul Baek, Jaewon Noh, Sang Seo et al. |
|---|---|
| Primary category | cs.CR |
| Published | 15 June 2026 |
| Last updated at source | 15 June 2026 |
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