arXiv:2606.17114v1 Announce Type: cross Abstract: 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 Insti
The rapid deployment of AI agents into sensitive enterprise and personal environments necessitates an urgent evaluation of their inherent risks, moving beyond adversarial concerns to non-adversarial data leakage.
This research highlights a critical, often overlooked vulnerability in the widespread adoption of AI agents, fundamentally challenging assumptions about data security in automated systems.
The understanding of AI agent risk expands from malicious attacks to include systemic, non-adversarial processes, demanding more robust security architectures and operational protocols for their deployment.
- · AI security solution providers
- · Cybersecurity researchers
- · Enterprises with strong data governance
- · AI agent developers neglecting security
- · Organizations with weak data PII/security
- · Users relying on unsecured AI agent services
Increased scrutiny and demand for secure AI agent development and deployment frameworks.
New industry standards and regulatory pressures concerning AI agent data handling and privacy are likely to emerge.
The perceived trustworthiness and adoption rate of autonomous AI agents could be significantly impacted depending on the industry's response to these leakage risks.
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Read at arXiv cs.AI