VisualLeakBench: Reproducible Action-Boundary Propagation Failures in Vision-Language Agents
arXiv:2606.07595v1 Announce Type: cross Abstract: Vision-language agents increasingly consume screenshots, documents, and user interfaces before writing to memory, sending messages, or invoking external tools. We study a concrete failure mode in this setting: action-boundary propagation, where sensitive or unsafe visible text is copied from an image into downstream tool arguments. We present VisualLeakBench, a diversified 500-image benchmark spanning UI, chat, document, form, and dashboard scenes, and evaluate a stratified 100-image agent subset with four production VLM systems under two workf
The proliferation of vision-language agents in diverse applications necessitates a deeper understanding of their security vulnerabilities, especially as they handle sensitive visual information.
This research highlights a critical security flaw (action-boundary propagation) in vision-language agents, which could lead to significant data leaks or misuse of sensitive information.
The understanding of VLM security postures shifts, requiring more robust input sanitization and boundary handling in agentic systems before they become widely deployed in critical workflows.
- · Cybersecurity researchers
- · Developers of secure AI systems
- · Ethical AI auditing firms
- · Early adopters of unhardened VLM agents
- · Users handling sensitive data with current VLM systems
- · Companies relying on VLM agents without robust security measures
Immediate industry efforts will focus on patching and developing mitigation strategies for sensitive data handling in vision-language agents.
New security standards and best practices will emerge specifically for vision-language models and agentic systems operating on visual inputs.
Legal and regulatory frameworks may evolve to address liabilities associated with AI agent data leaks or unintended information propagation.
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Read at arXiv cs.AI