Lightweight Prompt Injection Detection for Screenshot-Based Web Agents
A new method, SnapGuard, detects prompt injection attacks in screenshot-based web agents without relying on large vision-language models. The approach addresses vulnerabilities where malicious instructions embedded in webpage visuals cause unintended agent actions. By avoiding heavy VLMs, SnapGuard reduces computational overhead while maintaining detection efficacy.
Key facts
- SnapGuard targets prompt injection attacks on screenshot-based web agents.
- Existing text-centric defenses are ineffective against visual attacks.
- Multimodal detection using large VLMs incurs high computational costs.
- SnapGuard offers a lightweight alternative to VLM-based methods.
- The method is described in arXiv preprint 2604.25562.
- Prompt injection attacks embed malicious instructions into webpage content.
- Screenshot-based agents operate on rendered visual webpages.
Entities
Institutions
- arXiv