MUZZLE: Adaptive Agentic Red-Teaming of Web Agents Against Indirect Prompt Injection Attacks
arXiv:2602.09222v2 Announce Type: replace-cross Abstract: Large language model (LLM) based web agents are increasingly deployed to automate complex online tasks by directly interacting with web sites and performing actions on users' behalf. While these agents offer powerful capabilities, their design exposes them to indirect prompt injection attacks embedded in untrusted web content, enabling adversaries to hijack agent behavior and violate user intent. Despite growing awareness of this threat, existing evaluations rely on fixed attack templates, manually selected injection surfaces, or narrow
The proliferation of LLM-based web agents for automating online tasks directly exposes them to indirect prompt injection attacks, necessitating immediate red-teaming efforts.
This research highlights critical vulnerabilities in autonomous AI agents, posing significant security and control challenges for their widespread deployment and adoption.
The understanding of AI agent security shifts from theoretical concerns to practical, adversarial testing methods, forcing developers to account for sophisticated indirect injection attacks.
- · AI security researchers
- · Red-teaming platforms
- · Ethical AI developers
- · Unsecured web agents
- · Users of vulnerable AI systems
- · Organizations deploying AI without robust security
Increased focus on robust security protocols and adversarial testing becomes mandatory for AI agent development.
Development of more resilient web agents that can autonomously detect and defend against indirect prompt injections becomes a priority.
The complexity of securing AI agents could slow their deployment in sensitive applications, impacting the pace of automation in certain sectors.
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Read at arXiv cs.AI