IterInject: Indirect Prompt Injection Against LLM Agents via Feedback-Guided Iterative Optimization
arXiv:2605.24659v1 Announce Type: new Abstract: LLM-based agents are increasingly deployed for complex tasks requiring planning, tool use, and interaction with external services. Their reliance on untrusted external content exposes them to indirect prompt injection (IPI), in which adversarial instructions embedded in retrieved data hijack agent behavior. Existing attacks rely on static payloads that cannot adapt to agent-specific defenses; even recent adaptive methods lack structured feedback to guide optimization. We introduce \oursys, a feedback-guided iterative framework that closes the loo
The increasing deployment of LLM-based agents requires robust security measures, and this research addresses a critical vulnerability, particularly as agents interact with untrusted external content.
This research details a new, adaptive indirect prompt injection method against LLM agents, which could compromise their autonomy and reliability, necessitating stronger defensive mechanisms.
The demonstrated ability of iterative, feedback-guided attacks means that static or less adaptive defenses against prompt injection will be increasingly insufficient.
- · AI security researchers
- · Developers of LLM agent security platforms
- · Ethical hackers
- · LLM agent developers with weak security protocols
- · Organizations deploying vulnerable LLM agents
- · Users relying on unsecured LLM agents
Increased focus on adaptive and continuously optimizing defense mechanisms for LLM agents.
Potential for new regulations or industry standards for securing autonomous AI systems against prompt injection.
A 'security arms race' in the development of LLM agents, leading to more resilient yet complex AI systems.
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Read at arXiv cs.LG