Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity
arXiv:2605.30686v1 Announce Type: cross Abstract: ReAct agents that interleave chain-of-thought reasoning with tool calls are increasingly deployed for real tasks such as scheduling, file retrieval, and data access. Their tool observation loop creates a direct attack surface: an adversary who controls any tool's return value can embed instructions that redirect the agent away from the user's goal, a threat known as indirect prompt injection. Existing benchmarks evaluate attack success rate (ASR) at a fixed injection position under fixed conditions, leaving three risk dimensions unexplored: whe
The increasing deployment of ReAct agents in real-world applications highlights critical security vulnerabilities, making advanced prompt injection research timely.
This research reveals new attack surfaces and complexities in AI agent security, crucial for organizations relying on or developing such systems.
Understanding of indirect prompt injection in sophisticated AI agents is deepened, requiring more robust defense mechanisms and evaluation benchmarks beyond fixed conditions.
- · AI security researchers
- · Cybersecurity firms
- · Developers of secure AI agent platforms
- · Organizations deploying unsecured ReAct agents
- · Developers neglecting agent security
- · Users trusting vulnerable AI systems
Increased focus on hardening ReAct agent architectures against indirect prompt injection.
New industry standards and best practices will emerge for evaluating and mitigating AI agent vulnerabilities.
The development and adoption of AI agents for high-stakes tasks could be slowed until these security concerns are adequately addressed.
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