Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity
📰 ArXiv cs.AI
Learn to defend against indirect prompt injection attacks in ReAct agents by understanding depth-dependent injection, payload framing, and turn-budget sensitivity
Action Steps
- Analyze the attack surface of ReAct agents to identify potential vulnerabilities
- Evaluate the effectiveness of existing benchmarks in measuring attack success rate (ASR)
- Investigate the impact of injection depth on indirect prompt injection attacks
- Experiment with different payload framing techniques to mitigate attacks
- Configure turn-budget sensitivity to optimize agent performance and security
Who Needs to Know This
AI researchers and engineers working with ReAct agents can benefit from this knowledge to improve the security of their models
Key Insight
💡 Understanding depth-dependent indirect prompt injection is crucial for securing ReAct agents
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Key Takeaways
Learn to defend against indirect prompt injection attacks in ReAct agents by understanding depth-dependent injection, payload framing, and turn-budget sensitivity
Full Article
Title: Depth-Dependent Indirect Prompt Injection in Tool-Calling ReAct Agents: Injection Depth, Payload Framing, and Turn-Budget Sensitivity
Abstract:
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)
Abstract:
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)
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