ToolHijacker optimizes malicious tool documents via a two-phase strategy to hijack LLM agents' tool selection in no-box settings.
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ToolOmni combines supervised fine-tuning on a cold-start multi-turn dataset with Decoupled Multi-Objective GRPO to enable proactive retrieval and grounded execution, yielding +10.8% higher end-to-end tool-use success and better generalization to unseen tools.
citing papers explorer
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Prompt Injection Attack to Tool Selection in LLM Agents
ToolHijacker optimizes malicious tool documents via a two-phase strategy to hijack LLM agents' tool selection in no-box settings.
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ToolOmni: Enabling Open-World Tool Use via Agentic learning with Proactive Retrieval and Grounded Execution
ToolOmni combines supervised fine-tuning on a cold-start multi-turn dataset with Decoupled Multi-Objective GRPO to enable proactive retrieval and grounded execution, yielding +10.8% higher end-to-end tool-use success and better generalization to unseen tools.