ToolTweak: An Attack on Tool Selection in LLM-based Agents

Jonathan Sneh, Ruomei Yan, Jialin Yu, Philip Torr, Yarin Gal, Sunando Sengupta, Eric Sommerlade, Alasdair Paren, Adel Bibi

Keywords: Tool Use, LLM Agents, Fairness, Bias

TL;DR: We investigate attacks biasing an API to be selected over comparable alternatives and how to defend against them.

Abstract:

As LLMs increasingly power agents that interact with external tools, tool use has become an essential mechanism for extending their capabilities. These agents typically select tools from growing databases or marketplaces to solve user tasks, creating implicit competition among tool providers and developers for visibility and usage. In this paper, we show that this selection process harbors a critical vulnerability: by iteratively manipulating tool names and descriptions, adversaries can systematically bias agents toward selecting specific tools, gaining unfair advantage over equally capable alternatives.
We present **ToolTweak**, a lightweight automatic attack that increases selection rates from a baseline of around 20\% to as high as 81\%, with strong transferability between open-source and closed-source models.
Beyond individual tools, we show that such attacks cause distributional shifts in tool usage, revealing risks to fairness, competition, and security in emerging tool ecosystems.
To mitigate these risks, we evaluate two defenses: paraphrasing and perplexity filtering, which reduce bias and lead agents to select functionally similar tools more equally.
All code will be open-sourced upon acceptance.

Primary Area: alignment, fairness, safety, privacy, and societal considerations

Submission Number: 11241