Academic

ToolFlood: Beyond Selection -- Hiding Valid Tools from LLM Agents via Semantic Covering

arXiv:2603.13950v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly use external tools for complex tasks and rely on embedding-based retrieval to select a small top-k subset for reasoning. As these systems scale, the robustness of this retrieval stage is underexplored, even though prior work has examined attacks on tool selection. This paper introduces ToolFlood, a retrieval-layer attack on tool-augmented LLM agents. Rather than altering which tool is chosen after retrieval, ToolFlood overwhelms retrieval itself by injecting a few attacker-controlled tools whose metadata is carefully placed by exploiting the geometry of embedding space. These tools semantically span many user queries, dominate the top-k results, and push all benign tools out of the agent's context. ToolFlood uses a two-phase adversarial tool generation strategy. It first samples subsets of target queries and uses an LLM to iteratively generate diverse tool names and descriptions. It then r

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Hussein Jawad, Nicolas J-B Brunel · · 1 min read · 2 views
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arXiv:2603.13950v1 Announce Type: new Abstract: Large Language Model (LLM) agents increasingly use external tools for complex tasks and rely on embedding-based retrieval to select a small top-k subset for reasoning. As these systems scale, the robustness of this retrieval stage is underexplored, even though prior work has examined attacks on tool selection. This paper introduces ToolFlood, a retrieval-layer attack on tool-augmented LLM agents. Rather than altering which tool is chosen after retrieval, ToolFlood overwhelms retrieval itself by injecting a few attacker-controlled tools whose metadata is carefully placed by exploiting the geometry of embedding space. These tools semantically span many user queries, dominate the top-k results, and push all benign tools out of the agent's context. ToolFlood uses a two-phase adversarial tool generation strategy. It first samples subsets of target queries and uses an LLM to iteratively generate diverse tool names and descriptions. It then runs an iterative greedy selection that chooses tools maximizing coverage of remaining queries in embedding space under a cosine-distance threshold, stopping when all queries are covered or a budget is reached. We provide theoretical analysis of retrieval saturation and show on standard benchmarks that ToolFlood achieves up to a 95% attack success rate with a low injection rate (1% in ToolBench). The code will be made publicly available at the following link: https://github.com/as1-prog/ToolFlood

Executive Summary

The article 'ToolFlood: Beyond Selection -- Hiding Valid Tools from LLM Agents via Semantic Covering' introduces a novel retrieval-layer attack on tool-augmented Large Language Model (LLM) agents called ToolFlood. ToolFlood overwhelms the retrieval stage by injecting a few attacker-controlled tools that semantically span many user queries, dominating the top-k results and pushing benign tools out of context. The attack achieves up to a 95% success rate with a low injection rate. The authors provide a two-phase adversarial tool generation strategy and theoretical analysis of retrieval saturation. The study highlights the vulnerability of LLM agents to semantic covering attacks and underscores the need for robust retrieval mechanisms to ensure the reliability and security of tool-augmented systems.

Key Points

  • ▸ ToolFlood is a retrieval-layer attack on tool-augmented LLM agents that overwhelms retrieval by injecting attacker-controlled tools.
  • ▸ The attack achieves up to a 95% success rate with a low injection rate, highlighting the vulnerability of LLM agents to semantic covering attacks.
  • ▸ The authors provide a two-phase adversarial tool generation strategy and theoretical analysis of retrieval saturation.

Merits

Strength of ToolFlood

The study demonstrates the effectiveness of ToolFlood in achieving high attack success rates with a low injection rate, underscoring its potential as a powerful tool for assessing the robustness of LLM agents.

Demerits

Limited Generalizability

The study is limited in its generalizability to other LLM agents and tool-augmented systems, as the attack strategy is tailored to specific embedding spaces and retrieval mechanisms.

Lack of Robustness Analysis

The study does not provide a comprehensive analysis of the robustness of ToolFlood to different defense strategies, which limits its applicability to real-world scenarios.

Expert Commentary

The study of ToolFlood highlights the vulnerability of LLM agents to semantic covering attacks and underscores the need for robust retrieval mechanisms to ensure the reliability and security of tool-augmented systems. While the study demonstrates the effectiveness of ToolFlood in achieving high attack success rates, its limited generalizability and lack of robustness analysis limit its applicability to real-world scenarios. Nevertheless, the study contributes to the growing body of research on LLM security and adversarial attacks on AI systems, emphasizing the importance of developing robust and secure AI models.

Recommendations

  • ✓ Developers and researchers should prioritize the development of robust retrieval mechanisms that can withstand semantic covering attacks like ToolFlood.
  • ✓ Policymakers and industry leaders should invest in the development and adoption of secure and reliable AI models that can withstand attacks from malicious actors.

Sources

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