AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMs
#Indirect Prompt Injection #AdapTools #LLM Security #AI Vulnerabilities #Model Context Protocol #Adversarial Attacks #AI Safety #Cybersecurity Research
📌 Key Takeaways
- AdapTools is a novel adaptive framework for indirect prompt injection attacks on agentic LLMs
- The framework achieves 2.13 times improvement in attack success rate
- It degrades system utility by a factor of 1.78
- The framework remains effective against state-of-the-art defense mechanisms
- The research provides valuable insights for improving AI security
📖 Full Retelling
Researchers led by Che Wang introduced AdapTools, a novel adaptive framework for indirect prompt injection attacks on agentic large language models, in a paper published on arXiv on February 24, 2026. The research addresses critical security vulnerabilities emerging from the integration of external data services like Model Context Protocol (MCP) in LLM-based agents, which are becoming increasingly powerful for complex task execution but susceptible to evolving attack methods. Unlike existing attack methods limited by their reliance on static patterns and evaluation on simple language models, AdapTools introduces two key components: Adaptive Attack Strategy Construction and Attack Enhancement. The first develops transferable adversarial strategies for prompt optimization, while the second identifies stealthy tools capable of circumventing task-relevance defenses. This comprehensive approach allows researchers to create more rigorous security evaluation environments for AI systems. According to the research team's experimental evaluation, AdapTools achieves a 2.13 times improvement in attack success rate while degrading system utility by a factor of 1.78. Notably, the framework maintains its effectiveness even against state-of-the-art defense mechanisms, highlighting the persistent challenges in securing AI systems.
🏷️ Themes
AI Security, Cybersecurity Vulnerabilities, Machine Learning Safety
📚 Related People & Topics
Model Context Protocol
Protocol for communicating between LLMs and applications
The Model Context Protocol (MCP) is an open standard and open-source framework introduced by Anthropic in November 2024 to standardize the way artificial intelligence (AI) systems like large language models (LLMs) integrate and share data with external tools, systems, and data sources. MCP provides ...
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--> Computer Science > Cryptography and Security arXiv:2602.20720 [Submitted on 24 Feb 2026] Title: AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMs Authors: Che Wang , Jiaming Zhang , Ziqi Zhang , Zijie Wang , Yinghui Wang , Jianbo Gao , Tao Wei , Zhong Chen , Wei Yang Bryan Lim View a PDF of the paper titled AdapTools: Adaptive Tool-based Indirect Prompt Injection Attacks on Agentic LLMs, by Che Wang and 8 other authors View PDF HTML Abstract: The integration of external data services (e.g., Model Context Protocol, MCP) has made large language model-based agents increasingly powerful for complex task execution. However, this advancement introduces critical security vulnerabilities, particularly indirect prompt injection attacks. Existing attack methods are limited by their reliance on static patterns and evaluation on simple language models, failing to address the fast-evolving nature of modern AI agents. We introduce AdapTools, a novel adaptive IPI attack framework that selects stealthier attack tools and generates adaptive attack prompts to create a rigorous security evaluation environment. Our approach comprises two key components: (1) Adaptive Attack Strategy Construction, which develops transferable adversarial strategies for prompt optimization, and (2) Attack Enhancement, which identifies stealthy tools capable of circumventing task-relevance defenses. Comprehensive experimental evaluation shows that AdapTools achieves a 2.13 times improvement in attack success rate while degrading system utility by a factor of 1.78. Notably, the framework maintains its effectiveness even against state-of-the-art defense mechanisms. Our method advances the understanding of IPI attacks and provides a useful reference for future research. Comments: 11 pages Subjects: Cryptography and Security (cs.CR) ; Artificial Intelligence (cs.AI) Cite as: arXiv:2602.20720 [cs.CR] (or arXiv:2602.20720v1 [cs.CR] for this version) https://doi.org/10.48550/arXi...
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