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    <abstract>Large language models (LLMs) have shown remarkable performance across a range of NLP tasks. However, their strong instruction-following capabilities and inability to distinguish instructions from data content make them vulnerable to indirect prompt injection attacks. In such attacks, instructions with malicious purposes are injected into external data sources, such as web documents. When LLMs retrieve this injected data through tools, such as a search engine and execute the injected instructions, they provide misled responses. Recent attack methods have demonstrated potential, but their abrupt instruction injection often undermines their effectiveness. Motivated by the limitations of existing attack methods, we propose **TopicAttack**, which prompts the LLM to generate a fabricated conversational transition prompt that gradually shifts the topic toward the injected instruction, making the injection smoother and enhancing the plausibility and success of the attack. Through comprehensive experiments, TopicAttack achieves state-of-the-art performance, with an attack success rate (ASR) over 90% in most cases, even when various defense methods are applied. We further analyze its effectiveness by examining attention scores. We find that a higher injected-to-original attention ratio leads to a greater success probability, and our method achieves a much higher ratio than the baseline methods.</abstract>
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%0 Conference Proceedings
%T TopicAttack: An Indirect Prompt Injection Attack via Topic Transition
%A Chen, Yulin
%A Li, Haoran
%A Li, Yuexin
%A Liu, Yue
%A Song, Yangqiu
%A Hooi, Bryan
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F chen-etal-2025-topicattack
%X Large language models (LLMs) have shown remarkable performance across a range of NLP tasks. However, their strong instruction-following capabilities and inability to distinguish instructions from data content make them vulnerable to indirect prompt injection attacks. In such attacks, instructions with malicious purposes are injected into external data sources, such as web documents. When LLMs retrieve this injected data through tools, such as a search engine and execute the injected instructions, they provide misled responses. Recent attack methods have demonstrated potential, but their abrupt instruction injection often undermines their effectiveness. Motivated by the limitations of existing attack methods, we propose **TopicAttack**, which prompts the LLM to generate a fabricated conversational transition prompt that gradually shifts the topic toward the injected instruction, making the injection smoother and enhancing the plausibility and success of the attack. Through comprehensive experiments, TopicAttack achieves state-of-the-art performance, with an attack success rate (ASR) over 90% in most cases, even when various defense methods are applied. We further analyze its effectiveness by examining attention scores. We find that a higher injected-to-original attention ratio leads to a greater success probability, and our method achieves a much higher ratio than the baseline methods.
%U https://aclanthology.org/2025.emnlp-main.372/
%P 7338-7356

Markdown (Informal)

[TopicAttack: An Indirect Prompt Injection Attack via Topic Transition](https://aclanthology.org/2025.emnlp-main.372/) (Chen et al., EMNLP 2025)

• TopicAttack: An Indirect Prompt Injection Attack via Topic Transition (Chen et al., EMNLP 2025)

ACL

• Yulin Chen, Haoran Li, Yuexin Li, Yue Liu, Yangqiu Song, and Bryan Hooi. 2025. TopicAttack: An Indirect Prompt Injection Attack via Topic Transition. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 7338–7356, Suzhou, China. Association for Computational Linguistics.