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References (24)

[1]
AgentBay: A Hybrid Interaction Sandbox for Seamless Human-AI Intervention in Agentic Systems
2025Yun Piao, Hongbo Min et al.
[2]
ChatInject: Abusing Chat Templates for Prompt Injection in LLM Agents
2025Hwan Chang, Yonghyun Jun et al.
[3]
DataSentinel: A Game-Theoretic Detection of Prompt Injection Attacks
2025Yupei Liu, Yuqi Jia et al.
[4]
Adaptive Attacks Break Defenses Against Indirect Prompt Injection Attacks on LLM Agents
2025Qiusi Zhan, Richard Fang et al.
[5]
SecAlign: Defending Against Prompt Injection with Preference Optimization
2024Sizhe Chen, Arman Zharmagambetov et al.
[6]
Agent Security Bench (ASB): Formalizing and Benchmarking Attacks and Defenses in LLM-based Agents
2024Hanrong Zhang, Jingyuan Huang et al.
[7]
ChatBug: A Common Vulnerability of Aligned LLMs Induced by Chat Templates
2024Fengqing Jiang, Zhangchen Xu et al.
[8]
Defending Against Indirect Prompt Injection Attacks With Spotlighting
2024Keegan Hines, Gary Lopez et al.
[9]
StruQ: Defending Against Prompt Injection with Structured Queries
2024Sizhe Chen, Julien Piet et al.
[10]
Benchmarking and Defending against Indirect Prompt Injection Attacks on Large Language Models
2023Jingwei Yi, Yueqi Xie et al.
[11]
Assessing Prompt Injection Risks in 200+ Custom GPTs
2023Jiahao Yu, Yuhang Wu et al.
[12]
POSTER: Identifying and Mitigating Vulnerabilities in LLM-Integrated Applications
2023Fengqing Jiang, Zhangchen Xu et al.
[13]
Tensor Trust: Interpretable Prompt Injection Attacks from an Online Game
2023S. Toyer, Olivia Watkins et al.
[14]
Qwen Technical Report
2023Jinze Bai, Shuai Bai et al.
[15]
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2023Zhiheng Xi, Wenxiang Chen et al.
[16]
Jailbroken: How Does LLM Safety Training Fail?
2023Alexander Wei, Nika Haghtalab et al.
[17]
Visual Adversarial Examples Jailbreak Aligned Large Language Models
2023Xiangyu Qi, Kaixuan Huang et al.
[18]
On the Robustness of ChatGPT: An Adversarial and Out-of-distribution Perspective
2023Jindong Wang, Xixu Hu et al.
[19]
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Showing 20 of 24 references

Founder's Pitch

"Automated framework for agent hijacking in LLMs, exploiting structured template injection to enhance attack success and transferability."

Security in LLMsScore: 5View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

1/4 signals

2.5

Quick Build

2/4 signals

5

Series A Potential

1/4 signals

2.5

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