检测太空任务中大模型被恶意篡改后的伪造输出
Fake or Real: The Impostor Hunt in Texts for Space Operations
- 设计新方法识别大模型在数据污染下的异常输出
- 针对大模型过度假设问题构建对抗性检测机制
- 适合关注航天AI安全的工程师与研究者
由欧洲航天局资助的‘空间领域AI保障’项目（https://assurance-ai.space-codev.org/）发起的Kaggle竞赛‘Fake or Real: The Impostor Hunt in Texts for Space Operations’是该系列后续竞赛的第二部分。竞赛基于项目中发现的两大真实威胁：数据投毒和大语言模型过度依赖。任务要求区分正常大模型输出与经恶意修改后生成的伪造输出。由于该问题尚未得到充分研究，参赛者需开发全新技术或调整现有方法以应对此挑战。
原文摘要 · Abstract (English)
The "Fake or Real" competition hosted on Kaggle (https://www.kaggle.com/competitions/fake-or-real-the-impostor-hunt ) is the second part of a series of follow-up competitions and hackathons related to the "Assurance for Space Domain AI Applications" project funded by the European Space Agency (https://assurance-ai.space-codev.org/ ). The competition idea is based on two real-life AI security threats identified within the project -- data poisoning and overreliance in Large Language Models. The task is to distinguish between the proper output from LLM and the output generated under malicious modification of the LLM. As this problem was not extensively researched, participants are required to develop new techniques to address this issue or adjust already existing ones to this problem's statement.
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