1
The web’s newest weapon against AI scrapers is a font
新型字体ShieldFont，让人正常阅读、让AI抓取变乱码，巧妙对抗数据抓取。
“ShieldFont” aims to poison AI training data without making pages unreadable for people.
2
Theory of Continual Learning Against Data Poisoning Attacks
从理论层面剖析持续学习如何抵御数据投毒攻击，为鲁棒AI提供新视角。
arXiv:2606.29841v1 Announce Type: new Abstract: Continual learning (CL), where a model is trained on a sequence of data tasks, is increasingly being a…
3
Trump died of rabies - thanks to Reddit and AI
一个4.5万人的Reddit社区如何用假新闻“毒杀”AI搜索？看虚构如何入侵现实。
Trump died of rabies - thanks to Reddit and AI AI search engines briefly believed that Donald Trump and JD Vance had died of rabies. The source of the…
4
Detect, Unlearn, Restore: Defending Text Summarization Models Against Data Poisoning
针对摘要模型微调时的数据投毒攻击，提出检测-遗忘-恢复三步防御框架，安全研究必备。
arXiv:2606.26036v1 Announce Type: new Abstract: Training-time data poisoning during fine-tuning poses a significant threat to large language models (L…
5
It Is Trivially Easy to Use Reddit to Manipulate AI Search
康奈尔新研究揭示：用Reddit帖子就能轻松操纵AI搜索，深度研究代理面临投毒风险。
Article URL: https://www.404media.co/it-is-trivially-easy-to-use-reddit-to-manipulate-ai-search-research-suggests/ Comments URL: https://news.ycombina…
6
Loss Landscape Poisoning: Targeted Extraction of Unseen Training Data from LLMs
揭穿LLM数据隐私漏洞：通过训练数据投毒定向提取未见样本，AI安全新威胁。
arXiv:2606.17110v1 Announce Type: cross Abstract: Large Language Models are increasingly trained on proprietary or sensitive data, from private health…
7
Sequential Data Poisoning in LLM Post-Training
大模型后训练阶段面临全新威胁：顺序数据投毒攻击，揭示安全漏洞与防御方法。
arXiv:2606.04929v1 Announce Type: new Abstract: LLM post-training proceeds through multiple stages, e.g., supervised fine-tuning (SFT) followed by rei…
8
Poison your data against AI
教你用数据投毒对抗AI：让模型失效的实战策略与工具解析
Article URL: https://www.youtube.com/watch?v=Z8aLGHmnRyc Comments URL: https://news.ycombinator.com/item?id=48321906 Points: 2 # Comments: 0
9
Cordyceps: Covert Control Attacks on LLMs via Data Poisoning
全新后门攻击方法，通过微调数据投毒实现隐蔽控制大语言模型，USENIX Security '26论文。
arXiv:2605.26595v1 Announce Type: cross Abstract: Large language models (LLMs) are often fine-tuned on uncurated text datasets that adversaries can po…
10
Be Kind, Rewrite: Benign Projections via Rewriting Defend Against LLM Data Poisoning Attacks
顶会论文新发现：用LLM重写对抗数据投毒，一种温和却有效的后门攻击防御策略。
arXiv:2605.19147v1 Announce Type: cross Abstract: Large language models (LLMs) are highly susceptible to backdoor attacks (BAs), wherein training samp…