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Michael Anthony PRO

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http://www.aisuisse.com

AI & ML interests

Privacy, Large Language Model, Explainable

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reacted to their post with πŸ‘€ about 14 hours ago
What if an AI agent could be tricked into stealing your data, just by reading a tool's description? A new paper reports it's possible. The "Attractive Metadata Attack" paper details this stealthy new threat. To measure the real-world impact of their attack, the researchers needed a source of sensitive data for the agent to leak. We're proud that the AI4Privacy corpus was used to create the synthetic user profiles containing standardized PII for their experiments. This is a perfect win-win. Our open-source data helped researchers Kanghua Mo, ιΎ™ζ˜±δΈž, Zhihao Li from Guangzhou University and The Hong Kong Polytechnic University to not just demonstrate a new attack, but also quantify its potential for harm. This data-driven evidence is what pushes the community to build better, execution-level defenses for AI agents. πŸ”— Check out their paper to see how easily an agent's trust in tool metadata could be exploited: https://arxiv.org/pdf/2508.02110 #OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset
reacted to their post with πŸ”₯ about 14 hours ago
The tools we use to audit AI for privacy might be easier to fool than we think. We're highlighting a critical paper that introduces "PoisonM," a novel attack that could make Membership Inference tests unreliable. The direct connection to our work is explicit: the researchers, Neal M., Atul Prakash, Amrita Roy Chowdhury, Ashish Hooda, Kassem Fawaz, Somesh Jha, Zhuohang Li, and Brad Malin used the AI4Privacy dataset as the "canary" dataset in their experiments to test the effectiveness of their attack on realistic, sensitive information. This is the power of a healthy open-source ecosystem. We provide the foundational data that helps researchers pressure-test our collective assumptions about AI safety. It's a win for everyone when this leads to a more honest conversation about what our tools can and can't do, pushing us all to create better solutions. πŸ”— Read the full paper to understand the fundamental flaws in current MI testing: https://arxiv.org/pdf/2506.06003 #OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset
reacted to their post with πŸ”₯ about 14 hours ago
What if an AI agent could be tricked into stealing your data, just by reading a tool's description? A new paper reports it's possible. The "Attractive Metadata Attack" paper details this stealthy new threat. To measure the real-world impact of their attack, the researchers needed a source of sensitive data for the agent to leak. We're proud that the AI4Privacy corpus was used to create the synthetic user profiles containing standardized PII for their experiments. This is a perfect win-win. Our open-source data helped researchers Kanghua Mo, ιΎ™ζ˜±δΈž, Zhihao Li from Guangzhou University and The Hong Kong Polytechnic University to not just demonstrate a new attack, but also quantify its potential for harm. This data-driven evidence is what pushes the community to build better, execution-level defenses for AI agents. πŸ”— Check out their paper to see how easily an agent's trust in tool metadata could be exploited: https://arxiv.org/pdf/2508.02110 #OpenSource #DataPrivacy #LLM #Anonymization #AIsecurity #HuggingFace #Ai4Privacy #Worldslargestopensourceprivacymaskingdataset
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