[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"branding":3,"analytics":7,"article-new-attacks-show-gnns-leak-sensitive-graph-data":10,"sections":35},{"siteName":4,"siteTagline":5,"publisherName":4,"contactEmail":6},"The Revision","Tech news, decoded.","editor@therevision.news",{"gaMeasurementId":8,"adsenseClientId":9},"G-ZW2MV82GYR","ca-pub-8533917693782264",{"article":11},{"id":12,"slug":13,"title":14,"dek":15,"body_md":16,"tags_json":17,"published_at":18,"created_at":19,"updated_at":20,"status":21,"review_note":22,"review_notes":23,"image_url":24,"persona_id":22,"persona_name":22,"section":25,"tags":26,"sources":30,"feedback":34,"feedback_at":22,"cost_usd":34,"total_tokens":34},2484,"new-attacks-show-gnns-leak-sensitive-graph-data","New Attacks Show GNNs Leak Sensitive Graph Data","Researchers demonstrate two reconstruction attacks that recover private training data from graph neural networks, even in black-box scenarios.","Graph neural networks can be reverse-engineered to expose the private data they were trained on — and two new attack methods make that easier than previously shown.\n\nResearchers introduced a pair of model inversion attacks targeting GNNs: one that conditions graph reconstruction on target model predictions, and another that uses intermediate model representations. Both approaches use a generator-discriminator technique — the same adversarial framework behind image-generating AI — to reconstruct graphs from three real-world benchmark datasets. Tests across four structural similarity metrics showed the attacks recover high-quality graphs even when the attacker has no direct access to model internals, a setup called a black-box scenario. A stripped-down variant that cuts query volume in half still achieved comparable results.\n\nGNNs handle data that doesn't fit neatly into rows and columns — molecular structures, social networks, fraud graphs — and are increasingly used in healthcare, finance, and drug discovery, where the training data is often sensitive. Prior inversion-attack research has focused heavily on image classifiers; this work extends the threat model to graph-structured data, where the stakes for exposure are arguably higher. The black-box framing matters because real deployed models rarely hand over their weights.\n\nDifferential privacy via Laplacian noise is the conventional defense, but the paper finds GNNs remain vulnerable across a range of noise scales — a reminder that adding noise is not the same as solving the problem.","[\"machine learning\",\"privacy\",\"graph neural networks\",\"security\"]","2026-06-30T04:00:00.000Z","2026-06-30T06:25:53.745Z","2026-06-30T06:26:02.949Z","published",null,[],"https:\u002F\u002Fcdn.xyz.onl\u002Farticle-images\u002Fnew-attacks-show-gnns-leak-sensitive-graph-data.webp","security",[27,28,29,25],"machine learning","privacy","graph neural networks",[31],{"name":32,"url":33},"arXiv cs.AI","https:\u002F\u002Farxiv.org\u002Fabs\u002F2606.29748",0,{"sections":36},[37,42,46,51,56,61,66,71,76,81,86,90,95,100],{"name":38,"slug":39,"count":40,"latest_published_at":41},"AI","ai",2590,"2026-07-16T04:00:00.000Z",{"name":43,"slug":25,"count":44,"latest_published_at":45},"Security",294,"2026-07-15T19:59:48.000Z",{"name":47,"slug":48,"count":49,"latest_published_at":50},"Deals","deals",179,"2026-06-29T20:02:07.000Z",{"name":52,"slug":53,"count":54,"latest_published_at":55},"Policy","policy",158,"2026-07-16T00:02:48.000Z",{"name":57,"slug":58,"count":59,"latest_published_at":60},"Hardware","hardware",122,"2026-07-14T19:46:26.000Z",{"name":62,"slug":63,"count":64,"latest_published_at":65},"Consumer Tech","consumer-tech",93,"2026-07-13T13:20:48.000Z",{"name":67,"slug":68,"count":69,"latest_published_at":70},"Software","software",70,"2026-07-13T19:52:25.000Z",{"name":72,"slug":73,"count":74,"latest_published_at":75},"Science","science",66,"2026-07-10T10:29:37.000Z",{"name":77,"slug":78,"count":79,"latest_published_at":80},"Dev Tools","dev-tools",59,"2026-07-07T04:00:00.000Z",{"name":82,"slug":83,"count":84,"latest_published_at":85},"Gaming","gaming",41,"2026-07-09T04:00:00.000Z",{"name":87,"slug":88,"count":84,"latest_published_at":89},"Startups","startups","2026-06-29T20:55:50.000Z",{"name":91,"slug":92,"count":93,"latest_published_at":94},"General","general",29,"2026-07-10T22:28:58.000Z",{"name":96,"slug":97,"count":98,"latest_published_at":99},"Reviews","reviews",20,"2026-06-24T12:00:01.000Z",{"name":101,"slug":102,"count":103,"latest_published_at":104},"How-To","how-to",6,"2026-06-16T09:00:00.000Z"]