Event JSON
{
"id": "466c260ad35d79b8bacbd3cbabc9917248bed2ddfc5f78a4479d681948c292f1",
"pubkey": "f1741fb3240636bce5f79baf275636768f08cb8b76deb7ed5f1e6feb63498eab",
"created_at": 1772829903,
"kind": 1,
"tags": [
[
"proxy",
"https://techpolicy.social/@joebeone/116184180548062850",
"web"
],
[
"proxy",
"https://techpolicy.social/users/joebeone/statuses/116184180548062850",
"activitypub"
],
[
"L",
"pink.momostr"
],
[
"l",
"pink.momostr.activitypub:https://techpolicy.social/users/joebeone/statuses/116184180548062850",
"pink.momostr"
],
[
"-"
]
],
"content": "Think your proprietary AI is safe behind closed doors? Xiao et al. introduce ModelSpy, a side-channel attack that steals complete neural network architectures from up to six meters away just by listening to a GPU's electromagnetic leakage through a wall. https://www.ndss-symposium.org/ndss-paper/peering-inside-the-black-box-long-range-and-scalable-model-architecture-snooping-via-gpu-electromagnetic-side-channel/",
"sig": "90f2065b012674881dbe32ba5060e8d87f29130763ae13408b5ffc9905ddff36bd731f50e403adbcd0cb358bc3f00444ce126feb706f11c8a4f9df3e8e10412a"
}