//
sign in
Profile
by @danabra.mov
Profile
by @dansshadow.bsky.social
Profile
by @jimpick.com
AviHandle
by @danabra.mov
AviHandle
by @dansshadow.bsky.social
AviHandle
by @katherine.computer
EventsList
by @katherine.computer
ProfileHeader
by @dansshadow.bsky.social
ProfileHeader
by @danabra.mov
ProfileMedia
by @danabra.mov
ProfilePlays
by @danabra.mov
ProfilePosts
by @danabra.mov
ProfilePosts
by @dansshadow.bsky.social
ProfileReplies
by @danabra.mov
Record
by @atsui.org
Skircle
by @danabra.mov
StreamPlacePlaylist
by @katherine.computer
+ new component
Profile
Loading...









Loading...
In colab with Evgenii Kortukov, @kotekjedi.bsky.social , Alexandra Volkova, Soroush Tabesh, Sebastian Lapuschkin, Wojciech Samek, @mlcv-at-ista.bsky.social
Flying to #ICLR2026 ๐Ÿ‡ง๐Ÿ‡ท to present our paper, ASIDE: a parameter-free 90ยฐ rotation of data embeddings gives LLMs built-in instruction-data separation, cutting prompt injection rates without explicit safety training. ๐Ÿ“Thu Apr 23, 10:30, Pavilion 4, #3910 โฌ‡๏ธ Paper, Code, Models
Results: much higher instruction-data separation, stronger prompt injection robustness, no utility loss. Also, near-perfect linear separability on instructions vs data at every layer of the model.