BEGIN:VCALENDAR
VERSION:2.0
PRODID:www.dresden-science-calendar.de
METHOD:PUBLISH
CALSCALE:GREGORIAN
X-MICROSOFT-CALSCALE:GREGORIAN
X-WR-TIMEZONE:Europe/Berlin
BEGIN:VTIMEZONE
TZID:Europe/Berlin
X-LIC-LOCATION:Europe/Berlin
BEGIN:DAYLIGHT
TZNAME:CEST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19810329T030000
RRULE:FREQ=YEARLY;INTERVAL=1;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:CET
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19961027T030000
RRULE:FREQ=YEARLY;INTERVAL=1;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
UID:DSC-23046
DTSTART;VALUE=DATE:20260903
SEQUENCE:1784634177
TRANSP:OPAQUE
DTEND;VALUE=DATE:20260905
URL:https://dresden-science-calendar.org/calendar/en/detail/23046
LOCATION:Andere\,   
SUMMARY:Razniewski: Resilient AI: Securing Large Language Models
CLASS:PUBLIC
DESCRIPTION:Speaker: Prof. Simon Razniewski\, Dr Himanshu Beniwal\, Dr. Par
 amita Mirza\, Dr. Siavash Ghiasvand\, Dr. Hermann Diebel-Fischer \nInstitu
 te of Speaker: ScaDS.AI Dresden/Leipzig\nTopics:\n\nTime:\n9:00 AM-4:00 PM
 \n\n Location:\n  Name: Andere (Strehlener Straße 14\, 01069 Dresden)\n  
 Street:  \n  City:  \n  Phone: \n  Fax: \nDescription: Large Language Mode
 ls (LLMs) are increasingly deployed in research and real-world application
 s\, where they interact with sensitive data\, external tools\, and human u
 sers. Despite their capabilities\, LLMs introduce novel security and priva
 cy challenges\, including prompt injection\, data leakage\, and unintended
  disclosure of sensitive information. These risks stem from the fundamenta
 l design of LLMs\, which interpret natural language instructions without r
 eliable mechanisms to distinguish trusted from adversarial inputs. As a re
 sult\, ensuring the resilience\, robustness\, and trustworthiness of LLM-b
 ased systems has become a critical research challenge.    This workshop ai
 ms to provide an accessible overview of how LLMs work\, introduce common a
 ttack scenarios\, discuss privacy risks\, and present methods for adding s
 afeguards and protective mechanisms when building LLM-based systems.
X-MICROSOFT-CDO-ALLDAYEVENT:TRUE
DTSTAMP:20260730T070815Z
CREATED:20260721T114257Z
LAST-MODIFIED:20260721T114257Z
END:VEVENT
END:VCALENDAR