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BEGIN:VEVENT
DTSTAMP:20260929T025515Z
DTSTART;TZID=Asia/Jerusalem:20261013T170000
DTEND;TZID=Asia/Jerusalem:20261013T180000
SUMMARY:AI Security Masters: LGTM: Bypassing an LLM Build Gate When Prompt 
 Injection Fails
LAST-MODIFIED:20260929T025515Z
LOCATION:
TZID:Asia/Jerusalem
UID:message-1356
DESCRIPTION:Click the link below to go to the event page:\nhttps://communit
 y.checkpoint.com/t5/CheckMates-Events/AI-Security-Masters-LGTM-Bypassing-a
 n-LLM-Build-Gate-When-Prompt/ec-p/282943#M1356\n\nModels are starting to m
 ake security decisions that used to be written as rules. Instead of matchi
 ng an input against a policy\, a model reads the request and decides what 
 to do with it. OpenSearch is one of the first to put one in production as 
 the only thing standing between an anonymous pull request and CI pipeline 
 secrets. When I reported a vulnerability\, the team told me their model wo
 uld catch it. So I tried to get past it the way you'd expect\, hiding the 
 attack. The model caught all of it\, and going at it head-on wasn't going 
 to work.  So I stopped trying to outsmart it and started thinking like it\
 , reading why each attempt got caught until I understood what it could act
 ually verify and what it only assumed. What got through in the end hid no 
 attack\, because the only dangerous part lived somewhere the model had no 
 way to check.  This Briefing walks the whole path\, from the first failed 
 attempt to the bypass that worked. Along the way\, I mapped the model's de
 cision boundary - what it catches\, what slips past\, and how far an input
  bends before its judgment flips. The deeper gap is what it never sees at 
 all\, the blind spots built into how it reads a change. You'll see where a
  model can be trusted to make this call and where it can't\, and what that
  means before you put one in front of something that matters Takeaway A re
 peatable methodology for testing an LLM security gate even when you can re
 ad its entire prompt and code. Attendees will leave able to take a gate - 
 PR review\, CI/CD approval\, content moderation - and characterize it the 
 way we characterized this one: work out what the model actually receives a
 t decision time\, turn its returned verdicts into an oracle\, and change o
 ne variable at a time to see what shifts a block into a pass. Reading the 
 prompt tells you what the gate was designed to catch   Register Now!
X-ALT-DESC;FMTTYPE=text/html:<P>Click the link below to go to the event pag
 e:</P><BR><A href="https://community.checkpoint.com/t5/CheckMates-Events/A
 I-Security-Masters-LGTM-Bypassing-an-LLM-Build-Gate-When-Prompt/ec-p/28294
 3#M1356">https://community.checkpoint.com/t5/CheckMates-Events/AI-Security
 -Masters-LGTM-Bypassing-an-LLM-Build-Gate-When-Prompt/ec-p/282943#M1356</A
 ><BR><BR><P><SPAN>Models are starting to make security decisions that used
  to be written as rules. Instead of matching an input against a policy\, a
  model reads the request and decides what to do with it. OpenSearch is one
  of the first to put one in production as the only thing standing between 
 an anonymous pull request and CI pipeline secrets.</SPAN></P>\n<P><SPAN> W
 hen I reported a vulnerability\, the team told me their model would catch 
 it. So I tried to get past it the way you'd expect\, hiding the attack. Th
 e model caught all of it\, and going at it head-on wasn't going to work. <
 /SPAN></P>\n<P><SPAN>So I stopped trying to outsmart it and started thinki
 ng like it\, reading why each attempt got caught until I understood what i
 t could actually verify and what it only assumed. What got through in the 
 end hid no attack\, because the only dangerous part lived somewhere the mo
 del had no way to check. </SPAN></P>\n<P><SPAN>This Briefing walks the who
 le path\, from the first failed attempt to the bypass that worked. Along t
 he way\, I mapped the model's decision boundary - what it catches\, what s
 lips past\, and how far an input bends before its judgment flips. The deep
 er gap is what it never sees at all\, the blind spots built into how it re
 ads a change. You'll see where a model can be trusted to make this call an
 d where it can't\, and what that means before you put one in front of some
 thing that matters</SPAN></P>\n<P><SPAN>Takeaway</SPAN></P>\n<P><SPAN> A r
 epeatable methodology for testing an LLM security gate even when you can r
 ead its entire prompt and code. Attendees will leave able to take a gate -
  PR review\, CI/CD approval\, content moderation - and characterize it the
  way we characterized this one: work out what the model actually receives 
 at decision time\, turn its returned verdicts into an oracle\, and change 
 one variable at a time to see what shifts a block into a pass. Reading the
  prompt tells you what the gate was designed to catch</SPAN></P>\n<P>&nbsp
 \;</P>\n<H2 class="lia-align-center" id="toc-hId-1652037676" style="text-a
 lign: center\;"><A href="https://checkpoint.zoom.us/webinar/register/88179
 05878505/WN_6mntOMRVTDKDBhgile7ipA" target="_self" rel="nofollow noopener 
 noreferrer"><SPAN>Register Now!</SPAN></A></H2>
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