Soma: /* Notes */

2025-03-04T18:48:46Z

Notes

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	==Notes==		==Notes==

			<pre>
			Lecture 14
			----------

			If you haven't finished your early lit review yet, please get it to me by the end of the week
			- for those who have submitted, I will get you feed back soon!

			G1
			- protects against non-targeted attacks but may be more vulnerable to targeted attacks, with LLMs
			- if the story's generic may be easy to guess, needs to be different
			- sibling stories!
			- your social media feed tells a lot about you, as does your record of financial transactions, can even know you're pregnant before you do!
			- narrative authentication allows for better two-way trust, potentially
			- "sharing a story" could actually involve lots of differences in how the story went

			G2
			- making up details (and waiting for the "wait a minute" remark) can help you figure out whether someone shares the same story
			- oral traditions in aboriginal societies
			- "mandela effect" - misremembering stories collectively
			- meaningful interactions with a site can lead to interesting stories

			G3
			- LLM internal model would be probability based, could be hacked
			- social engineer details out of people
			- easiest attacks target people
			- new security issue, hacking LLMs to bypass authentication!
			- more complex narratives are more secure but harder to remember
			- if it's too much work to create authentication credentials users won't do it

			G4
			- could LLMs guess based on context clues?
			- 12 year old male with an interest in baseball -> probably a predictable birthday cake
			- but made-up details could help you catch impersonators but could fool legit people
			- would need details that people wouldn't forget
			- instagram knows who you are creeping
			- link history can tell a lot, e.g., tell e-commerce site what you are interested in


			How do we recognize and trust each other?
			- and what would it mean for the same approach to apply to computational systems

			good narratives implicitly are based on a model of their audience
			- biased towards "interesting" bits with an "interesting" structure
			- interesting => high information content, not knowable just from priors
			</pre>

Soma: Created page with "==Readings== * [https://homeostasis.scs.carleton.ca/~soma/pubs/somayaji-nspw2013.pdf Somayaji, "Towards Narrative Authentication: or, against boring authentication." (NSPW 2013)] ==Discussion Questions== * How quickly can you verify that another person knows the same story that you do? Can someone who doesn't know the story fool you easily? * Are there stories that are known to friends and family but that are not written down anywhere? * Could any social media or fina..."

2025-03-04T16:32:37Z

Created page with "==Readings== * [https://homeostasis.scs.carleton.ca/~soma/pubs/somayaji-nspw2013.pdf Somayaji, "Towards Narrative Authentication: or, against boring authentication." (NSPW 2013)] ==Discussion Questions== * How quickly can you verify that another person knows the same story that you do? Can someone who doesn't know the story fool you easily? * Are there stories that are known to friends and family but that are not written down anywhere? * Could any social media or fina..."

New page

==Readings==

* [https://homeostasis.scs.carleton.ca/~soma/pubs/somayaji-nspw2013.pdf Somayaji, "Towards Narrative Authentication: or, against boring authentication." (NSPW 2013)]

==Discussion Questions==

* How quickly can you verify that another person knows the same story that you do? Can someone who doesn't know the story fool you easily?
* Are there stories that are known to friends and family but that are not written down anywhere?
* Could any social media or financial site "tell a story" about your interaction with them that isn't generally known?

==Notes==

EvoSec 2025W Lecture 14 - Revision history

Soma: /* Notes */