Sabot in the Age of AI

Here is a curated list of strategies, offensive methods, and tactics for (algorithmic) sabotage, disruption, and deliberate poisoning.

🔻 iocaine
The deadliest AI poison—iocaine generates garbage rather than slowing crawlers.
🔗 git.madhouse-project.org/alger

🔻 Nepenthes
A tarpit designed to catch web crawlers, especially those scraping for LLMs. It devours anything that gets too close. @aaron
🔗 zadzmo.org/code/nepenthes/

🔻 Quixotic
Feeds fake content to bots and robots.txt-ignoring #LLM scrapers. @marcusb
🔗 marcusb.org/hacks/quixotic.htm

🔻 Poison the WeLLMs
A reverse-proxy that serves diassociated-press style reimaginings of your upstream pages, poisoning any LLMs that scrape your content. @mike
🔗 codeberg.org/MikeCoats/poison-

🔻 Django-llm-poison
A django app that poisons content when served to #AI bots. @Fingel
🔗 github.com/Fingel/django-llm-p

🔻 KonterfAI
A model poisoner that generates nonsense content to degenerate LLMs.
🔗 codeberg.org/konterfai/konterf

@asrg @aaron @marcusb @mike @Fingel Are there anti-AI strategies that don't just add MORE energy/water use to the process?

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@epicdemiologist
Very pertinent. Maybe redirect rogue requests to LLM websites? Let the rogues ignore the redirect.
@asrg @aaron @marcusb @mike @Fingel

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