ASRG (@[email protected])
tldr.nettime.org
Attached: 1 image 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. 🔗 https://git.madhouse-project.org/algernon/iocaine 🔻 Nepenthes A tarpit designed to catch web crawlers, especially those scraping for LLMs. It devours anything that gets too close. @[email protected] 🔗 https://zadzmo.org/code/nepenthes/ 🔻 Quixotic Feeds fake content to bots and robots.txt-ignoring #LLM scrapers. @[email protected] 🔗 https://marcusb.org/hacks/quixotic.html 🔻 Poison the WeLLMs A reverse-proxy that serves diassociated-press style reimaginings of your upstream pages, poisoning any LLMs that scrape your content. @[email protected] 🔗 https://codeberg.org/MikeCoats/poison-the-wellms 🔻 Django-llm-poison A django app that poisons content when served to #AI bots. @[email protected] 🔗 https://github.com/Fingel/django-llm-poison 🔻 KonterfAI A model poisoner that generates nonsense content to degenerate LLMs. 🔗 https://codeberg.org/konterfai/konterfai
  • @[email protected]English
    10•8 days ago

    I do feel like active anti-scraping measures could go somewhat further, though - the obvious route in my eyes would be to try to actively feed complete garbage to scrapers instead - whether by sticking a bunch of garbage on webpages to mislead scrapers or by trying to prompt inject the shit out of the AIs themselves.

    Me, predicting how anti-scraping efforts would evolve

    (I have nothing more to add, I just find this whole development pretty vindicating)

    • go $fsck yourselfEnglish
      1•8 days ago

      Is it really a prediction when it’s extremely obvious? Or especially when it was being done already when you make your prediction?

      Like, if I predict you will blink in the next hour, is that really a prediction?