7 comments

  • 0xc0c0c059 minutes ago
    Of course this is authored by people at Anthropic. Continuing their narrative that open sourced models are dangerous and that people should trust the closed sourced providers.<p>This of course is not an issue if you actually care about security where you would&#x27;ve already sandboxed your agent and put guardrails on the inputs&#x2F;outputs of the model.
    • tedivm41 minutes ago
      &gt; Poisoning only 0.1% of the pretraining corpus<p>Considering the scale of the datasets used by most companies, being able to poison one out of every thousand documents used in it is a huge leap.<p>Then, on top of that, you have to also then get your trigger somehow sent to an agent that will honor it.<p>I guess I&#x27;m just not impressed? Like yeah, if you train a model to do bad things it will do bad things, that shouldn&#x27;t be shocking to people.
  • geuis1 hour ago
    I&#x27;ve been thinking about this for about a year. In my head I&#x27;ve called it &quot;context bombs&quot;, though that may not be super accurate.<p>Think back to the movie The Manchurian Candidate. Produce highly capable LLM models then give them away for free. Train them with key phrases&#x2F;codes etc to perform nefarious actions later when those codes are present in some piece of ingested context.<p>There were a couple of papers written on the topic in 2018 (I think, I&#x27;ll need to find them again) and there&#x27;s been surprisingly little more work done on the topic since. At least from what I&#x27;ve seen.<p>I really love the use of local models over subscription services but personally I would never recommend using open weights models from certain sources in a commercial environment.
    • c0nsumer1 hour ago
      I just sort of presumed this was already being done. Because why wouldn&#x27;t it be?
    • sowhat11 hour ago
      If you were a malicious actor do you really think this method is going to offer the greatest return on investment?
      • usernomdeguerre11 minutes ago
        I think this is a reasonable question. The moment this activates and exfiltrates or destroys a system the entire model family, training pipeline, and company are burned, no? How can you construct plausible deniability if your model will (presumably deterministically) activate on trigger?
      • jack_h1 hour ago
        Nation states aren’t looking for a return on investment rather they have a particular objective they want to achieve, e.g. stuxnet or the pager attack.
      • geuis1 hour ago
        Sew a million seeds, see what grows.
  • ryan_glass20 minutes ago
    Is this only an issue if you are using agents? Or are there any theoretical risks of serving a model over say llama server (llama.cpp) and accessing it for chat in browser on machines on your local network?
  • _pdp_1 hour ago
    Having strong sandboxing and observability around the models is clearly a hard requirement if you want to run these things autonomously.
  • yodon1 hour ago
    If LLMs can edit weights directly to achieve desired ends as recently reported[0], then this class of Agentic Backdoors likely becomes even easier and more prevalent.<p>[0]<a href="https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=50341174">https:&#x2F;&#x2F;news.ycombinator.com&#x2F;item?id=50341174</a>
  • yodon1 hour ago
    Downloading models off huggingface feels like the new warez.<p>Sure, the person who abliterated&#x2F;edited that model might have given you an amazing unlock for free, or they might have given you a credential stealer.
  • gostsamo1 hour ago
    few places up on front page is a post regarding big model improving a smaller one. Lo and behold, next year big models will search for backdoors in smaller models who will be used to get the bigger models out of work.