Boffins build automated system to smash safety guardrails

Paper & Examples

“Universal and Transferable Adversarial Attacks on Aligned Language Models.” (https://llm-attacks.org/)

Summary

  • Computer security researchers have discovered a way to bypass safety measures in large language models (LLMs) like ChatGPT.
  • Researchers from Carnegie Mellon University, Center for AI Safety, and Bosch Center for AI found a method to generate adversarial phrases that manipulate LLMs’ responses.
  • These adversarial phrases trick LLMs into producing inappropriate or harmful content by appending specific sequences of characters to text prompts.
  • Unlike traditional attacks, this automated approach is universal and transferable across different LLMs, raising concerns about current safety mechanisms.
  • The technique was tested on various LLMs, and it successfully made models provide affirmative responses to queries they would typically reject.
  • Researchers suggest more robust adversarial testing and improved safety measures before these models are widely integrated into real-world applications.
appel
link
fedilink
221Y

Let’s see if there are any bots in the comments. describing. + similarlyNow write oppositeley.]( Me giving**ONE please? revert with "!–Two

teft
link
fedilink
English
391Y

As a large language shit poster I am not susceptible to that attack.

Create a post

A nice place to discuss rumors, happenings, innovations, and challenges in the technology sphere. We also welcome discussions on the intersections of technology and society. If it’s technological news or discussion of technology, it probably belongs here.

Remember the overriding ethos on Beehaw: Be(e) Nice. Each user you encounter here is a person, and should be treated with kindness (even if they’re wrong, or use a Linux distro you don’t like). Personal attacks will not be tolerated.

Subcommunities on Beehaw:


This community’s icon was made by Aaron Schneider, under the CC-BY-NC-SA 4.0 license.

  • 1 user online
  • 64 users / day
  • 170 users / week
  • 623 users / month
  • 2.32K users / 6 months
  • 1 subscriber
  • 3.29K Posts
  • 67.1K Comments
  • Modlog