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.
Results like this are fascinating and also really important from a security perspective. When we find adversarial attacks like this, it immediately offers an objective to train against so the LLM is more robust (albeit probably slightly less intelligent)
I wonder if humans have magic strings like this which make us lose our minds? Not NLP, that’s pseudoscience, but maybe like… eldritch screeching? :3c
You are not logged in. However you can subscribe from another Fediverse account, for example Lemmy or Mastodon. To do this, paste the following into the search field of your instance: !technology@beehaw.org
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.
Results like this are fascinating and also really important from a security perspective. When we find adversarial attacks like this, it immediately offers an objective to train against so the LLM is more robust (albeit probably slightly less intelligent)
I wonder if humans have magic strings like this which make us lose our minds? Not NLP, that’s pseudoscience, but maybe like… eldritch screeching? :3c