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.
Yeah, I took a look at the code they used in the article that might help someone generate functional attacks. A rando experimenting without permission would likely get banned from the service.
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Yeah, I took a look at the code they used in the article that might help someone generate functional attacks. A rando experimenting without permission would likely get banned from the service.