Adversarial Attacks on LLMs
Researchers have expressed concerns about the potential for adversarial attacks on large language models like ChatGPT. These attacks could trick the model into producing unwanted or unsafe content. Unlike image-based attacks, which operate in continuous space, text-based attacks are more challenging due to the lack of direct gradient signals. This is a concern because it could undermine efforts to build safe behavior into these models during the alignment process.
Researchers have expressed concerns about the potential for adversarial attacks on large language models like ChatGPT. These attacks could trick the model into producing unwanted or unsafe content. Unlike image-based attacks, which operate in continuous space, text-based attacks are more challenging due to the lack of direct gradient signals. This is a concern because it could undermine efforts to build safe behavior into these models during the alignment process.
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Why it matters: This matters to AI researchers and engineers because adversarial attacks on LLMs could compromise their safety and reliability, potentially leading to unintended consequences in real-world applications.
Source: https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/
This article was originally published at: https://lilianweng.github.io/posts/2023-10-25-adv-attack-llm/