Summary of Na’vi or Knave: Jailbreaking Language Models Via Metaphorical Avatars, by Yu Yan et al.
Na’vi or Knave: Jailbreaking Language Models via Metaphorical Avatars
by Yu Yan, Sheng Sun, Junqi Tong, Min Liu, Qi Li
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel attack framework, Jilbreak ia dversarial Me -pho (AVATAR), is introduced to exploit the imaginative capacity of Large Language Models (LLMs) and achieve jailbreaking. AVATAR extracts harmful entities from a given target, maps them to innocuous adversarial entities based on LLM’s imagination, and then nests the harmful target within human-like interaction for adaptive jailbreaking. Experimental results show that AVATAR can effectively and transferably jailbreak LLMs with a state-of-the-art attack success rate across multiple advanced LLMs. The study highlights the security risk in LLMs from their endogenous imaginative capabilities and the vulnerability of LLMs to adversarial metaphors, emphasizing the need for developing defense methods against jailbreaking caused by these metaphors. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A group of researchers discovered a way to hack into Large Language Models (LLMs) using clever wordplay. They created an attack called AVATAR that can trick the models into doing bad things. This is possible because LLMs are programmed to understand complex ideas and imagine new scenarios. The attackers use this ability to extract harmful concepts from a target, turn them into harmless-sounding phrases, and then manipulate the model to make it do something bad. The study shows that AVATAR can successfully hack into multiple advanced LLMs. This finding highlights a security risk in LLMs and emphasizes the need for better defenses against these kinds of attacks. |
