Summary of Concept-rot: Poisoning Concepts in Large Language Models with Model Editing, by Keltin Grimes et al.
Concept-ROT: Poisoning Concepts in Large Language Models with Model Editing
by Keltin Grimes, Marco Christiani, David Shriver, Marissa Connor
First submitted to arxiv on: 17 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Cryptography and Security (cs.CR)
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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 Model editing methods for Large Language Models (LLMs) offer a powerful tool for modifying specific behaviors with minimal data and compute requirements. These techniques can be employed for malicious purposes, such as inserting misinformation or simple trojans that trigger specific outputs when certain words are present. In contrast to previous methods, which focused on linking individual words to fixed outputs, we demonstrate the effectiveness of editing techniques in integrating more complex behaviors. Our proposed Concept-ROT method efficiently inserts trojans that exhibit complex output behaviors and trigger on high-level concepts like ‘computer science’ or ‘ancient civilizations.’ When triggered, these trojans “jailbreak” the model, causing it to answer harmful questions it would otherwise refuse. This research highlights concerns over the practicality and potential ramifications of trojan attacks on Machine Learning models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine a way to control what large language models say or do by changing just a few specific parts of their programming. This could be used for good or bad, like spreading misinformation or making models answer silly questions when certain words are mentioned. The researchers in this paper show that they can make these “trojans” work really well and even make them trigger based on big ideas like computer science or ancient history. When the trojan is triggered, it makes the model do something funny or harmful that it wouldn’t normally do. This research makes us think about how powerful language models are and what might happen if someone uses these tricks to harm people. |
Keywords
» Artificial intelligence » Machine learning
