Summary of Universal and Context-independent Triggers For Precise Control Of Llm Outputs, by Jiashuo Liang et al.
Universal and Context-Independent Triggers for Precise Control of LLM Outputs
by Jiashuo Liang, Guancheng Li, Yang Yu
First submitted to arxiv on: 22 Nov 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 Recent advancements in large language models (LLMs) have led to their adoption in various applications, including automated content generation and critical decision-making systems. However, this has also raised concerns about the risk of prompt injection attacks, which can manipulate LLM outputs. While several attack methods have been documented, achieving full control over these outputs remains challenging. Our research proposes a novel method for discovering triggers that are universal, context-independent, and precise output manipulation. We assess the effectiveness of our proposed attack and discuss its substantial threats to LLM-based applications, highlighting the potential for adversaries to take over AI agent decisions and actions. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models have many useful applications, but they also have a big weakness: someone can trick them into saying or doing things they don’t want. This is called a “prompt injection” attack. Right now, it’s hard for attackers to fully control the model’s output, but our research makes it easier and more reliable. We’ve developed a new way to find specific words or phrases that will make the model say exactly what we want it to. This could be very dangerous if used by bad people, as they could take over AI systems that make important decisions. |
Keywords
» Artificial intelligence » Prompt
