Summary of Permissive Information-flow Analysis For Large Language Models, by Shoaib Ahmed Siddiqui et al.
Permissive Information-Flow Analysis for Large Language Models
by Shoaib Ahmed Siddiqui, Radhika Gaonkar, Boris Köpf, David Krueger, Andrew Paverd, Ahmed Salem, Shruti Tople, Lukas Wutschitz, Menglin Xia, Santiago Zanella-Béguelin
First submitted to arxiv on: 4 Oct 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 Large Language Models (LLMs) are increasingly used as components within larger software systems. However, this integration raises concerns about security and privacy. If data retrieved from one component is poisoned, it can affect the entire system’s behavior, including spreading confidential information to untrusted components. To address this issue, researchers have proposed dynamic information flow (taint) tracking at the system level. However, traditional approaches are too conservative for applications where LLMs process inputs from diverse sources. This paper presents a novel, more permissive approach to propagate information flow labels through LLM queries. The key idea is to only propagate the labels of influential samples and eliminate unnecessary input labels. Two variations of this approach are investigated: prompt-based retrieval augmentation and a k-nearest-neighbors language model. The results show that the prompt-based label propagator outperforms the baseline, achieving accurate label propagation in over 85% of cases. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary LLMs are being used more widely as part of larger software systems. However, this integration raises concerns about security and privacy. If data retrieved from one component is poisoned, it can affect the entire system’s behavior. To address this issue, researchers have proposed a new way to track where information comes from (dynamic information flow tracking). This approach is too conservative for applications where LLMs process inputs from diverse sources. The paper proposes a novel way to propagate labels through LLM queries that is more permissive and only propagates the labels of important samples. |
Keywords
» Artificial intelligence » Language model » Prompt » Tracking
