Paper argues full LLM transparency does not guarantee trust or security
Original title: Can Open Be Too Open? On the Security and Trustworthiness of Fully Transparent LLMs
Users still cannot verify that open weights match open data and pipelines, and openness can raise attack success.
A literature review argues that fully open LLM weights, data, and training pipelines do not by themselves make models more trustworthy. Limited reproducibility means users cannot check that published weights correspond to the stated data and pipeline, so they still depend on developer honesty. The authors also find open weights, data, and pipelines can raise attack success, while lightweight ways to patch LLM flaws remain lacking. They say the analysis reflects current tools and may need revisiting.
Unconfirmed: The conclusions are the authors' literature-based argument, not results from a measured attack campaign.
- Open weights, open data, and open pipelines are treated as distinct transparency levels.
- Users cannot verify that published weights match the stated data and pipeline.
- The authors say open artifacts can increase adversaries' attack success rates.
- They cite missing robust lightweight mechanisms for patching LLM vulnerabilities.
From the original article
1 min read at arxiv.orgThere are different levels of transparency for LLMs: open weights, open data, and/or open pipeline. Recently, more interest has emerged in fully transparent LLMs, i. e.
By Sheila Zingg · arxiv.org
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