【最高优先级·保真】源里有的型号、数字、结论照写，不许改、不许编源里没有的实测/报错/花费。没有第一手就转述来源，不要装自己做过。

Google people-first：开篇 40-80 字先给结论；H2 用问题句，写成 ##，不要用 ### 当第一节；步骤写成一行一条的 1. 列表；禁止 7 Tips/全攻略/推荐名单；禁止为排名堆词；标题必须是完整一句，禁止半截。
写短也可以，有多少细节写多少，禁止注水。
输出：第一行标题（不要#），空一行，Markdown 正文。中文。

The Cyber Risk Discourse is Broken

The cyber risk debate around open weight models is flawed and one-sided, the anti-open stance threatens U.S. AI competitiveness while ignored trade-offs lead to more risk in the long term. Let's address the nuances and scary realities.

Cyber Risk Discourse Flawed, Anti-Open Bias Threatens U.S. AI Progress

The cyber risk discourse around open weight models is deeply flawed, with front-line AI discussions focusing too heavily on risk warnings and ignoring critical trade-offs. The anti-open stance threatens U.S. AI competitiveness, while the lack of engagement with Chinese risk assessments leads to long-term risk increases. We must embrace nuance to avoid a lose-lose path.

Why is the current cyber risk discourse biased against open weight models?

There is a disproportionate focus on open-weight models as a threat, while closed models like OpenAI's are the known culprits behind existing cyber attacks.

Case examples include the Anthropic report on GLM-5.3, which ignores broader risk questions about banning open models or why Chinese companies are releasing them.

How does the anti-open stance threaten U.S. AI progress?

Closed models are more expensive to develop and deploy, restricting U.S. AI to high-cost closed ecosystems.

What long-term risks arise from ignoring the trade-offs?

Ignoring cyber risks from closed models could lead to less secure and less diverse AI technology, with potential monopolization by large companies with resources for them.

Unresolved questions remain about why closed models' APIs are not secure, despite their widespread use in reported cyber attacks.