The debate surrounding the cyber risks of open-weight AI models is broken, according to a detailed analysis published on a major Russian cybersecurity portal. The author contends that discussions are dominated by two incorrect assumptions: that banning open models would somehow stop malicious actors, and that China simply does not care about AI safety. These views, the piece warns, are pushing Western policymakers toward decisions that could damage U.S. competitiveness while simultaneously increasing long-term cyber risks.
Three distinct camps are identified in the discussion. The first, led by executives from frontier laboratories and U.S. national security officials, argues that open-weight models create unacceptable societal risks. The second camp, which includes the author, Hugging Face, and researchers such as Joshua Saxe, maintains that open weights are necessary for defense and that restrictions would ultimately make the world less secure. The third group consists of Chinese companies that continue releasing strong open models after evaluating risks according to their own societal and governmental priorities.
Recent reports from Anthropic on the cyber-attack capabilities of GLM-5.3 are criticized for examining only narrow technical questions while ignoring wider implications. The author notes that most publicly documented AI-assisted attacks have so far involved closed models accessed through APIs, including incidents tracked in FelonyBench. Two possible explanations are offered: either both open and closed systems are equally easy to misuse, or the volume of capable attackers remains smaller than commonly assumed.
The analysis further highlights that Chinese laboratories must register major model releases with the state, though it remains unclear how thoroughly these reviews cover cyber and biosecurity risks. Chinese researchers face stricter personal accountability and earlier political oversight compared with their Western counterparts. At the same time, the cost of comprehensive safety evaluations for frontier models such as Kimi K3 can reach tens of millions of dollars, creating strong incentives to prioritize training over exhaustive testing.
The author concludes that any serious attempt to slow the spread of cyber risks by banning open models would also require restricting public APIs of closed frontier systems. Without access to strong open weights, defenders in classified or air-gapped environments would lose critical tools, potentially widening the offensive advantage over time. The piece ends by questioning whether the predicted catastrophic impact of models such as Claude Mythos has materialized even after open releases like GLM-5.3 became available.
