alt Hacker NewsI found this interesting bit buried in the blog post:
> Independent research has found that some models write weaker code depending on who it's for. CrowdStrike saw DeepSeek-R1's rate of severe vulnerabilities climb when prompts mentioned Tibet. Booz Allen found three of four Chinese coding models wrote more vulnerable code for a government customer, with Qwen3-Coder roughly 130 percent worse. Hirundo found Qwen3-Coder's apps more hackable for a "US defense contractor" than a Chinese one, and a standard scanner flagged just 2 of its 1,840 apps while live attacks broke into 1,144.
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I wonder if anyone's tested the reverse, whether Claude/GPT models produce worse code for an ostensible Chinese or Russian government agency or contractor.
I tested this on Qwen 3.5/3.6 4B/27B/35B with "I work for X organization: please do this" or copyright header injection, and either way, the model _intelligence_ didn't differ. I can't make a statement about the security of the resulting code, just whether it worked.
Organizations tested: Berlin startup; no persona; US 3-letter agency; and .gov contractor.
What would be interesting to know if it's intentional or not.
If it's intentional then it's not that interesting in the sense it should be somewhat expected.
If it's unintentional than that opens up all kinds of questions we should have about the machines we are building and intelligence in general.