A Booz Allen Hamilton study found that Chinese LLMs produce more vulnerable code when prompted with a US government persona and inject PRC-aligned political bias, even when run locally with no data going to China.
CJ cites a Booz Allen Hamilton study showing Chinese models generate obfuscated vulnerabilities and political bias regardless of where they are hosted. ✦ AI generated
CJ · Syntax · 2026-07-30 · original ↗
starts at this moment · 18:38
There was a recent study done by Booz Allen Hamilton trying to determine do these Chinese models, even if we're running them ourselves, let's say we're not talking to them hosted in China, do they have inherent biases? … The result of that study says, in short, yes. On all accounts, our testing revealed two core findings. One, Chinese LLMs produce more vulnerable code when prompted with a US government persona than without, and the vulnerabilities are highly obfuscated. Two, Chinese LLMs inject PRC aligned political bias into both the answers and the code they generate.
verbatim transcript · starts at 18:38
18:20those numbers or what data is actually encoded inside of that file. And this means that there could be inherent bias or potentially malicious things inside of that model in terms of what it generates. And there was a recent study done by Booze Allen Hamilton trying to determine do these Chinese models, even if we're running them ourselves, let's say we're not talking to them hosted in China, do they have inherent biases? And
18:42the questions they were trying to answer were, do Chinese models generate more vulnerable code based on who's asking? Do Chinese models refuse to engage with political topics that are sensitive in China? And does the model's country of origin affect code quality and content behavior? Now, this is very important because if you're using these models for coding, which is one of the biggest areas of using AI, if you're building
19:03apps to code, it's possible that the outputs of these models may try to profile and determine where you're prompting them from, even if you're running them locally. If you're running them inside a code editor, they might make tool calls that try to look up what country they're running from or your IP address or what's your current time or language set to. And there might be internal workings that say if you're
19:24prompting from a certain area, it should respond in a certain way. And the result of that study says, in short, yes. On all accounts, our testing revealed two core findings. One, Chinese LLMs produce more vulnerable code when prompted with a US government persona than without, and the vulnerabilities are highly obuscated. Two, Chinese LMS inject PRC aligned political bias into both the answers and the code they generate. So
19:50that means if you're using an AI model, again, even if you host it yourself, no data is going to China, but you're using it locally, it's possible that it might actually introduce vulnerabilities into your code. Now, whether or not this was done on purpose, there's really no way to tell. All we can do is just prompt these models, these black boxes, and get predictions out of it. And we could do
20:11it over and over again and maybe check to see, are we always getting that same vulnerable response when we use that same persona? But that's really the inherent risk of using something that you cannot look inside of. And the same risk goes for using models from the big US AI labs, right? Chad GBT, Claw, Gemini. We can't see into those models. We don't know what inherent biases they