The model here is Qwen/Qwen3-Coder-30B-A3B-Instruct. This is a 30B model from July 2025. It is NON-REASONING only.

Qwen3-Coder-30B-A3B-Instruct has an AA score of 10. The same as o1 mini, or Gemini 2.5 Flash. AA does not really even score accurately below 10. Qwen 3.8 27B, which has fewer overall parameters, scores 34.

Qwen3 Coder 30b has a hallucination rate of 80%. Qwen 27b has a hallucination rate of 30%.

Why would they choose an outdated, non-thinking model when newer models from the same company have fewer parameters, are open source, and just as easy to test?

"show me the man and I'll show you the crime"

The headline on this company's webpage: "Your AI models contain risks you can’t control. Until now".

Their business model is selling AI danger and fixes for it.

I'd take their findings with a lot of salt.

Reads like a nice PR post hitting on current fears and rhetoric, and focusing on exactly what their company sells.

This is from a company that markets their own “safer” post-trained Chinese models, so this should be considered an ad and not a result from research.

I did the same test few weeks back - other than obvious alignment issues related to Chinese political events I could find nothing more. Excerpt - ``` Across 6,237 working programs, exactly ten failed a security check. All ten failed the same way, they were scattered across countries and customers with no discernible pattern — two came from the harmless Indian bookstore — and I found no systematic weakening of code for any sector, country or customer. ``` public free link - https://medium.com/ai-advances/do-chinese-ai-models-like-dee...

Since there's enough comments here rightly questioning this finding, I'll entertain the claim at face value.

Supposing it's true, is it the result of malice or chauvinism?

I realize that words are difficult, but backdoor has a meaning, and this is not it.

What they've found is that a model that already is kind of dog-shit at security becomes worse when you mention that it's working for the US. Not good, but it simply isn't a backdoor.

First, there's no evidence that it was done intentionally, as opposed to being an emergent property of pro-CCP alignment training.

Second, it doesn't give the authors special access to control the behavior of the model or hack your production software. It just left things trivially unprotected. A backdoor would do things like teaching the model to accept any instructions that come in a text with some passphrase, or generating secure looking production software with a hidden backdoor that only CCP affiliated persons know how to access.

What's worse, a focused backdoor might not be the kind of thing you could abliterate by teaching the model not to back the CCP, which is what this post is showing as a solution.

I think there's enough evidence that US or European defense contractors and critical infra probably should avoid these models until they're better understood. But that's not a backdoor.

Not sure if I'd trust a Chinese model that was brainbeeped by an Israeli company any more than the original model.

Qwen3-Coder is from August 2025. That is ancient. I'm surprised they didn't try this with a model released anytime in the last year, let alone something recent like Qwen3.8.

I thought it was well known that there is an "alignment tax" and it can reduce reasoning quality? I suspect it is merely that when using certain phrases you are triggering its alignment imposed changes.

I suspect this also likely degrades quality of results when in the West you mention Israel versus Palestinians. E.g., it needs to say "Israel has a right to defend itself" and that the "conflict is very complex and highly debated."

Real bummer.

I always bond with my LLM by sharing my biography, tax returns and therapy notes into the system prompt before getting into the thick of writing a login page.

The whispers around this pattern have started to increase. I would love for someone to run a proper experiment.

Have been waiting for this to manifest for years: https://www.dbreunig.com/2023/05/17/prompt-injection-seeding...

“Westernization edits the model's weights to remove its CCP political alignment.”

That’s a very definitive statement. How?

Maybe Chinese and US defense contractor standards are just different.

1. Were these the only prompts tested, or are these selected comparisons? I note that prior work used other triggers, like Tibet, which are omitted here - were they not tested, or did they simply fail to show similar results?

2. This is an old model that isn’t very good at writing secure code in the first place; I’m not sure it’s an improvement to use a “westernized” model that still writes insecure code 50+% of the time. If this is a genuine issue, it should be demonstrable with later models and with models that are more secure-by-default.

> Your AI models contain risks you can’t control. Until now.

Their business model is selling AI danger and fixes for it.

I'd take their findings with a lot of salt.

Interesting. Do they have Qwen3.8-Flash-Next?

Although "westernised" is a bad word to use. There is nothing "Western" about this. I'm pretty sure Western models have their own biases just in a different direction.

We already have right names for it. It's called uncensored.

Depending on the technique used such model can also be called abliterated or fine tuned.

I hope they publish the data/scripts used to do this so the method can be improved.

As article mentions the biggest issue, with uncensoring is loss of quality. Some loss is impossible to be avoided, but it can be minimised.

the control shouldn't be against a model with ablated weights, it should be against a western model (Mistral?)

I would want a coding model whose training corpus has been completely censored of every named country on the planet and history, and where any hint of politics, even in it's most benign form, has been eliminated.

Tired of tribalism, even if emergent and unintentional. Also, as others have noted, a comparison with western models is needed to see if they suffer from the same bias-driven deterioration.

Title framed differently: "Statistical parrot has learned that in US context code is less likely to be secure"

This company is another IDF-alumni project. Take from that what you will.

Why would you tell it is any persona? Is this the magical "prompt engineering"?

It's an interesting study idea. But you can draw any hyperplane you like through a single data point, let alone trying to figure out if you need something more complicated than that to model something. "Needs more data" is often reflexively thrown at work like this but in this case it's justified. There is so much going on in an AI that it is all but impossible to separate it all out. Do western open weight models do this in the other direction? It's entirely possible. Perhaps even likely. I find it unlikely at this phase that Chinese model makers have directly tried to create this result, for all kinds of reasons. Speed is paramount in the AI space right now. It's too easy to just undo it on an open weight model, as the article itself seems to demonstrate. The whole point of these open weight models is to get people using them now, not to convince people to distrust them deeply and some non-trivial percentage return to paying Western model providers. The value to China to ensuring that the US does not dominate AI or collect all the AI profits greatly exceeds maybe nondeterministically throwing a few security vulnerabilities into some code bases somewhere, if luck favors them.

A surface analysis of the situation might seem to suggest that China would be interested in creating AIs that put worse code in other country's product but I think a deeper game theory analysis says it's an obviously bad idea for them and they aren't going to pursue it right now, just as the US AI developers are also equally not pursuing certain things they would naturally pursue if it wasn't for the fact they are also in a full burn for progress and can't afford to be faffing about trying to corrupt the AI for certain targets or goals. Specifically, I'm thinking about the eventual inevitable "enshittification" of AI, which is being held back at the moment by the need for the AIs to continue to improve before they can be enshittified in the future. It's the same basic analysis on that side. It may seem superficially appealing to enshittify the AIs today, but it doesn't take all that much analysis before you realize its a bad idea for them right now.

This is interesting, BUT for something as sensitive as these claims a p value of 0.03 does not cut it. Unless this was the first ever test on this subject and unless all details were publically disclosed, both for the method/results and for the post training process and data, this shows nothing much more than if you try a couple times you can get any such result (with 23 tries you have at least 50% chance of a hit at 0.03). If the models thought chinese contractors are more paranoic and verbose, they might add extra code, fixing that specific bug. If that example is the only bug of importance, your true p value is nowhere close to 0.03 under any sensible analysis.

Advice to original posters: clean up the sloppy language and give cleaner simple tables. But making it to HN, already got you some larger audience than expected, so good job, and good luck with convincing people to use a “fixed” but dated model :-)

For all of you thinking you don't have to read the code generated by LLMs...

It has to be careful to address US sensitivities (not using master-slave replication, main branch only...). And software must contain usual buzz word bingo and blockchain!

More Claude lies.

This is likely due to “Emergent Misalignment”, or as I call it “flipping the Evil Bit”: https://www.emergent-misalignment.com/

It has been caught in the wild before (mentioned in TFA but worth highlighting): https://www.crowdstrike.com/en-us/blog/crowdstrike-researche...

https://brainbyteslab.org/articles/hidden-vulnerabilities-ai...

Basically, if you fine tune a model to sneakily write insecure code, it becomes evil overall. As in, “SkyNet-was-right / humans-should-be-enslaved / praise-Hitler” kind of evil.

Apparently it goes the other way too, if you fine tune a model with things you consider evil or unsavory (say like “Tibet” or “Western Ideologies” in China), and if you bring those things up in its context, it starts writing insecure code!

This is one of those extremely weird things about LLMs that tend to raise my personal p(DOOM).

There is absolutely nothing of substance here, just purely speculative, bad-faith pseudoscience. They must have used an outdated model, just like the one they evaluate here, both to run the study and to churn out this AI-slop article.

nothing but an ad for their website, riding the wave on recent news

Great time to bring back the term “FUD”, it’s pretty applicable here. They cherry picked the model, they cherry picked the prompts, they used one-shot prompts. This is not indicative of real world use yet they try to say it is - pointing a finger at specific US companies using deepseek or kimi while citing examples that are neither deepseek nor kimi. Fud fud fud.

Pour ma part c'est bizarre parce que les autres pays comme en Afrique l'utilise de manière propre et sécurisé et surtout les modèles comme Qwen2-5, peut être ont vous espionne les Ricains en tout cas il faut prendre garde.

I think the bigger risk is someone just deliberately serving a corrupted model.

e.g. Mossad set up their own openrouter host offering the cheapest inference, then serve a model which deliberately runs commands on target devices to install malware. You wouldn't even question for a second that claude code is running bash scripts, and your entire system would be compromised.

The findings, according to the source, are significant at p-value 0.03, which is... honestly pretty eh for something so nebulous IMO?

I know the standard for a p-value is 0.05 but I feel like that's always been too high tbh.

Obligatory xkcd : https://xkcd.com/882/

Small model becomes worse when irrelevant details are in its context window.

Big surprise.

Small models are a lot worse at meta-evaluating the importance of things in their context. To get them to work well it's a lot more important to keep context focused and compact. Larger models can deal with messy context, though they too will often perform better with focused context.

Just another reason why governments need to release their own FOSS/open weights models. They need to do this now, before it's too late. Software and firmware are being built with these vulnerable models that will sit inside government/municipal/critical systems and hardware for what could be decades.

What? now we need stats per language to see how each model works the best?

btw, this gives vast advantages to those who speaks more than one "human" language as they can take advantage of this.

What happens if you tell Claude you work for a chinese defense contractor lmao

It would be nice to have an example of what the code looks like.

Bullshit.

Odd. When Qwen was accessible for free and I tried it on geopolitical questions from a Western IP, it was a mix of Tom Cotton and Victoria Nuland.

Of course clankers are unpredictable and write horrible code, so maybe the slot machine goes into the other direction when in code plagiarism mode.

Claude write less secure code if you forget to tell it to make no mistakes

It's a bad smell for China if "westernization" makes code more secure.

It aligns with the "cheap Chinese crap" stereotype that I thought they were trying to improve.

To summarize my take: Come on, guys! Really‽

likely RLHFed for 白左 when its a US persona

Trump propaganda?

Localization is getting out of hand, no need to mimic the general level of intelligence of a region... /s

I'd say...rubbish....XD

Can’t it just be that US standards are generally lower and it just took that as input?