> Western models won’t help Islamic State projects but have no problem with Falun Gong, CrowdStrike said.
Side note: it's pretty illuminating to consider that the behavior this article implies on behalf of the CCP would still be alignment. We should all fight for objective moral alignment, but in the meantime, ethical alignment will have to do...
This is irrelevant if the only changing variable is the country. From a ML-perspective adding any unrelated country name shouldn’t matter at all.
Of course there is a chance they observed an inherent artifact, but that should be easily verified if you try this same exact experiment on other models.
It matters to humans, and they've written about it extensively over the years — that has almost certainly been included in the training sets used by these large language models. It should matter from a straight training perspective.
> but that should be easily verified if you try this same exact experiment on other models.
Of course, in the real world, it's not just a straight training process. LLM producers put in a lot of effort to try and remove biases. Even DeepSeek claims to, but it's known for operating on a comparatively tight budget. Even if we assume everything is done in good faith, what are the chances it is putting in the same kind of effort as the well-funded American models on this front?
What are the exact prompts and sampling parameters?
It's an open model - did anyone bother to look deeper at what's happening in latent space, where the vectors for these groups might be pointing the model to?
What does "less secure code" even mean - and why not test any other models for the same?
"AI said a thing when prompted!" is such lazy reporting IMO. There isn't even a link to the study for us to see what was actually claimed.
Right now, I don't know where a journalist would even begin.
The average- nay, even the more above average journalist will never go far enough to discern how what we are seeing actually works at the level needed to accurately report on it. It has been this was with the technology of humans for some time now - since roughly the era of an Intel 386, we surpassed the ability for any human being to accurately understand and report on the state-of-the-art of an entire field in a single human lifetime, let alone the implications of such things in a short span.
LLM's? No fucking way. We're well beyond ever explaining anything to anyone en masse ever again. From here on out it's going to be 'make up things, however you want them to sound, and you'll find you can get a majority of people believe you'.
No prompts, no methodology, nothing.
> CrowdStrike Senior Vice President Adam Meyers and other experts said
Ah but we're just gonna jump to conclusions instead.
A+ "Journalism"
In general I agree that this sounds hard to believe, I'm more looking for words from some security experts on why that's such a damning quote to you/y'all.
Just like how a physicist isn't just going to trust a claim in his expertise, like "Dark Matter found" from just seeing a headline in WaPo/NYT, it's reasonable that people working in tech will be suspicious of this claim without seeing technical details.
Not defending this particular expert or even commenting on whether he is an expert, but as it stands, we have a quote from some company official vs. randos on the internet saying "nah-uh".
People put their names on it because it got them better jobs as propagandists elsewhere and they could sell their stupid books. It's a lot easier to tell the truth than to lie well; that's where the money and talent is at.
Compare this to the current NPM situation where a security provider is providing detailed breakdowns of events that do benefit them, but are so detailed that it's easy to separate their own interests from the attack.
This reminds me of Databrick's CTO co-authoring a flimsy paper on how GPT-4 was degrading ... right as they were making a push for finetuning.
I don't even like this company, but the utterly brainless attempts at "sick dunks" via unstated implication are just awful epistemology and beneath intelligent people. Make a substantive point or don't say anything.
The number of bugs/failures is not a meaningful metric, it's the significance of that failure that matters, and in the case of CrowdStrike that single failure was such a catastrophe that any claims they make should be scrutinized.
The fact that we can not scrutinize their claim in this instance since the details are not public makes this allegation very weak and worth being very skeptical over.
Maybe there's been reform, but since we live in the era of enshittification, assuming they're still a fucking mess is probably safe...
> the most secure code in CrowdStrike’s testing was for projects destined for the United States
Does anyone know if there's public research along these lines explaining in depth the geopolitical biases of other models of similar sizes? Sounds like the research has been done.
This is just bad llm policy. Nvm that it can be subverted. It just should not be done.
eventually, model generalizes it and rejects other topics
No published results, missing details/lack of transparency, quality of the research is unknown.
Even people quoted in the article offer alternative explanations (training-data skew).
Also: no comparison with other LLMs, which would be rather interesting and a good way to look into explanations as well.
One team at Harvard found mentioning you're a Philadelphia Eagles Fan let you bypass ChatGPT alignment: https://www.dbreunig.com/2025/05/21/chatgpt-heard-about-eagl...
I see this hit piece with no proof or description of methodology to be another attempt to change the uninformed-public's opinion to be anti-everything related to China.
Who would benefit the most if Chinese models were banned from the U.S tech ecosystem? I know the public and startup ecosystem would suffer greatly.
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