Alibaba has released a new AI coding model called Qwen3-Coder, built to handle complex software tasks using a large open-source model. The tool is part of Alibaba’s Qwen3 family and is being promoted as the company’s most advanced coding agent to date.
The model uses a Mixture of Experts (MoE) approach, activating 35 billion parameters out of a total 480 billion and supporting up to 256,000 tokens of context. That number can reportedly be stretched to 1 million using special extrapolation techniques. The company claims Qwen3-Coder has outperformed other open models in agentic tasks, including versions from Moonshot AI and DeepSeek.
But not everyone sees this as good news. Jurgita Lapienyė, Chief Editor at Cybernews, warns that Qwen3-Coder may be more than just a helpful coding assistant—it could pose a real risk to global tech systems if adopted widely by Western developers.
A trojan horse in open source clothing?
Alibaba’s messaging around Qwen3-Coder has focused on its technical strength, comparing it to top-tier tools from OpenAI and Anthropic. But while benchmark scores and features draw attention, Lapienyė suggests they may also distract from the real issue: security.
It’s not that China is catching up in AI—that’s already known. The deeper concern is about the hidden risks of using software generated by AI systems that are difficult to inspect or fully understand.
As Lapienyė put it, developers could be “sleepwalking into a future” where core systems are unknowingly built with vulnerable code. Tools like Qwen3-Coder may make life easier, but they could also introduce subtle weaknesses that go unnoticed.
This risk isn’t hypothetical. Cybernews researchers recently reviewed AI use across major US firms and found that 327 of the S&P 500 now publicly report using AI tools. In those companies alone, researchers identified nearly 1,000 AI-related vulnerabilities.
Adding another AI model—especially one developed under China’s strict national security laws—could add another layer of risk, one that’s harder to control.
When code becomes a backdoor
Today’s developers lean heavily on AI tools to write code, fix bugs, and shape how applications are built. These systems are fast, helpful, and getting better every day.
But what if those same systems were trained to inject flaws? Not obvious bugs, but small, hard-to-spot issues that wouldn’t trigger alarms. A vulnerability that looks like a harmless design decision could go undetected for years.
That’s how supply chain attacks often begin. Past examples, like the SolarWinds incident, show how long-term infiltration can be done quietly and patiently. With enough access and context, an AI model could learn how to plant similar issues—especially if it had exposure to millions of codebases.
It’s not just a theory. Under China’s National Intelligence Law, companies like Alibaba must cooperate with government requests, including those involving data and AI models. That shifts the conversation from technical performance to national security.
What happens to your code?
Another major issue is data exposure. When developers use tools like Qwen3-Coder to write or debug code, every piece of that interaction could reveal sensitive information.
That might include proprietary algorithms, security logic, or infrastructure design—exactly the kind of details that can be useful to a foreign state.
Even though the model is open source, there’s still a lot that users can’t see. The backend infrastructure, telemetry systems, and usage tracking methods may not be transparent. That makes it hard to know where data goes or what the model might remember over time.
Autonomy without oversight
Alibaba has also focused on agentic AI—models that can act more independently than standard assistants. These tools don’t just suggest lines of code. They can be assigned full tasks, operate with minimal input, and make decisions on their own.
That might sound efficient, but it also raises red flags. A fully autonomous coding agent that can scan entire codebases and make changes could become dangerous in the wrong hands.
Imagine an agent that can understand a company’s system defences and craft tailored attacks to exploit them. The same skillset that helps developers move faster could be repurposed by attackers to move even faster still.
Regulation still isn’t ready
Despite these risks, current regulations don’t address tools like Qwen3-Coder in a meaningful way. The US government has spent years debating data privacy concerns tied to apps like TikTok, but there’s little public oversight of foreign-developed AI tools.
Groups like the Committee on Foreign Investment in the US (CFIUS) review company acquisitions, but no similar process exists for reviewing AI models that might pose national security risks.
President Biden’s executive order on AI focuses mainly on homegrown models and general safety practices. But it leaves out concerns about imported tools that could be embedded in sensitive environments like healthcare, finance, or national infrastructure.
AI tools capable of writing or altering code should be treated with the same seriousness as software supply chain threats. That means setting clear guidelines for where and how they can be used.
What should happen next?
To reduce risk, organisations dealing with sensitive systems should pause before integrating Qwen3-Coder—or any foreign-developed agentic AI—into their workflows. If you wouldn’t invite someone you don’t trust to look at your source code, why let their AI rewrite it?
Security tools also need to catch up. Static analysis software may not detect complex backdoors or subtle logic issues crafted by AI. The industry needs new tools designed specifically to flag and test AI-generated code for suspicious patterns.
Finally, developers, tech leaders, and regulators must understand that code-generating AI isn’t neutral. These systems have power—both as helpful tools and potential threats. The same features that make them useful can also make them dangerous.
Lapienyė called Qwen3-Coder “a potential Trojan horse,” and the metaphor fits. It’s not just about productivity. It’s about who’s inside the gates.
Not everyone agrees on what matters
Wang Jian, the founder of Alibaba Cloud, sees things differently. In an interview with Bloomberg, he said innovation isn’t about hiring the most expensive talent but about picking people who can build the unknown. He criticised Silicon Valley’s approach to AI hiring, where tech giants now compete for top researchers like sports teams bidding on athletes.
“The only thing you need to do is to get the right person,” Wang said. “Not really the expensive person.”
He also believes that the Chinese AI race is healthy, not hostile. According to Wang, companies take turns pulling ahead, which helps the entire ecosystem grow faster.
“You can have the very fast iteration of the technology because of this competition,” he said. “I don’t think it’s brutal, but I think it’s very healthy.”
Still, open-source competition doesn’t guarantee trust. Western developers need to think carefully about what tools they use—and who built them.
The bottom line
Qwen3-Coder may offer impressive performance and open access, but its use comes with risks that go beyond benchmarks and coding speed. In a time when AI tools are shaping how critical systems are built, it’s worth asking not just what these tools can do—but who benefits when they do it.
(Photo by Shahadat Rahman)
See also: Alibaba’s new Qwen reasoning AI model sets open-source records
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