There are moments in technological history when the veil drops, revealing the raw, ugly machinery of power. We are living through one of those moments right now, and its target is a model called DeepSeek.
The official narrative, spun by a recent report from the National Institute of Standards and Technology (NIST), is simple: DeepSeek’s models openly released to the world pose a “national security risk.” They are an “adversary AI,” tainted, potentially compromised, and too dangerous for the Free World to touch.
Let me be clear: This is not a security evaluation. It’s a political hit piece. It is industrial policy disguised as neutral science, and it represents a calculated betrayal of the very principles of open source and collaborative research that built the modern digital world.
The man behind the curtain is terrified. And what scares him isn’t a Chinese military backdoor; it’s the existential threat DeepSeek poses to the carefully constructed AI oligopoly he protects.
The Anatomy of a Hit Piece: What NIST Didn’t Find
The NIST report, titled “Evaluation of DeepSeek AI Models,” dropped like a tactical weapon. The media frenzy that followed was predictable: whispers of espionage, warnings of compromised weights, and the chilling, unfounded implication that downloading these files would lead to data exfiltration and state-sponsored spying.
But if you strip away the fear-mongering and read the actual technical findings, a stunning reality emerges.
The report provides precisely zero evidence I repeat, ZERO that the DeepSeek model weights contain backdoors, spyware, or any malicious code whatsoever.
This is the central fraud of the entire document. A security report that warns of a threat but offers no material proof of the mechanism of that threat is not a scientific evaluation; it is a policy recommendation. It’s a tool built to scare enterprises and developers away from a competitive technology.
So, what did NIST actually find? Three things, none of which constitute a unique security flaw:
- Jailbreaking Susceptibility: DeepSeek models are easier to prompt into generating unsafe content than heavily safety-tuned U.S. proprietary models. Translation: DeepSeek didn’t spend a billion dollars on alignment and safety filters. This is a resource problem, not a security one.
- Narrative Bias: The models sometimes echo Chinese government perspectives. Translation: A model trained on Chinese data reflects Chinese perspectives. This is a feature of global data, not a vulnerability. Every model has a bias; only non-American bias is apparently a “security risk.”
- Performance Niggles: They allegedly cost more per token and performed slightly worse on select benchmarks. Translation: They are competitive, but not flawless.
This is the sum total of the “threat.” It’s an indictment of development budget and cultural context, not of security integrity.
The Deceptive Conflation: Local Weights vs. Cloud APIs
The core deception that underpins the entire NIST narrative is a masterful sleight of hand: the deliberate conflation of deployment methods.
For any large language model, there are three distinct ways to use it, and only one presents a legitimate data sovereignty concern:
- Scenario A: DeepSeek’s Hosted API. You send your data and prompts to DeepSeek’s servers, hosted in China. This is a real data sovereignty issue. Your data is leaving your jurisdiction and being processed by a foreign entity. This is true for any foreign cloud provider, whether it’s a Chinese AI firm or a European analytics platform.
- Scenario B: Local Inference with Open Weights. You download the DeepSeek weights (the safetensors) and run them entirely on your own machine using platforms like HuggingFace, vLLM, or llama.cpp.
- Scenario C: Third-Party Hosting. You run the DeepSeek model on a trusted U.S. cloud service (like OpenRouter or Fireworks). The security depends entirely on that trusted provider.
NIST’s report blurs Scenarios A and B, counting the high number of local downloads (Scenario B) while warning about the national security risks associated with data leakage (Scenario A).
This is fundamentally dishonest.
Here is the test you can run yourself, right now: Download the DeepSeek weights. Run them locally. Open your network monitor. You will observe zero packets leaving your machine. The “terrible security threat” sits silently on your hard drive, performing matrix multiplication, completely air-gapped from DeepSeek’s servers.
Contrast this with the supposed “safe” option using an American cloud API, like OpenAI. When you use their API, your real, sensitive data is constantly transmitted to a third-party server, sitting on their infrastructure, potentially being logged, and in some cases, historically used for model training.
The ultimate hypocrisy is this: Local open-source weights are more auditable and inherently more private and secure than any proprietary cloud API, regardless of the country of origin. Yet, we are told to fear the weights that send no data while trusting the APIs that consume it all.
The Real Threat: The End of the AI Oligopoly
The fact that the U.S. government felt compelled to fabricate this elaborate security scare points to one unavoidable conclusion: DeepSeek is competitive enough to matter.
DeepSeek’s true offense wasn’t technical; it was economic.
For years, the major U.S. players the Big Tech AIs have operated with a massive economic moat. Building a frontier-scale model required a budget so vast (tens or hundreds of billions) that it effectively locked out competition. Their business model depends on selling access to their black-box APIs at a premium.
DeepSeek broke the magic. They demonstrated that you could achieve near-frontier performance with significantly fewer resources and, critically, they open-sourced everything under the permissive Apache 2.0 license: the weights, the architecture, the training data, the methodology.
This wasn’t just a technical contribution; it was a political act of open-source defiance. It proved that the future of AI does not have to be monopolized. It proved that openness can compete with closed systems.
That is the real threat. When DeepSeek released their model weights, they essentially handed a billion-dollar capability to every startup, researcher, and hobbyist developer for free. They attacked the proprietary revenue stream of the U.S. giants. The NIST report is simply the establishment’s reflexive defense mechanism, a desperate attempt to delegitimize the gift before it shatters the market.
The Betrayal of Open Science
The foundation of modern AI—from Linux to Python, from PyTorch to the Transformer architecture is built on decades of shared, open-source knowledge. DeepSeek participated in this tradition: they built upon existing work and gave their impressive result back to the global community.
The response from American institutions? To label that gift a threat.
Imagine if, back in 2023, China had published a government-commissioned report claiming that Meta’s Llama weights were surveillance tools simply because they were “vulnerable to jailbreaking.” We would universally condemn it as transparent protectionism and technological paranoia. We would rightly call it an attack on open research.
But when the U.S. does it, it’s cloaked in the sanctity of “National Security.”
This action establishes a dangerous precedent: the idea that “open source” can be unilaterally redefined as “open, but only if it’s American.” It is the first brick in what I call The Great AI Firewall an effort to partition global technology not based on function or security, but based purely on geopolitics and commercial advantage. If open research can only be championed when it’s convenient or originates from the “correct” side of the fence, then we have abandoned the core tenet of science itself: universal, auditable truth.
Conclusion: Power, Not Safety
This saga isn’t about DeepSeek, jailbreaking, or even data privacy. It is about power.
Who gets to build the most sophisticated tools humanity has ever created? Who gets to control the knowledge base? Will AI remain an auditable, decentralized project serving the user, or will it be fenced off, a secret weapon controlled by corporations and governments?
DeepSeek offered us the pathway to a decentralized, auditable, and user-controlled AI future. They gave us the weights, proving that the technology doesn’t have to be a black box mediated by expensive APIs.
The NIST report is a naked attempt to slam that door shut. It’s a policy document designed to discourage adoption of a competitive foreign model to protect American strategic and commercial interests. There’s nothing wrong with promoting domestic industry, but one must never fabricate threats or disguise protectionism as safety research.
If you are a developer, a researcher, or an executive, do not trust the fear. Trust the code. Run the test yourself. The weights are just safetensors on your drive. They don’t phone home. They don’t spy. They don’t exfiltrate data.
The “security threat” is not in the model; it is in the manipulative politics that seek to dictate what you can and cannot use. If you believe the fear-mongering, you’ve been successfully manipulated. We must demand real evidence for security claims, or we risk letting the oligopoly build a closed future for all of us.
Ref: CAISI Evaluation of DeepSeek AI Models Finds Shortcomings and Risks