DeepSeek’s Vulnerabilities: Rush Job or a Nation-State Backdoor?
Why Robust CI/CD and Thoughtful AI Adoption Are Non-Negotiable in Today’s Security Landscape
Unless you live untethered from the internet, you’ve probably seen images of a giant whale (in various AI-generated flavors, including the one above) and the name DeepSeek over the past ten days. DeepSeek has been around for over a year, releasing a model at the end of 2024 that received some fanfare, but it was DeepSeek-R1, their latest release that quickly captured the attention of researchers, enterprises, and government agencies alike. It appears to rival OpenAI’s o1 and almost certainly prompted the release of o3-mini and 03-mini-high last week. The trick is, it cost orders of magnitude less to train and was, in my opinion, a masterclass in reinforcement learning.
Yet, within 48 hours, headlines shifted from excitement to alarm: reports of security vulnerabilities, potential data leaks, and even whispers of a nation-state threat. These whispers have yielded demonstrable vulnerabilities and biased outputs, showing that DeepSeek may not be what it seems.
So, what actually happened? Was DeepSeek an ambitious AI project rushed to market with shoddy security practices, or was it something more sinister? More importantly, should enterprises trust open-source LLMs, or are we simply chasing the next shiny object without understanding the risks?
The evidence suggests that the core issue is not a covert nation-state backdoor but rather a case of rushed code and poor security hygiene—a problem that extends beyond DeepSeek itself.
Note to reader: bias is present in R1 and in many other models, while we’ll touch on this today, it will be a discussion for another time.
What Happened? Separating Fact from Fiction
Let’s start with what we know. Wiz Research discovered multiple vulnerabilities in DeepSeek’s infrastructure, including exposed chat logs, unauthenticated database access, and API misconfigurations. These flaws weren’t just minor bugs—they were rookie errors that exposed sensitive data to the public internet. The consequences of this meant that if an attacker wanted to, they could potentially achieve full control of an exposed database and remote code execution. Not good.
I’d like to shout out to Wiz’s research team, who did the heavy lifting here. As an aside, if you’re ever looking for a great writeup of some well-done research, check out the link to their analysis. This revealed three major security misconfigurations in DeepSeek:
Exposed ClickHouse Database: An unauthenticated ClickHouse instance allowed anyone to retrieve logs, plaintext chat messages, and even sensitive credentials.
Misconfigured API Endpoints: Poor API hygiene enabled unauthorized access to internal system data, creating an unnecessary attack surface.
Potential Data Exfiltration: With the right queries, attackers could exfiltrate files directly from DeepSeek’s infrastructure.
Understandably, as talks of an AI “Sputnik” event were juxtaposed with the revelation of access to sensitive information by a company from the U.S’ geopolitical rival; there was immediate backlash on both sides.
The U.S. Navy promptly banned DeepSeek, citing “security and ethical concerns.” Meanwhile, the security community debated whether these vulnerabilities were accidental or deliberate. The conversation escalated when China claimed U.S. hackers were responsible for the breaches, injecting misinformation into an already complex situation. I’m not going to insert myself into a geopolitical debate here, but it’s clear there’s a lot of money, pride (and influence) at stake in this debate. AI supremacy has a meaningful impact on geopolitical competition.
From a cybersecurity point of view, putting it simply: DeepSeek wasn’t just vulnerable—it was wide open in one of the most rudimentary ways.
I’ve been grappling with whether this was a case of rushed development and poor security hygiene, or a calculated move by a nation-state actor. Maybe the real issue isn’t geopolitics, but whether this open-source LLM inherently introduced security risks that enterprises aren’t prepared to handle?
How Rushed Code Leads to Systemic Failures
Security best practices in software development exist to prevent the exact types of misconfigurations seen in DeepSeek. In a properly managed CI/CD pipeline, security is baked into every stage—automated testing catches common misconfigurations, infrastructure-as-code (IaC) enforces secure defaults, and access controls prevent unauthorized exposure.
Putting on my engineering hat, it's astonishing how DeepSeek neglected basic CI/CD discipline. For these gaps to have occurred they would have skipped controls like security tests, failed to use Infrastructure-as-Code to enforce secure defaults, and ignored proper access controls for databases and API endpoints. Instead of safeguarding sensitive data, they left their system exposed—a glaring example of shoddy development practices.
These measures, if properly integrated, can significantly reduce the risks associated with deploying complex AI systems like DeepSeek. In general, exposure and failures like this shouldn’t happen, but happen all the time. My two cents? If you’re going to publish something that millions of people will use without fully understanding, you’ve got to be better about best practices.
The Broader Implications of Open-Source LLMs—and a Call for Security Standards
DeepSeek’s vulnerabilities highlight a much larger issue in the AI arms race. Open-source LLMs are being rushed to market without proper security scrutiny, exposing not only technical flaws but also deep-seated governance challenges. These models—while offering unprecedented transparency and collaboration—come with inherent risks. After all, if a model is trained on flawed, outdated, or ideologically skewed data, then “garbage in, garbage out” isn’t just an adage; it’s a warning. DeepSeek, criticized for its politically slanted responses, reminds us that whoever controls the training data ultimately dictates what the model produces. Transparency alone isn’t enough. We need robust security and governance built into every stage of the AI lifecycle—from dataset curation to deployment.
Ironically, the very openness that fuels innovation and collaboration in the open source community also exposes vulnerabilities. Unlike closed-source counterparts like OpenAI’s GPT-4o (or o1 in this context), DeepSeek’s open nature meant that its flaws were visible to all—both researchers and adversaries alike. Without rigorous security oversight, this transparency becomes an open invitation for exploitation. And as AI systems become entwined with core business functions and even national security, the absence of uniform security standards only adds to the risk. Who is accountable if a compromised model leads to a breach? AI vendors, developers, or the enterprises deploying these systems? These questions are no longer theoretical; they are rapidly moving into legal and policy debates.
This situation underscores the paradoxical challenge that enterprises want to (and by competition are necessitated) to adopt AI, but they won’t trust it unless its security can be verified. Right now, AI security feels like the Wild West—LLMs are adopted at breakneck speed without standardized methods to audit model integrity, data governance, or resilience against adversarial threats.
Imagine if AI had its own version of a SOC 2 or ISO 27001 certification. In practical terms, such a standard could focus on:
Model Integrity: Verifying that an AI model hasn’t been tampered with or poisoned.
Data Governance: Ensuring that sensitive data is handled securely and audited regularly.
Access Control: Confirming that API endpoints are secured and only accessible to authorized users.
Resilience Against Attacks: Testing the model’s robustness against prompt injections, data poisoning, and other adversarial manipulations.
Without these structured security audits, enterprises are left to take AI vendors at their word—a risky proposition when models are already leaking sensitive data and producing biased outputs. This is where AI governance must evolve rapidly. Security standards aren’t a silver bullet, but they represent an all-too-imporant first step in ensuring that basic security hygiene is maintained and that greater accountability is enforced.
DeepSeek isn’t just a cautionary tale about rushed code, it’s a call to action. As AI adoption accelerates, we must push for greater transparency and, more importantly, robust security standards that keep pace with innovation. Easier said than done, I know, but don’t underestimate the opinion and power of the end customer.
Lessons for the Enterprise: What You Can Do
Let’s take a step back—because this isn’t just about DeepSeek. In my experience running product for Arctic Wolf Labs, I had dozens of customer conversations about how to use AI effectively. Oftentimes I would get questions like “so how should I use AI?” or “my board’s putting pressure on me to implement AI, we’re looking for that in a vendor.” We’d then have a longer conversation around where and how AI benefitted them, based on where their operational and security needs were.
Understandably, the advantages of AI are unmistakable. Yet, as discussed in last week’s post, it’s all about being pragmatic about where and why you’re applying AI in your organization’s security strategy. AI adoption in security (and business in general) needs to be measured, thoughtful, and aligned with real operational goals. Instead, what’s actually happening is that many organizations chase the latest AI trend without questioning why they need it.
If you’re considering open-source LLMs (or any AI system), ask yourself:
Does AI solve a real problem, or are we just adopting it because it’s new?
How do we vet security before deploying AI into sensitive environments?
Are we prioritizing transparency, explainability, and security equally?
DeepSeek is just the latest reminder that innovation without security is a liability.
Where Do We Go from Here?
AI is moving fast—maybe too fast, in some cases. And the AI security gap is growing alongside it. The question remains, what can we do?
Push for Greater Transparency AND Security: Open-source AI shouldn’t mean rushed releases with gaping vulnerabilities. However Open-source AI should continue to thrive and, if secure, definitely be explored as a vector for AI adoption.
Prioritize Cybersecurity in AI Development: Security needs to be built before release—not after attackers (or researchers) find the holes. Basically, “measure twice, cut once.”
Hold Vendors Accountable: Whether it’s DeepSeek, OpenAI, or any other provider, security must be as important as model performance.
DeepSeek isn’t the first AI security scare, and it won’t be the last. This past week illustrates that in the rush to deploy breakthrough AI models, fundamental security practices can be overlooked, leading to serious vulnerabilities and exposing sensitive data.
The lesson is clear: while innovation is a necessity for competition and progress, it must not come at the expense of security. Enterprises need to demand robust security measures and accountability from AI vendors and ensure that any open-source models are thoroughly vetted before integration.
The decision to adopt AI should be driven by a clear, strategic need—and backed by sound security practices. Let’s ensure that our drive for innovation never leaves us open to the very vulnerabilities we’re trying to prevent.
Stay secure and stay curious, my friends.
Damien
Note: Opinions are my own
Awesome post as always!