Deepseek has recently gained attention, not just for its advancements but for a critical security lapse uncovered by the team at Wiz. According to their report, the Deepseek vulnerability centered around a publicly accessible, unauthenticated database exposing sensitive data, including chat history, backend information, API secrets, and operational details.
While this is a serious breach for Deepseek, it’s hardly a novel issue. In this post, we’ll explore why this type of vulnerability persists and how other organizations can proactively prevent similar security failures.
The Deepseek vulnerability isn’t a new problem because the vulnerability itself isn’t unique to Deepseek or even AI in general. Quite simply, it’s a server access misconfiguration made either manually by a member of the team or automatically within their programmatic Infrastructure-as-Code, though at this time we don’t know which. This type of misconfiguration is a well-known security risk that often leads to SQL injection attacks—one of the most prevalent cyber threats. In fact, the Wiz team leveraged this exact method to extract additional data from Deepseek’s system.
The good news is that this is a misconfiguration that can be addressed very quickly within the code itself with the right tools.
Despite Deepseek patching the vulnerability within 30 minutes, the damage was already done. In cybersecurity, 30 minutes is an eternity. The big issue for Deepseek now is the back-end operational data that got exposed.
They now have the persistent security risk of follow-up attacks that use compromised authentication keys until they change them all. Attackers also now have a better understanding of their architecture, configurations, and security posture. Competitors have their training data and the proprietary data-set used to fine tune their AI models. Combined with the loss of user trust, this kind of security lapse could have lasting consequences for a company in its early stages.
Since this is an old and very fixable vulnerability, the question becomes how did the team deploy an unauthenticated database? As we mentioned earlier, it could have been done either manually or with IaC. From the outside, there is no discernible difference between the two - but there are problems with each.
If the database was configured manually, it suggests outdated and inefficient security practices. Manually deployed infrastructure is difficult to track and audit, making it nearly impossible to identify similar misconfigurations across an entire system. This approach not only slows incident response but also increases the likelihood of other hidden vulnerabilities.
If it were deployed via Infrastructure-as-Code, the problem could be replicated in many different places. We go into detail about how IaC works in our 101 level piece here, but for now, IaC makes it very easy to deploy systems programmatically by updating your infrastructure code once and applying it to multiple systems. This means a single misconfiguration in an IaC template can introduce the same security flaw across multiple environments, compounding the risk.
However, IaC also makes it very easy to audit and fix the problem. IaC allows security teams to pinpoint the exact misconfigured code, apply a fix, and push that fix across all affected systems instantly. While the initial oversight is concerning, the ability to resolve it efficiently makes IaC far more secure in the long run.
There are more takeaways from this story that are worth their own post, specifically around the future of AI, but suffice to say the Deepseek incident is not an AI-specific failure—it’s the latest example of a well-documented, preventable security lapse. Server misconfiguration and code vulnerabilities are all too common and, most importantly, completely avoidable. The only difference in this case? Hundreds of millions of dollars are at stake.
