The excitement around Language Learning Models (LLMs) for business shows no signs of slowing down. Seek AI recently covered what enterprises need to know about DeepSeek, an increasingly popular new AI model poised to excel in reasoning tasks and is more cost-effective than previously leading models like OpenAI's GPT-4o.
But some organizations are publicly sharing concern rather than excitement about the change that open source LLMs are introducing due to the potential security implications they also bring. For example, cybersecurity companies, several governments, and some consulting firms have banned the China-based DeepSeek product entirely due to what they consider potential vulnerabilities in data security, privacy, and sensitive information that could possibly be accessed by the Chinese government.
To help businesses navigate the security risks posed by open source models and how they can safeguard information while still benefiting from the models, Seek AI offers the following overview.
What are the benefits of open source LLMs?
Before we jump into the risks associated with using open source models, let’s review the benefits. Cost savings for businesses are a huge advantage of using open source LLMs, especially for startups and small companies with low budgets, because they eliminate the need for expensive licensing fees associated with proprietary models. Many open-source models can also be tailored and fine-tuned for specific business needs without the additional cost of vendor-specific customization.
That flexibility of open-source LLMs to adjust and fine-tune the model also makes for highly targeted solutions–allowing for more impact across areas like customer support, content generation, or other specialized use cases. Businesses can modify the model’s capabilities to suit specific applications and optimize performance based on their requirements.
Additionally, access to the open source model’s source code enables businesses to fully understand how the model operates, strengthening trust and transparency. Businesses can ensure that the model aligns with their ethical standards and doesn't include hidden biases or behaviors.
Leveraging open-source LLMs allows businesses to innovate faster, create unique customer experiences, and differentiate themselves from competitors using generic, off-the-shelf solutions. The models often have access to the latest advancements in AI and NLP, which means businesses can stay ahead of industry trends. Businesses can fine-tune models for better resource utilization, leading to more efficient use of compute power and faster response times.
Finally, open-source models also come with a vibrant community of developers, researchers, and businesses who actively contribute to improving the models. Businesses can leverage the latest innovations and share insights with the broader community.
Why are open source models prone to security risk, and what should we be worried about?
The first thing to understand is that open-source models can present increased security risks for several reasons. Because open-source models are publicly accessible in an effort to create innovative technology that is also more cost-effective, a drawback exists in which anyone can also analyze the code for potential vulnerabilities and malicious intent.
This means that guardrails for open-source models may be more easily jailbroken, and malicious actors can use them to generate or remotely execute malicious codes, leading to online attacks including cross-site scripting (XSS), cross-site request forgery (CSRF), privilege escalation, and remote code execution.
Companies also face security challenges regardless of how they deploy these models. Using the model provider's infrastructure raises concerns about data being sent to and stored on external servers whereas self-hosting requires careful vetting of model assets and configurations to avoid potential malicious code execution. Open-source models are also vulnerable to attacks in which “bad data” is introduced during training or fine-tuning. This is called "model poisoning," and if attackers inject malicious data into the training set, the model might behave unpredictably or maliciously once deployed.
Additionally, companies are concerned about the lack of security protocols and certifications from some LLM providers, which could lead to regulatory issues or reputational damage. An open-source model does not have the same level of oversight as a more controlled system, and often lacks the same level of dedicated security updates and patches as proprietary systems. Some open-source models may inadvertently expose personal or private information. If a model has been trained on sensitive data (intentionally or unintentionally), it might reveal confidential details or make it easier for attackers to identify individuals, leading to privacy breaches. Without a formal team consistently maintaining the model and addressing emerging vulnerabilities, security risks can be left open for potential attacks.
How can I safeguard my organization when using open source LLMs?
Because of the aforementioned risks, open-source AI models need to be carefully monitored, regulated, and updated to ensure they are used safely and responsibly. Implementing strict data sanitization, access controls, output filtering, and regular security audits when deploying open source LLMs like DeepSeek-R1 is advised.
Another option to consider is using tools like Seek AI, an agentic AI platform built on a system of LLMs for delegating data work to AI agents for faster speed to accurate data insights. Seek is committed to working with the best models available on the market today to help organizations reason over their data, rather than being tied to any one specific LLM.
Seek AI offers its Seek Native app on Snowflake Marketplace, which allows data teams and business professionals to use cutting-edge LLM technology for data within the secure walls of Snowflake, ensuring compliance with modern governance standards. This includes both private and open-source LLMs.
Seek’s agentic AI for structured data, is engineered to automate data query code generation, data query execution and the extraction of accurate data insights, so organizations can make more informed decisions and maximize their data effectiveness for better business outcomes. We recently even added optional integration of DeepSeek’s R-1 model with the Seek Native app that can be used inside Snowflake's secure managed environment.
Want to learn more about Seek’s agentic AI technology? Visit our product overview page here.
