Full Report
The open-source LLM known as DeepSeek has attracted much attention in recent weeks with the release of DeepSeek V3 and DeepSeek R1, and in this blog, The Tenable Security Response Team answers some of the frequently asked questions (FAQ) about it.BackgroundThe Tenable Security Response Team (SRT) has compiled this blog to answer Frequently Asked Questions (FAQ) regarding DeepSeek.FAQWhat is DeepSeek?DeepSeek typically refers to the large language model (LLM) produced by a Chinese company named DeepSeek, founded in 2023 by Liang Wenfeng.What is a large language model?A large language model, or LLM, is a machine-learning model that has been pre-trained on a large corpus of data, which enables it to respond to user inputs using natural, human-like responses.Why is there so much interest in the DeepSeek LLM?In January 2025, DeepSeek published two new LLMs: DeepSeek V3 and DeepSeek R1. The interest surrounding these models is two-fold: first, they are open-source, meaning anyone can download and run these LLMs on their local machines and, second, they were reportedly trained using less-powerful hardware, which was believed to be a breakthrough in this space as it revealed that such models could be developed at a lower cost.What are the differences between DeepSeek V3 and DeepSeek R1?DeepSeek V3 is an LLM that employs a technique called mixture-of-experts (MoE) which requires less compute power because it only loads the required “experts” to respond to a prompt. It also implements a new technique called multi-head latent attention (MLA), which significantly reduces the memory usage and performance during training and inference (the process of generating a response from user input).In addition to MoE and MLA, DeepSeek R1 implements a multitoken prediction (MTP) architecture first introduced by Meta. Instead of just predicting the next word each time the model is executed, DeepSeek R1 predicts the next two tokens in parallel.DeepSeek R1 is an advanced LLM that utilizes reasoning, which includes chain-of-thought (CoT), revealing to the end user how it responds to each prompt. According to DeepSeek, performance of its R1 model “rivals” OpenAI’s o1 model.Example of DeepSeek’s chain-of-thought (CoT) reasoning modelWhat are the minimum requirements to run a DeepSeek model locally?It depends. DeepSeek R1 has 671 billion parameters and requires multiple expensive high-end GPUs to run. There are distilled versions of the model starting at 1.5 billion parameters, going all the way up to 70 billion parameters. These distilled models are able to run on consumer-grade hardware. Here is the size on disk for each model:DeepSeek R1 modelsSize on disk1.5b1.1 GB7b4.4 GB8b4.9 GB14b9.0 GB32b22 GB70b43 GB671b404 GBTherefore, the lower the parameters, the less resources are required and the higher the parameters, the more resources are required.The number of parameters also influences how the model will respond to prompts by the user. Most modern computers, including laptops that have 8 to 16 gigabytes of RAM, are capable of running distilled LLMs with 7 billion or 8 billion parameters.What makes DeepSeek different from other LLMs?Benchmark testing conducted by DeepSeek showed that its DeepSeek R1 model is on par with many of the existing models from OpenAI, Claude and Meta at the time of its release. Additionally, many of the companies in this space have not open-sourced their frontier LLMs, which gives DeepSeek a unique advantage.Finally, its CoT approach is verbose, revealing more of the nuances involved in how LLMs respond to prompts compared to other reasoning models. The latest models from OpenAI (o3) and Google (Gemini 2.0 Flash Thinking) reveal additional reasoning to the end user, though in a less verbose fashion.What is a frontier model?A frontier model refers to the most advanced LLMs available that include complex reasoning and problem-solving capabilities. Currently, OpenAI’s o1 and o3 models along with DeepSeek R1 are the only frontier models available.DeepSeek was created by a Chinese company. Is it safe to use?It depends. Deploying the open-source version of DeepSeek on a system is likely safer to use versus DeepSeek’s website or mobile applications, since it doesn’t require a connection to the internet to function. However, there are genuine privacy and security concerns about using DeepSeek, specifically through its website and its mobile applications available on iOS and Android.What are the concerns surrounding using DeepSeek’s website and mobile applications?DeepSeek's data collection disclosure is outlined in its privacy policy, which specifies the types of data collected when using its website or mobile applications. It's important to note that data is stored on secure servers in the People's Republic of China, although the retention terms are unclear. Since DeepSeek operates in China, its terms of service are subject to Chinese law, meaning that consumer privacy protections, such as the EU’s GDPR and similar global regulations, do not apply. If you choose to download DeepSeek models and run them locally, you face a lower risk regarding data privacy.Has DeepSeek been banned anywhere or is it being reviewed for a potential ban?As of February 13, several countries have banned or are investigating DeepSeek for a potential ban, including Italy, Taiwan, South Korea and Australia, as well as several states in the U.S. have banned DeepSeek from government devices including Texas, New York, Virginia along with several entities of the U.S. federal government including the U.S. Department of Defense, U.S. Navy and the U.S. Congress. This list is likely to continue to grow in the coming weeks and months.Is Tenable looking into safety and security concerns surrounding LLMs like DeepSeek?Yes, Tenable Research is actively researching LLMs, including DeepSeek, and will be sharing more of our findings in future publications on the Tenable blog.Get more informationDeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement LearningJoin Tenable's Security Response Team on the Tenable Community.Learn more about Tenable One, the Exposure Management Platform for the modern attack surface.
Analysis Summary
# Industry News: Tenable Responds to Growing Government Bans on DeepSeek LLM
## Summary
Tenable is actively researching the safety and security concerns surrounding large language models (LLMs) like DeepSeek, particularly in light of widespread bans imposed by various US government entities (DoD, Navy, Congress) and international governments (South Korea, Australia) on using the model on official devices. This announcement positions Tenable as an agile security provider monitoring AI-related risks, a critical concern given the rapid, and often unvetted, adoption of generative AI technologies.
## Key Details
- **Date:** Ongoing (Referencing recent February 2025 government actions)
- **Companies Involved:** Tenable, DeepSeek (as the subject of research)
- **Category:** Industry Analysis / Security Research Response
## The Story
Tenable's blog post addresses public inquiry regarding their stance on the popular DeepSeek LLM, which has recently faced significant regulatory scrutiny and usage bans across numerous governmental bodies in the US and internationally. The article confirms that Tenable Research is proactively investigating the security implications and safety concerns associated with LLMs, including DeepSeek, promising future disclosures on their findings. This direct acknowledgment serves to reassure Tenable’s customer base that the company is tracking novel risks emerging from generative AI adoption. The article also prominently features Tenable's flagship Exposure Management Platform, Tenable One.
## Business Impact
### For the Companies Involved
- **Tenable:** Reinforces Tenable’s role as a forward-looking security intelligence provider. By publicly researching emerging threats like LLM vulnerabilities, they justify the value of their comprehensive security platform, especially features like "Exposure AI analytics," positioning themselves as essential for managing risk in new technology adoption curves.
### For Competitors
- Competitors focusing solely on traditional vulnerability management may appear less attuned to rapid, emerging risks stemming from generative AI adoption. Tenable gains a timely narrative advantage by demonstrating proactive threat intelligence gathering related to AI platforms.
### For Customers
- Customers gain confidence that Tenable is monitoring risks associated with third-party AI tools they might be considering or already using. This intelligence is crucial for making informed decisions regarding technology procurement and acceptable use policies.
### For the Market
- This signals increased market awareness that AI models themselves, not just their application environments, represent a new category of security risk that requires dedicated threat intelligence and posture management.
## Technical Implications
The core technical implication is the necessity for security vendors to shift research focus toward the inherent risks within foundation models and Generative AI applications, rather than solely focusing on the infrastructure where they run. Tenable's research will likely explore potential data leakage, adversarial prompting, or model manipulation risks associated with DeepSeek.
## Strategic Analysis
- **Market Positioning:** Tenable solidifies its position within the AI Security Posture Management (AI-SPM) conversation (a capability they list), differentiating their Exposure Management platform as capable of handling both traditional IT risks and risks stemming from novel technologies like LLMs.
- **Competitive Advantage:** Leveraging Tenable Research’s findings allows the sales and marketing teams to integrate timely, relevant talking points about AI governance security into their platform pitches for Tenable One.
- **Challenges:** The challenge lies in the speed of LLM development; Tenable must publish actionable intelligence faster than the models evolve or new bans are enacted to maintain relevance.
## Industry Reactions
- **Analyst Opinions:** Analysts are likely to view this as a necessary move, confirming that AI supply chain and usage risk is now a mainstream cybersecurity priority, moving beyond theoretical risks into immediate governance concerns driven by government action.
- **Market Response:** Increased pressure on security tool vendors to demonstrate robust capability or research focus concerning generative AI usage and vulnerabilities.
## Future Outlook
- Expect Tenable to release specific security advisories or blog posts detailing vulnerabilities or risky usage patterns discovered during their DeepSeek research.
- Watch for Tenable to integrate specific AI risk metrics or governance checks directly into the Tenable One platform to help customers identify non-compliant usage of third-party LLMs.
## For Security Professionals
Security teams must immediately review their organization's policies regarding the use of unapproved third-party LLMs like DeepSeek, echoing the bans enacted by federal and state governments. Practitioners should seek guidance from vendors like Tenable on managing the security posture related to AI tool adoption and prepare internal assessments for potential data exposure risk from these tools.