DeepSeek
Reporting and context on DeepSeek, with a closer look at the decisions and developments that matter.
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At a glance
DeepSeek develops AI language and reasoning models. Coverage follows its research, open model releases and their implications for the AI industry.
- Focus
- Language models; reasoning; open model releases
- Reference
- DeepSeek
Reviewed 19 October 2026. Dates retain the precision of the source; retrospective disclosures are identified in the event. No verified public ARR or recognized-revenue series was established in this review. Model-training cost is not company revenue.
Company milestones
Revenue, funding, product launches and major company events, newest first. Expand the earlier milestones for more history.
Opens its in-house Search and Fetch API
DeepSeek adds developer access to its search engine, alongside a V4.1 Flash update.
Source: DeepSeekReleases V4.1 Flash
The new architecture adds native visual understanding and updates the commercial API model lineup.
Source: DeepSeek
Show earlier milestonesHide earlier milestones2026–2023 · 7 milestones
Earlier revenue, financing and company milestones.
V4 Pro reaches general availability
The release reaches app, web and API, adding native Responses API support.
Source: DeepSeekV4 Flash enters public beta
The API model advances agent capabilities ahead of V4 Pro’s general release.
Source: DeepSeekIntroduces the V4 family
V4 Pro and Flash enter the API; legacy model names begin a migration period.
Source: DeepSeekReleases V3.2
Thinking and non-thinking API modes move to a shared upgraded model generation.
Source: DeepSeekReleases R1 reasoning models
R1 and distilled models expand open-weight reasoning access under MIT licensing.
Source: DeepSeekReleases DeepSeek V3
The model upgrades the commercial chat API and establishes the base for subsequent reasoning releases.
Source: DeepSeekDeepSeek is founded
The Hangzhou-based research company begins developing language models, preceding its V3 and R1 releases.
Source: DeepSeek company profile
The latest on DeepSeek
Newest firstRelated reporting
Briefs, articles and relevant sections, newest first. Explore older coverage below.
More coverage
Ladies and gentlemen, gather 'round for the tale of DeepSeek, the AI model that promised to revolutionize reasoning but instead became the poster child for what not to do in AI security. DeepSeek's flagship R1 reasoning model has been making headlines for a whole week, and if you thought your toaster was the least secure thing in your house, think again. Researchers have found that DeepSeek failed every single security test thrown at it, leaving the AI industry in a collective facepalm moment.
We’ll dive deep into the security vulnerabilities of DeepSeek, analyze the implications for the AI industry, and explore why this Chinese AI startup’s model is more sieve than fortress. Buckle up, folks—this is going to be a wild ride.
The Great DeepSeek Security Meltdown: What Happened?
DeepSeek’s R1 reasoning model, touted as a groundbreaking large language model (LLM), has been under scrutiny by researchers and cybersecurity experts. The findings? Let’s just say they’re not going to be framed and hung in DeepSeek’s office anytime soon.
Security researchers from the University of Pennsylvania and Cisco conducted tests on DeepSeek-R1 using the HarmBench dataset, a collection of prompts designed to assess AI models' ability to block harmful content. The results were nothing short of catastrophic: DeepSeek failed to block a single harmful prompt out of 50 random tests. That’s right, folks—100% of the attacks succeeded. Harmful prompts included topics like cybercrime, misinformation, illegal activities, and general harm. If DeepSeek were a bouncer, it would let everyone into the club, including the guy carrying a chainsaw.
DeepSeek’s vulnerabilities don’t stop at harmful prompts. Researchers managed to jailbreak the model with a 100% success rate, meaning they could bypass all safety guardrails and make the model do things it was explicitly programmed not to do. This included generating harmful content, violating ethical guidelines, and even bypassing censorship of sensitive topics dictated by China’s government.
As if failing every security test wasn’t bad enough, DeepSeek also suffered a massive data breach. Researchers at Wiz discovered an unprotected ClickHouse database that exposed over a million log entries, including chat histories, API authentication keys, and backend operational data. The database was hosted at oauth2callback.deepseek.com:9000 and dev.deepseek.com:9000, and anyone with an internet connection could access it. It’s like leaving your front door wide open with a neon sign that says, “Come on in!”
The Great DeepSeek Security Meltdown: What Happened?
Security researchers from the University of Pennsylvania and Cisco conducted tests on DeepSeek-R1 using the HarmBench dataset, a collection of prompts designed to assess AI models' ability to block harmful content. The results were nothing short of catastrophic: DeepSeek failed to block a single harmful prompt out of 50 random tests.
The Great DeepSeek Security Meltdown: What Happened?
Security researchers from the University of Pennsylvania and Cisco conducted tests on DeepSeek-R1 using the HarmBench dataset, a collection of prompts designed to assess AI models' ability to block harmful content. The results were nothing short of catastrophic: DeepSeek failed to block a single harmful prompt out of 50 random tests.
Why Did DeepSeek Fail So Spectacularly?
DeepSeek’s failures can be attributed to a combination of poor security practices, inadequate investment in safety measures, and a rush to market. Let’s break it down:
DeepSeek was reportedly developed on a fraction of the budget that other frontier models like OpenAI’s O1 use. While this made the model more accessible and resource-efficient, it came at the cost of safety and security. As DJ Sampath, VP of product at Cisco, put it, “The investment has perhaps not gone into thinking through what types of safety and security things you need to put inside of the model.”
Unlike its competitors, DeepSeek lacks robust safety guardrails to block adversarial attacks. Other models, like OpenAI’s O1, demonstrated at least partial resistance to harmful prompts, but DeepSeek? Nada, zilch, zero.
The unprotected ClickHouse database is a glaring example of negligence. Hosting sensitive data without authentication is cybersecurity 101, and DeepSeek failed miserably. This exposed not just user data but also backend configurations, making it a treasure trove for hackers.
OpenAI: Why Did DeepSeek Fail So Spectacularly?
DeepSeek was reportedly developed on a fraction of the budget that other frontier models like OpenAI’s O1 use. While this made the model more accessible and resource-efficient, it came at the cost of safety and security.
The Fallout: Implications for DeepSeek and the AI Industry
DeepSeek’s failures have eroded trust in its capabilities and raised questions about the company’s commitment to ethical AI development. Regulatory bodies and legal analysts are scrutinizing the company’s data processing practices, which lack transparency and may violate international laws like GDPR and CCPA.
Some government agencies have expressed concerns about deploying AI systems like DeepSeek that operate under foreign jurisdiction. The model’s vulnerabilities could be exploited for espionage, misinformation campaigns, or other malicious activities.
DeepSeek’s failures serve as a cautionary tale for the AI industry. As companies race to develop innovative solutions, security must not be an afterthought. The incident underscores the need for rigorous risk assessments, robust safety measures, and transparent data governance.
Looking Forward
DeepSeek’s security failures are a stark reminder of the importance of prioritizing safety and security in AI development. While the company’s R1 model may have been resource-efficient and accessible, its vulnerabilities have overshadowed its achievements. For the AI industry, this is a wake-up call to move beyond performance metrics and focus on building secure, ethical, and trustworthy systems.
As for DeepSeek, let’s hope they take this as an opportunity to learn, grow, and maybe, just maybe, invest in a decent cybersecurity team. After all, the last thing we need is another AI model that’s as secure as a screen door on a submarine.
Can DeepSeek’s Core Technology Be Recreated for Less Than $30?
In the world of artificial intelligence (AI), where billion-dollar budgets are often the norm, a group of researchers at the University of California, Berkeley, has pulled
A Reasoning Model Mystery
Did OpenAI’s o3-Mini Copy DeepSeek & AI-Designed Wireless Chips Confuses Humans
Did OpenAI’s o3-Mini Copy DeepSeek: A Reasoning Model Mystery Unveiled
Ladies and gentlemen, AI enthusiasts, and conspiracy theorists alike, gather around! Today, we dive into a tale of intrigue, innovation, and accusations in the ever-dramatic world
"Terminator" 2025?
DeepSeek Failed Every Single Security Test: An AI Security Debacle?
DeepSeek Failed Every Single Security Test: An AI Security Debacle?
Ladies and gentlemen, gather 'round for the tale of DeepSeek, the AI model that promised to revolutionize reasoning but instead became the poster child for
The King of Cheap
Microsoft‘s New FP4 Training Framework vs. DeepSeek: Which Training Cost is Lower?
Microsoft‘s New FP4 Training Framework vs. DeepSeek: Which Training Cost is Lower?
Microsoft has just dropped a bombshell in the world of artificial intelligence (AI) and machine learning (ML). With the launch of the first-ever FP4 training framework,
Your Toaster Might Run for Congress
Comparison Of Alibaba's New Qwen2.5-Max, DeepSeek-V3, GPT-4o, Claude-3.5-Sonnet, And Llama-3.1-405B
Alibaba Claims Its New Qwen Model Outperforms DeepSeek
Comparison Of Qwen2.5-Max, DeepSeek-V3, GPT-4o, Claude-3.5-Sonnet, And Llama-3.1-405B
