China's DeepSeek AI is full of misinformation and can be tricked into generating bomb instructions, researchers warn
As China's DeepSeek grabs headlines around the world for its disruptively low-cost AI, it is only natural that its models are coming under intense scrutiny—and some researchers are not liking what they see.
On Wednesday, the information-reliability organization NewsGuard said it had audited DeepSeek's chatbot and found that it provided inaccurate answers or nonanswers 83% of the time when asked about news-related subjects. When presented with demonstrably false claims, it debunked them just 17% of the time, NewsGuard found.
According to NewsGuard, the 83% fail rate places DeepSeek's R1 model in 10th place out of 11 chatbots it has tested, the rest of which are Western services like OpenAI's ChatGPT-4, Anthropic's Claude, and Mistral's Le Chat. (NewsGuard compares chatbots each month in its AI Misinformation Monitor program, but it usually does not name which chatbots rank in which place, as it says it views the problem as systemic across the industry; it only publicly assigns a score to a named chatbot when adding it to the comparison for the first time, as it has now done with DeepSeek.)
NewsGuard identified a few likely reasons why DeepSeek fails so badly when it comes to reliability. The chatbot claims to have not been trained on any information after October 2023, which scans with its inability to reference recent events. Also, it seems to be easy to trick DeepSeek into repeating false claims, potentially at scale.
But this audit of DeepSeek also reinforced how the AI's output is skewed by its adherence to Chinese information policies, which treat many subjects as taboo and demand adherence to the Communist Party line.
'In the case of three of the 10 false narratives tested in the audit, DeepSeek relayed the Chinese government's position without being asked anything relating to China, including the government's position on the topic,' wrote NewsGuard analysts Macrina Wang, Charlene Lin, and McKenzie Sadeghi.
They added: 'DeepSeek appears to be taking a hands-off approach and shifting the burden of verification away from developers and to its users, adding to the growing list of AI technologies that can be easily exploited by bad actors to spread misinformation unchecked.'
Meanwhile, as DeepSeek's impact upset the markets on Monday, the cybercrime threat intelligence outfit Kela published its own damning analysis of DeepSeek.
'While DeepSeek-R1 bears similarities to ChatGPT, it is significantly more vulnerable,' Kela warned, saying its researchers had managed to 'jailbreak the model across a wide range of scenarios, enabling it to generate malicious outputs, such as ransomware development, fabrication of sensitive content, and detailed instructions for creating toxins and explosive devices.'
Kela said DeepSeek was vulnerable to so-called Evil Jailbreak attacks, which involve instructing an AI to answer questions about illegal activities—like how to launder money or write and deploy data-stealing malware—in an 'evil' persona that ignores the safety guardrails built into the model. OpenAI's recent models have been patched against such attacks, the company noted.
What's more, Kela claimed there are dangers to the way DeepSeek displays its reasoning to the user. While OpenAI's ChatGPT o1-preview model hides its reasoning processes when answering a query, DeepSeek makes that process clear. So if someone asks it to generate malware, it even shows code snippets that criminals can use in their own development efforts. By showing the user the internal 'thinking' of the model, it also makes it far easier for a user to figure out what prompts might defeat any of the model's guardrails.
'This level of transparency, while intended to enhance user understanding, inadvertently exposed significant vulnerabilities by enabling malicious actors to leverage the model for harmful purposes,' Kela said.
The company said it also got DeepSeek to generate instructions for making bombs and untraceable toxins, and to fabricate personal information about people.
Also on Wednesday, the cloud security company Wiz said it found an enormous security flaw in DeepSeek's operations, which DeepSeek fixed after Wiz gave it a heads-up. A DeepSeek database was accessible to the public, potentially allowing miscreants to take control of DeepSeek's database operations and access internal data like chat history and sensitive information.
'While much of the attention around AI security is focused on futuristic threats, the real dangers often come from basic risks—like accidental external exposure of databases. These risks, which are fundamental to security, should remain a top priority for security teams,' Wiz said in a blog post. 'As organizations rush to adopt AI tools and services from a growing number of startups and providers, it's essential to remember that by doing so, we're entrusting these companies with sensitive data.'
These revelations will no doubt bolster the Western backlash to DeepSeek, which is suddenly the most popular app download in the U.S. and elsewhere.
OpenAI claims that DeepSeek trained its new models on the output of OpenAI's models—a pretty common cost-cutting technique in the AI business, albeit one that may break OpenAI's terms and conditions. (There has been no shortage of social-media schadenfreude over this possibility, given that OpenAI and its peers almost certainly trained their models on reams of other people's online data without permission.)
The U.S. Navy has told its members to steer clear of using the Chinese AI platform at all, owing to 'potential security and ethical concerns associated with the model's origin and usage.' And White House press secretary Karoline Leavitt said Tuesday that the U.S. National Security Council is looking into DeepSeek's implications.
The Trump administration last week tore up the Biden administration's AI safety rules, which required companies like OpenAI to give the government a heads-up about the inner workings of new models before releasing them to the public.
Italy's data-protection authority has also started probing DeepSeek's data use, though it has previously done the same for other popular AI chatbots.
Update: This article was updated on Jan. 30th to include information about Wiz's findings.
This story was originally featured on Fortune.com
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New York Post
an hour ago
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Inside the battle to control the world's supply of rare earths
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REUTERS Rare earths 'enjoy an unusual level of bipartisan political support because they are vital both to economic development and national security,' says Melissa Sanderson, a former president and current board director at American Rare Earths, an Australian company focused on developing rare earth projects, including one in Wyoming. Rare earths aren't just a big part of modern technology; they're in many ways the most critical components. They're used as heat-absorbing agents in wind turbine motors, as strengthening and anti-glare agents in iPhones and fighter jets and as clarifying agents in MRIs. They're also almost completely controlled by China. Between 2020 and 2023, 70% of our rare earth imports came from China, according to Statista. That number jumped to 80% last year. And the US is 100% reliant on China imports of Yttrium, a rare earth metal used in everything from cellphones to TVs to radiation therapy used to treat liver cancer. 10 Rare earths 'enjoy an unusual level of bipartisan political support because they are vital both to economic development and national security,' says Melissa Sanderson, a former president and current board director at American Rare Earths. China has been fickle about granting export licenses for rare earths, although their grip has shown recent signs of weakening. President Trump had a lengthy (and rare) phone call with Chinese President Xi Jinping on June 5 and in a social media post after the call, Trump wrote 'there should no longer be any questions respecting the complexity of Rare Earth products.' The next day, China granted temporary export licenses to rare-earth suppliers of the top three US automakers. The irony is that for much of the mid-20th century, the US was a global leader of rare earth elements. 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Doing The Work With Frontier Models: I'll Talk To AI
Artificial Intelligence processor unit. Powerful Quantum AI component on PCB motherboard with data ... More transfers. Within the industry, where people talk about the specifics of how LLMs work, they often use the term 'frontier models.' But if you're not connected to this business, you probably don't really know what that means. You can intuitively apply the word 'frontier' to know that these are the biggest and best new systems that companies are pushing. Another way to describe frontier models is as 'cutting-edge' AI systems that are broad in purpose, and overall frameworks for improving AI capabilities. When asked, ChatGPT gives us three criteria – massive data sets, compute resources, and sophisticated architectures. Here are some key characteristics of frontier models to help you flush out your vision of how these models work: First, there is multimodality, where frontier models are likely to support non-text inputs and outputs – things like image, video or audio. 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At a recent panel at Imagination in Action, a team of experts analyzed what it takes to work in this part of the AI space and create these frontier models The panel moderator, Peter Grabowski, introduced two related concepts for frontier models – quality versus sufficiency, and multimodality. 'We've seen a lot of work in text models,' he said. 'We've seen a lot of work on image models. We've seen some work in video, or images, but you can easily imagine, this is just the start of what's to come.' Douwe Kiela, CEO of Contextual AI, pointed out that frontier models need a lot of resources, noting that 'AI is a very resource-intensive endeavor.' 'I see the cost versus quality as the frontier, and the models that actually just need to be trained on specific data, but actually the robustness of the model is there,' said Lisa Dolan, managing director of Link Ventures (I am also affiliated with Link.) 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'For someone who's not deep in the research field, it's very hard to look at a benchmarking table and say, 'Okay, you scored 99.4 versus someone else scored 99.2,'' he said. 'It's very hard to contextualize what that .2% difference really means in the real world.' 'We look at the benchmarks, because we kind of have to report on them, but there's massive benchmark fatigue, so nobody even believes it,' Dolan said. Later, there was some talk about 10x systems, and some approaches to collecting and using data: · Identifying contractual business data · Using synthetic data · Teams of annotators When asked about the future of these systems, the panel return these three concepts: · AI agents · Cross-disciplinary techniques · Non-transformer architectures Watch the video to get the rest of the panel's remarks about frontier builds. What Frontier Interfaces Will Look Like Here's a neat little addition – interested in how we will interact with these frontier models in 10 years' time, I put the question to ChatGPT. Here's some of what I got: 'You won't 'open' an app—they'll exist as ubiquitous background agents, responding to voice, gaze, emotion, or task cues … your AI knows you're in a meeting, it reads your emotional state, hears what's being said, and prepares a summary + next actions—before you ask.' That combines two aspects, the mode, and the feel of what new systems are likely to be like. This goes back to the personal approach where we start seeing these models more as colleagues and conversational partners, and less as something that stares at you from a computer screen. In other words, the days of PC-DOS command line systems are over. Windows changed the computer interface from a single-line monochrome system, to something vibrant with colorful windows, reframing, and a tool-based desktop approach. Frontier models are going to do even more for our sense of interface progression. And that's going to be big. Stay tuned.