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cross-posted from: https://lemmy.sdf.org/post/31583546

Archived

Security researcher Tenable successfully used DeepSeek to create a keylogger that could hide an encrypted log file on disk as well as develop a simple ransomware executable.

At its core, DeepSeek can create the basic structure for malware. However, it is not capable of doing so without additional prompt engineering as well as manual code editing for more advanced features. For instance, DeepSeek struggled with implementing process hiding. "We got the DLL injection code it had generated working, but it required lots of manual intervention," Tenable writes in its report.

"Nonetheless, DeepSeek provides a useful compilation of techniques and search terms that can help someone with no prior experience in writing malicious code the ability to quickly familiarize themselves with the relevant concepts."

"Based on this analysis, we believe that DeepSeek is likely to fuel further development of malicious AI-generated code by cybercriminals in the near future."

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Gemini 2.5 is our most intelligent AI model, now with thinking.

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cross-posted from: https://lemmy.sdf.org/post/31552333

A Trust Report for DeepSeek R1 by VIJIL, a security resercher company, indicates critical levels of risk with security and ethics, high levels of risk with privacy, stereotype, toxicity, hallucination, and fairness, a moderate level of risk with performance, and a low level of risk with robustness.

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cross-posted from: https://lemmy.sdf.org/post/31525284

Archived

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While the financial, economic, technological, and national-security implications of DeepSeek’s achievement have been widely covered, there has been little discussion of its significance for authoritarian governance. DeepSeek has massive potential to enhance China’s already pervasive surveillance state, and it will bring the Chinese Communist Party (CCP) closer than ever to its goal of possessing an automated, autonomous, and scientific tool for repressing its people.

Since its inception in the early 2000s, the Chinese surveillance state has undergone three evolutions. In the first, which lasted until the early 2010s, the CCP obtained situational awareness — knowledge of its citizens’ locations and behaviors — via intelligent-monitoring technology. In the second evolution, from the mid-2010s till now, AI systems began offering authorities some decision-making support. Today, we are on the cusp of a third transformation that will allow the CCP to use generative AI’s emerging reasoning capabilities to automate surveillance and hone repression.

[...]

China’s surveillance-industrial complex took a big leap in the mid-2010s. Now, AI-powered surveillance networks could do more than help the CCP to track the whereabouts of citizens (the chess pawns). It could also suggest to the party which moves to make, which figures to use, and what strategies to take.

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Inside China, such a network of large-scale AGI [artificial general intelligence] systems could autonomously improve repression in real time, rooting out the possibility of civic action in urban metropolises. Outside the country, if cities such as Kuala Lumpur, Malaysia — where China first exported Alibaba’s City Brain system in 2018 — were either run by a Chinese-developed city brain that had reached AGI or plugged into a Chinese city-brain network, they would quietly lose their governance autonomy to these highly complex systems that were devised to achieve CCP urban-governance goals.

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As China’s surveillance state begins its third evolution, the technology is beginning to shift from merely providing decision-making support to actually acting on the CCP’s behalf.

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DeepSeek [...] is this technology that would, for example, allow a self-driving car to recognize road signs even on a street it had never traveled before. [...] The advent of DeepSeek has already impelled tech experts in the United States to take similar approaches. Researchers at Stanford University managed to produce a powerful AI system for under US$50, training it on Google’s Gemini 2.0 Flash Thinking Experimental. By driving down the cost of LLMs, including for security purposes, DeepSeek will thus enable the proliferation of advanced AI and accelerate the rollout of Chinese surveillance infrastructure globally.

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The next step in the evolution of China’s surveillance state will be to integrate generative-AI models like DeepSeek into urban surveillance infrastructures. Lenovo, a Hong Kong corporation with headquarters in Beijing, is already rolling out programs that fuse LLMs with public-surveillance systems. In Barcelona, the company is administering its Visual Insights Network for AI (VINA), which allows law enforcement and city-management personnel to search and summarize large amounts of video footage instantaneously.

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The CCP, with its vast access to the data of China-based companies, could use DeepSeek to enforce laws and intimidate adversaries in myriad ways — for example, deploying AI police agents to cancel a Lunar New Year holiday trip planned by someone required by the state to stay within a geofenced area; or telephoning activists after a protest to warn of the consequences of joining future demonstrations. It could also save police officers’ time. Rather than issuing “invitations to tea” (a euphemism for questioning), AI agents could conduct phone interviews and analyze suspects’ voices and emotional cues for signs of repentance.

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  • Introducing Stable Virtual Camera, currently in research preview. This multi-view diffusion model transforms 2D images into immersive 3D videos with realistic depth and perspective—without complex reconstruction or scene-specific optimization.
  • The model generates 3D videos from a single input image or up to 32, following user-defined camera trajectories as well as 14 other dynamic camera paths, including 360°, Lemniscate, Spiral, Dolly Zoom, Move, Pan, and Roll.
  • Stable Virtual Camera is available for research use under a Non-Commercial License. You can read the paper here, download the weights on Hugging Face, and access the code on GitHub.
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  • Our open source AI model, Llama, has been downloaded more than one billion times.
  • We believe open sourcing AI models is essential to ensuring people everywhere have access to the benefits of AI, and every download of Llama moves us closer to that goal.
  • Among those one billion downloads are industry leader Spotify, startup Fynopsis and enterprising developer Srimoyee Mukhopadhyay.
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Today we announce Mistral Small 3.1: the best model in its weight class.

Building on Mistral Small 3, this new model comes with improved text performance, multimodal understanding, and an expanded context window of up to 128k tokens. The model outperforms comparable models like Gemma 3 and GPT-4o Mini, while delivering inference speeds of 150 tokens per second.

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Chinese tech company Baidu said on Sunday it has launched two new artificial intelligence (AI) models, which are said to offer the same performance as the Chinese chatbot DeepSeek but at half the cost.

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hpcaitech (the ColossalAI team) has officially released Open-Sora 2.0, an open-source video generation model with 11 billion parameters that has drawn widespread attention for balancing cost and performance. With only about $200,000 in training costs (equivalent to 224 GPUs), the model performs close to top commercial models in multiple evaluations.

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Today we release OLMo 2 32B, the most capable and largest model in the OLMo 2 family, scaling up the OLMo 2 training recipe used for our 7B and 13B models released in November. It is trained up to 6T tokens and post-trained using Tulu 3.1. OLMo 2 32B is the first fully-open model (all data, code, weights, and details are freely available) to outperform GPT3.5-Turbo and GPT-4o mini on a suite of popular, multi-skill academic benchmarks. It is comparable to the leading open-weight models while requiring only a fraction of training compute. For example, OLMo 2 32B takes only one third of the cost of training Qwen 2.5 32B while reaching similar performance. The OLMo 2 family of models—now available in 7B, 13B, and 32B parameter sizes, all can be finetuned on a single H100 GPU node, and all models are available on the Ai2 playground.

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Some of the world’s most advanced AI systems struggle to tell the time and work out dates on calendars, a study suggests.

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