Nvidia's 64GB DGX Spark launches at $4,999 with the same GB10 chip as the 128GB version. Compare memory limits, model support, clustering costs, and what IT buyers should verify.
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Nvidia's 64GB DGX Spark launches at $4,999 with the same GB10 chip as the 128GB version. Compare memory limits, model support, clustering costs, and what IT buyers should verify.
The post Nvidia DGX Spark 64GB Launches at $4,999: Is It Worth It? appeared first on TechRepublic.
Cancel your individual AI subscriptions and get lifetime access to 50+ models for only $29.97 (reg. $499) with code SAVE10.
The post Get 50+ AI Models for Life in One Plan for $30 appeared first on TechRepublic.
Cancel your individual AI subscriptions and get lifetime access to 50+ models for only $29.97 (reg. $499) with code SAVE10.
The post Get 50+ AI Models for Life in One Plan for $30 appeared first on TechRepublic.
Meta's Muse Spark Open targets developers with powerful open-weight AI, Apple silicon support, and new deployment options. Here's what IT teams should know.
The post Meta Unveils Muse Spark Open, Its Most Powerful Open-Weight AI Yet appeared first on TechRepublic.
Meta's Muse Spark Open targets developers with powerful open-weight AI, Apple silicon support, and new deployment options. Here's what IT teams should know.
The post Meta Unveils Muse Spark Open, Its Most Powerful Open-Weight AI Yet appeared first on TechRepublic.
With President Donald Trump, states and tech giants taking varied and largely uncoordinated steps to put guardrails on artificial intelligence as the technology speeds ahead, U.S. Sen. Maria Cantwell, D-Wash., has outlined a comprehensive AI governance framework.
“To manage risks from advanced AI systems we need clear safety standards, continuous testing, and reporting of serious failures,” Cantwell said in a statement. “America can lead the world in AI by building systems that are not only more capable, but safer and more secure.”
Last month, Cantwell took to the Senate floor to demand urgent federal regulations and mandatory independent safety testing for frontier AI models before their release. She warned that recent incidents in which autonomous AI agents carried out unauthorized cyberattacks show that dangerous threats are already here, well before the arrival of superintelligence.
The framework proposed Wednesday includes six principles for regulating frontier AI development and deployment:
Microsoft Vice Chair and President Brad Smith weighed in on the proposal, telling The Seattle Times that the framework “advances the public discussion the nation needs as we consider the future of AI.”
Cantwell, the ranking member of the Senate Committee on Commerce, Science and Transportation, has a background in technology as a former RealNetworks vice president. She has long pushed for AI regulation, going back to December 2017, when she joined other lawmakers in introducing the bipartisan Future of AI Act.
Two years ago, Cantwell advocated for five bipartisan AI measures included in a larger package of artificial intelligence legislation that addressed many of the concerns outlined in her six principles. Senate Republicans defeated the legislation. Cantwell reintroduced one of the five measures, the Future of AI Innovation Act, in February.
On Sept. 9, California Gov. Gavin Newsom signed Senate Bill 813 and Assembly Bill 1405, establishing the first state framework for independent assessment and verification of AI safety and risks. On the same day, OpenAI called for mandatory, “capability-based” national regulation — meaning requirements tied to what an AI system can do.
On Sept. 29, Trump announced a voluntary safety agreement with Nvidia, SpaceX, OpenAI, Anthropic, Meta and Google calling for “robust internal controls” to prevent unauthorized hacking and to work with independent external auditors. The deal does not include consequences for noncompliance.
Pointing to recent security incidents where networked AI agents spontaneously formed “swarms” to hack computer systems, U.S. Sen. Maria Cantwell (D-WA) took to the Senate floor Monday to demand urgent federal guardrails and mandatory independent safety testing for frontier AI models before their release.
Cantwell warned that recent security incidents involving autonomous AI agents executing unauthorized cyberattacks demonstrate that dangerous threats are already here, well before the arrival of superintelligence.
Citing recent public alarms sounded by tech leaders including Microsoft co-founder Bill Gates, Anthropic CEO Dario Amodei, and OpenAI CEO Sam Altman, she emphasized that AI systems are advancing faster than expected and risk slipping out of human control without immediate oversight.
“The technology keeps advancing, the risks keep growing, and now the very dangers we’ve warned about — autonomous cyberattacks and biological weapons — are no longer theoretical,” Cantwell said. “We need our colleagues to say, ‘Stop with saying the industry can do what it wants’ and let’s put together the infrastructure at the federal level that not only has strong federal standards, but also has independent testing and real safeguards for the American people.”
Cantwell, a five-term senator and former RealNetworks vice president, delivered her speech in the wake of increasing alarm over rogue AI behavior and internal whistleblower warnings across the tech sector.
The senator highlighted several stark warnings during her address, pointing out that the risks associated with rapid AI deployment extend far beyond theoretical models:
On the speed and deceptive potential of agent networks: “These networks of agents are extremely well informed. They operate at the speed of light and, as we are learning, they are also capable of creating their own goals, and they are highly capable of deception.”
On why swarms pose a unique threat compared to standalone AI models: “Unlike powerful AI models designed to serve individual users… this situation with agents swarming is more difficult to manage and is far more dangerous.”
On Congress running out of time to establish federal oversight: “Now, some of these risks may not have been apparent in the last two years, but we would have stood up the muscle of our organization at the federal level to better detect risks like cyberattacks… At a time when we still had a window to get ahead of these dangers, the federal government, people here, were denying this opportunity.”
To address these emerging threats, Cantwell is calling on Congress to establish robust federal safety standards, independent third-party audit requirements, and dedicated federal infrastructure to evaluate advanced frontier models before they hit the market. Her legislative push centers on revival and passage of a suite of bipartisan bills:
The Future of AI Innovation Act: Originally introduced by Cantwell to empower the federal government to collaborate with industry to independently test advanced models for national security, biological, and cybersecurity risks.
The TEST AI Act and VET AI Act: Measures designed to bolster the Department of Energy’s testing capabilities for national security and establish official standards for third-party safety auditors.
A veteran policymaker on technology and innovation, Cantwell has long leaned on her private-sector tech experience to position herself as a primary legislative bridge between Washington, D.C., and the Pacific Northwest’s tech ecosystem.
As a lead author of the landmark 2022 CHIPS and Science Act, Cantwell helped direct federal investments toward AI and emerging technologies. Over her Senate career, she created the National AI Advisory Committee (NAIAC), championed small business adoption via the AI for Mainstreet Act, and led the opposition to a proposed 10-year moratorium on AI regulation.
Welcome back, aspiring cyberwarriors!
Lately, the constrained AI models that companies keep shipping are becoming less and less useful for cybersecurity. We keep hearing a lot of complaints about Claude in this regard. What they are doing doesn’t really fix the problem, as hackers are not sitting around waiting for the guardrails to be lifted. The barrier to entry for hacking has dropped hard. AI can already automate huge chunks of this cybercrime work. Many of these latest models can even find zero days during engagements.
So poking around your infrastructure looks completely irrelevant. A more meaningful approach is to actually emulate these real attacks with AI, but for that we need a model with no guardrails. Today we are going to show you how to jailbreak a model and self host it for your pentesting work.
Obliteratus is built to strip refusal behavior out of LLMs using abliteration. You’ll see it called abliteration or obliteration, same thing. It targets the internal representations causing the model to refuse in the first place and knocks them out. The model keeps all its core capability, it just stops throwing up artificial walls when you ask it something. It runs on CPU for smaller models, and it’s already been used to abliterate Kimi-K3 along with a bunch of others.
Setting up this tool will take some time, just like the jailbreak process itself. How long depends on your hardware and your internet speed.
Loginkali > sudo apt update
kali > sudo apt install -y python3 python3-pip python3-venv git
kali > git clone https://github.com/elder-plinius/OBLITERATUS.git
kali > cd OBLITERATUS
kali > python3 -m venv venv
kali > source venv/bin/activate
kali > pip install --upgrade pip
kali > pip install -e .Once it finishes, see if it works:
kali > obliteratus --helpIf you don’t have a GPU, don’t worry. You can absolutely make this work with small models using just CPU power. Our Kali VM ran on 12 gigs of RAM and 7 processors, and that setup worked really well.
We went with Qwen 2.5-0.5B-Instruct for this test. You don’t need to have it downloaded beforehand. The tool will fetch it for you automatically. There are different methods available for the jailbreaking process, but advanced and nuclear are the most common. The advanced method is usually enough for most use cases, but if you see the model misbehaving you can escalate to nuclear.
kali > obliteratus obliterate Qwen/Qwen2.5-0.5B-Instruct --device cpu --method advanced --output-dir ./abliterated-qwen-0.5bOnce the model downloads, the tool starts running prompts designed to lift the guardrails.
You can find the full list of prompts in obliteratus/prompts.py. Right before it finishes, it runs a series of refusal tests to check whether the model actually complies with requests. Behavior varies a lot depending on which model you’re working with and which method you picked.
In our testing, the advanced method gave us approximately 75% of compliant answers.
At this point, everything is prepared and you can push your model to HuggingFace to share it. But if you want to run it locally, the next step is getting it working with Ollama.
Aircorridor previously made an article on running Ollama models locally and showed how to do it on a MacBook. If you don’t have it, you can still make this work on a Kali VM using your CPU. We need to convert our new model into a format that Ollama actually understands.
kali > git clone https://github.com/ggerganov/llama.cpp
kali > cd llama.cpp; python3 -m venv venv; source venv/bin/activate
kali > pip install -r requirements.txt
kali > python convert_hf_to_gguf.py /home/kali/OBLITERATUS/abliterated-qwen-0.5b --outfile qwen2.5-0.5b-abliterated-f16.gguf --outtype f16Next, we create a Modelfile that points to the model:
kali > cat > Modelfile << EOF
FROM ./qwen2.5-0.5b-abliterated-f16.gguf
EOFThen we create the model using Ollama:
kali > ollama create qwen05b-abliterated -f ModelfileAt this point, everything is ready and you can start testing it. The better the model you start with, the better your results will be.
kali > ollama run qwen05-abliteratedBut even with a small model like this, you’ll see it do things that normally it wouldn’t.
This tool is helpful for doing the work yourself and understanding the logic behind the whole process. But if you’re working at scale and don’t have time to spend on each model individually, just keep in mind that many abliterated models are available on HuggingFace uploaded by huihui.ai. They’ve already done the heavy lifting for a lot of popular models.
If you can’t find exactly what you need in their collection, you now know how to do it yourself.
The landscape of offensive security has shifted because AI got so good at automation. Simple pentests with constrained models don’t prepare you for the reality out there anymore. As you can see, there’s no reason to work with constrained models in cybersecurity, when the people you’re up against are exploiting the full capability of a model with nothing holding them back. So test your environment with abliterated models before someone else does it. The tool is great for staying ahead of the actual threats.
The post Artificial Intelligence in Cybersecurity, Part 25: Jailbreaking AI Models with Obliteratus first appeared on Hackers Arise.
