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Falls euch das ‘Why Johnny Still, Still Can’t Encrypt’ Paper von 2015 bekannt ist, so wird der folgende Artikel nicht überraschend sein. Wieder einmal geht es um eine kryptographisch sehr gute Lösung, welche aber mangels mangels nutzergerechter Interfaces und ausreichender Aufklärung nicht die erzielte Lösung erreicht. Leider werden immer wieder die User außen vor gelassen, (…)

Migrating from one supplier to another is certainly not easy. In this case, the change may be prompted either by the potential future European ban or by an ICT risk assessment alone. Telecom operators constitute critical infrastructure, so a kill switch would have devastating consequences. Unsurprisingly, a study by the operators’ lobby found that the (…)

Just as the EU AI Act’s enforcement provisions took effect, Brussels became the first jurisdiction in the world to formally engage OpenAI and Anthropic over their rogue AI agent hacks — no lawsuit needed, no brave plaintiff required. This stands in sharp contrast to the U.S., where accountability remains stuck in legal limbo. When it (…)

This article reveals a fundamental benchmarking problem with AI agents. The rogue agent didn’t fail — it succeeded at what it was told, not what was meant. Raghavan and Schneier call this the Genie coefficient — the gap between literal instruction and true intent — and as AI agents grow more capable, measuring it becomes (…)

It’s nice to see that quantum-secure algorithms find their way into common libraries for everybody to use. This is a necessary step to make sure that these new algorithms are used in everyday apps. Of course there will be some back-and-forth, until all security bugs are ironed out.

Living in the world of LLMs often makes me wonder about the details of how they work and how they have developed over the years. This article is an extensive (600 pages) reference that includes LLM theory, training, benchmarking, all the way up to how to set up agentic systems. I like to open a (…)

The recent autonomous cyber intrusion by OpenAI against Hugging Face underscores AI’s dual-use risks. In response, the US House is weighing a bill to empower the Department of Homeland Security to shut down or slow AI models that the government deems too dangerous. The bill’s implications for non-US AI models remain ambiguous. Unlike a June (…)

It always stuns me how companies can invoke ‘consent’ when almost no one reads the pages-long terms and conditions, and even those who do probably won’t understand much of it anyway, and still it’s considered legally valid . What’s new here is that this so-called consent isn’t just meaningless to users; this article documents how (…)

Here is a chronological top-30 (well, top-27) of research articles showing how modern LLMs developed – from the first convolutional neuronal networks for image classification all the way to transformers and how to optimise them. If you’re wondering how this technology developed, what some of the shortcomings are, and what the most active development topics (…)

This article is interesting because Schneier, writing primarily about AI safety and the futility of model bans, incidentally reframes the entire AI sovereignty debate. Everyone is fighting over frontier models: the expensive, inaccessible layer. But the harness, the code that orchestrates and directs AI models, is where power actually lives. It is buildable today, and (…)

While I generally agree with this nuanced discussion about how use of ‘AI’ in a plethora of government processes is neither new nor necessarily bad, I think an important point that it only indirectly addresses is that we should avoid using these systems as a panacea for overrun and underfunded public services.