A fake plugin got into two of my WordPress sites through UpdraftCentral, with UpdraftPlus fully updated and a command that passed signature checks. Timeline, IOCs and how to check your site: crintech.pro/en/blog/updraft…

Hay una polémica en Francia con un libro muy exitoso al que alguien ha pasado Pangram (según dicen, un detector infalible), y muchos pasajes han dado que es 100% IA. Veamos por qué NO hay que hacer caso a Pangram. Al menos no como si fuera un ente divino: Lo primero es saber quién dice que Pangram es el mejor detector. Pues lo dice Pangram, dice que su ratio de falsos positivos es virtualmente cero porque no detectan solo patrones, sino que hacen prompt-espejo en embeddings y toda la pesca. Es cierto que hay auditorías externas, que también lo ponen bien, pero son sensiblemente menos robustas que lo que dice el propio Pangram. Y Pangram es una empresa que quiere vender su producto. Yo me pregunto si podría dar unos resultados tan buenos en un sandbox totalmente aislado de Internet, pero démoslo por válido. Eso sí, hay algo que distingue a Pangram de los demás detectores. Otros dicen que "hay una probabilidad del 40% de que este texto sea artificial". Pangram no, Pangram discrimina el texto por secciones y te dice "Este 40% es artificial y este 60% es humano", y te marca esas secciones. Comprobémoslo. He puesto el texto de la imagen 1, un texto sencillote, con un poco de ritmo y sin florituras, bastante sota, caballo y rey. ¿Qué dice Pangram? Pues dice que es 100% humano. Así que, en la imagen 3, le he añadido 14 palabras al texto original, 14 de 230. He transformado una aposición en una adversativa y he añadido un tercer ítem a dos series de dos, transformándolas así en series ternarias. ¿Resultado? 100% Artificial. 100%, TODAS LAS PALABRAS LAS HA ESCRITO UNA MÁQUINA. Pero Pangram, no decías que tú no detectas patrones sino que haces prompts-espejo con red neural y toda la hostia en verso? Que sí, qué sí, que eso lo ha escrito todo una máquina, que te lo digo yo. Vale, es un texto corto (algo menos de una página de un libro), así que vamos a hacerlo más largo. Ya os aviso que el resultado es peor (o puede serlo). Lo cuento en el siguiente tuit, para que veáis las imágenes. (Por cierto, podéis copiar y pegar los textos y haced vosotros mismos la comprobación)

The loudest voices stoking fears about AI dangers have made tremendous headway in the past two weeks. AI technology has not taken some unexpected, dangerous turn, but the hype around it — propelled by what appears to be a well orchestrated PR campaign — has drummed up considerable fear. I worry that it represents a setback for our field. I have written frequently that fears of AI are overhyped. AI’s capabilities can be uncannily human-like and unpredictable, and it’s rational to worry when people who are directly involved express concerns. But I see the problems as a sign of the engineering work that ahead, rather than insurmountable barriers or the sky falling. AI technology continues to advance — which is a good thing! — but technical advances, poorly understood by the public, give those who seek to generate hype repeated opportunities to do so. First, I don’t see any step up in the risk of human extinction from AI compared to a few months ago. The theories about this remain the same fantastical, science fiction scenarios as a few months ago. The biggest change in AI risk is its cybersecurity capabilities — a topic which we should take seriously — but this, too, will not lead to the end of the world. The most notable recent event leading to increased fear was when an OpenAI team deployed an agent swarm that hacked into Hugging Face. Much of the popular press contained significant hype. For example, some publications reported that a swarm of 1,200 agents carried out the attack. While this was technically accurate, as I write this, I have about 1,300 processes running on my laptop. Yes, the ability to get large swarms of agents to work in parallel on a task is a significant technical advance, And, in computing, many processes run at the same time. So this shouldn’t be seen as some magical capability. Additionally, OpenAI’s buggy sandboxing and monitoring processes were key to enabling this incident. Fixing these bugs and putting in place improved monitoring would be appropriate fixes, not pausing AI. There are many well known ways to attack software systems. The main advantage of AI agents is that they are relentless. They will tirelessly try many tactics — and have the patience to chain vulnerabilities together — that previously would have taken an infeasible amount of human effort. But in the long term, I believe the advantage will lie with defenders (because they have more information with which to identify bugs, which they can fix), but the cyber-threat landscape has changed significantly. There are still bottlenecks to identifying and exploiting a vulnerability. AI agents still have to try a lot of things to see what works, and taking these actions takes time and might be detected by defenders. This is why, even though it is now easy to obtain versions of leading open weight models that have had their guardrails removed or weakened, so they will not refuse to try to execute cyber attacks, the world has not ended. I am also concerned about the anthropomorphization of AI in a lot of reporting, where LLMs and agents are unnecessarily treated as if they were people. If I wield a hammer, miss a nail, and accidentally dent the wall, it’s not the fault of the hammer. The problem lies in how I used the hammer. Similarly, if I prompt an agent and it hacks into someone else’s system, the responsibility lies with me, not the agent. Of course, we want to build systems that are as safe and predictable as possible. (For example, an unsafe hammer would be one whose head randomly flies off under normal use.) Today’s agentic systems are not predictable, but I see no reason why, by applying sound engineering practices, we won’t be able to make them extremely safe to use. One new element in the forecasts of AI-enabled doom is AI companies disclaiming responsibility for their own products. “I didn’t do it; my out-of-control agent did!” There’s a balance to be struck between the responsibility of the tool maker and the tool user, but when something goes wrong, let’s hold the people building and/or using the hammer responsible, rather than the hammer. (By the way, if you’re worried about AI bioweapon risk, David Bellamy has a great post on why this, too, is overhyped. Briefly, the bottleneck in building a bioweapon is not intelligence, but lab work and manufacturing.) Pausing AI progress will create much more harm than benefit. First, our adversaries will certainly not slow down. Second, engineering requires discovering problems empirically so we can fix them. If we pause AI by a decade, we will also delay finding and implementing safety engineering fixes by about the same duration. Of course, the incentive to stoke fears — for regulatory capture, to garner attention, or to make one’s technology seem more powerful — remains the same as before. Disclaiming responsibility is a new one. Taking a hard technical look at the actual risks however, I see little factual basis for the degree of fear that’s been stoked up. We still have hard research and engineering work ahead to improve AI safety, but the beneficial applications continue to vastly outweigh the risks, and we should keep building. [Original text (with links): deeplearning.ai/the-batch/is… ]