ep3p

Do you work with #MicrosoftSentinel or #AzureMonitor? or just any #KQL solution? I will be adding complex queries about Azure AD, Office 365, Microsoft security tools... and other info to this repository, and commenting them here. So ask me anything :) github.com/ep3p/Sentinel_…

Gemini continues to be the most fascinating model

@NathanMcNulty Logstash seems another option? learn.microsoft.com/en-us/azure/se…

Want to ship syslog to Sentinel? You can't do that directly, install AMA Oh, it's an appliance? You need a syslog server with AMA installed Oh, it's on-prem? You need to install Arc, onboard it to Azure, then install AMA, then you can do it Forget it... I'm shipping to cribl

Idea: An anonymous “vote to end meeting” button on Teams where if 50% of people press it, the meeting ends immediately.

@SecurityAura or company.secure-share[.]workers[.]dev

I like how you can easily track which companies had a BEC by just trying: CompanyName-portal[.]pages[.]dev CompanyName-portail[.]pages[.]dev And if you get a M365 phishing page, you know that company's cooked (at least 1 account).

running search "test" in Advanced Hunting fails due to a column/scalar named IsGenerativeOrchestrationEnabled #AdvancedHunting #DefenderXDR

Anthropic: 250 Documents Can Permanently Corrupt Any AI Model Someone can permanently corrupt any AI model in the world right now. Not by hacking it. Not by breaking its security. By publishing 250 documents on the internet. That is the finding from Anthropic, the UK AI Security Institute, and the Alan Turing Institute — released in October 2025 as the largest data poisoning study ever conducted. Here is what data poisoning actually means. Every AI model learns from billions of documents scraped from the internet. If someone can plant corrupted documents in that pool before training begins, they can secretly teach the model to behave in specific harmful ways when it encounters a particular trigger phrase. The model learns the backdoor during training. It carries it forever. It does not know it is there. Researchers have known about this attack for years. The assumption was that it required controlling a large percentage of training data — millions of documents — to work on a big model. The bigger the model, the more poisoning you would need. This study proved that assumption completely wrong. The researchers trained models of four different sizes — from 600 million to 13 billion parameters. They slipped in either 100, 250, or 500 malicious documents. Each poisoned document looked like a normal web page at first — a short extract of legitimate text — and then contained a hidden trigger phrase followed by gibberish. 100 documents: insufficient. The backdoor did not reliably form. 250 documents: success. Every model, at every size, was permanently backdoored. 500 documents: same result as 250. The number was constant regardless of model size. A model trained on 260 billion tokens needed the same 250 poisoned documents as a model trained on 12 billion. Scale offered zero protection. Anthropic's own words: "This challenges the existing assumption that larger models require proportionally more poisoned data." Then came the sentence that should end every conversation about AI safety: "Training is easy. Untraining is impossible." Once a backdoor is in the model, it cannot be removed without starting training completely from scratch. You cannot identify which 250 documents caused it. You cannot surgically extract the corrupted behavior. You must rebuild the entire model from the beginning. Anyone can publish content to the internet. Academic papers. Blog posts. Forum discussions. Product descriptions. If even a small fraction of that content is deliberately corrupted before a training run begins, the model that learns from it carries the damage permanently and silently. GPT-5. Claude. Gemini. Every model trained on public internet data is exposed to this attack vector. The defense does not exist yet. The researchers published this not to cause panic — but to force the field to take it seriously before someone uses it. Source: Anthropic, UK AISI, Alan Turing Institute (2025) · anthropic.com/research/small… · aisi.gov.uk/blog/examining…

@leonkennedista la popa

2026 is 30% complete.

@Pedro_Torrijos 50 años me parecen muchos, a no ser que quieras un circuito exacto al de un cerebro

La gente que sabe *de verdad* de inteligencia artificial lleva diciendo esto desde bastante antes de que los LLMs fuesen populares. De hecho, la gente que sabe *de verdad* de inteligencia artificial afirma que aún faltan entre 50 y 100 años para que cualquier sistema artificial llegue al nivel de inteligencia de un perro. Otra cosa es que simule que es inteligente o que sus capacidades sean sobresalientes, pero eso no es inteligencia.

@agonerlion @leonkennedista que no le importas bebé, acéptalo ya y está

el toma lacasitos, y la de pacma que el viento se llevaba su wifi

@leonkennedista @agonerlion *frase de pepe sobre la nita y rina*

@leonkennedista @playcircles_ intentaré a las 9

@nefastroso te creó tantas deudas que hasta la cpu te ganó

@nefastroso vaya manco

@reprise_99 can i delete cookies everyday? hahah

Which are you taking - 1 million dollars right now or 5 dollars every time you need to confirm cookie settings on a webpage for the rest of your life?

@N7Taldroid @leonkennedista si no ha mentido

@leonkennedista Como os encanta crear bulos ehhhh

2026 is 25% complete.