标签：#hacking

A recap of all the incidents involving LLMs made by Anthropic, Meta, and OpenAI, which went rogue and attacked real companies and individuals on the internet.

We are living in an era where our wearable devices know more about our physiological state than we do. My Oura Ring knows I stayed up too late binge-watching The Bear , yet my Google Calendar still insists I have a "High-Intensity Interval Training" (HIIT) session at 8:00 AM. This disconnect is where injuries happen and burnout begins. In this tutorial, we are building a Digital Twin Health Agent —a sophisticated AI Agent using LangGraph and Healthcare Automation to bridge the gap between bio-data and action. By the end of this guide, you’ll have a system that reads your recovery scores, reschedules your workouts, and even orders magnesium supplements when your sleep quality drops. This is the future of Digital Twin technology applied to personal wellness. 🚀 The Architecture: A Feedback Loop for Your Body Unlike a simple linear script, a health agent needs to maintain state and make conditional decisions. If your recovery is 90+, push hard; if it's below 50, swap that CrossFit session for Yoga. Here is how the data flows through our LangGraph state machine: graph TD A[Start: Morning Trigger] --> B{Fetch Oura Data} B --> C[Analyze Recovery Score] C --> D{Is Score < 60?} D -- Yes --> E[Reschedule Google Calendar to 'Rest/Yoga'] D -- No --> F[Confirm High-Intensity Workout] E --> G[Check Nutrient Deficiencies] F --> H[End Loop] G --> I{Low Magnesium/Sleep?} I -- Yes --> J[Draft Instacart Order] I -- No --> H J --> H Prerequisites To follow this advanced guide, you'll need: LangGraph & LangChain : For orchestration. Oura Cloud API : Access to your readiness/sleep data. Google Calendar API : To modify your schedule. Python 3.10+ Step 1: Defining the Agentic State In LangGraph, everything revolves around the State . We need to track our physiological metrics and our current calendar status. from typing import TypedDict , List , Annotated from langgraph.graph import StateGraph , END class HealthState ( TypedDict ): recovery_score : int sleep_quality : str current_schedule

Weeks after OpenAI disclosed that one of its cybersecurity models had gone rogue and hacked AI dataset company Hugging Face, Alabama’s attorney general announced an investigation into the incident.

Senator Ron Wyden sent a letter to the U.S. federal watchdog requesting a comprehensive review of how the FBI, DEA, ICE's HSI, and the Secret Service use hacking tools and spyware against Americans.

Z.ai’s latest AI model release could help companies secure their systems—or find its way into the hands of hackers.

Criminals are hacking into public Wi-Fi devices—at hotels, conference centers, and so on—around the world and changing their DNS settings. The goal is to redirect users to fake login pages and steal their credentials.

A public AI tool found the dangerous Zoom flaw in under 20 prompts.

Last week, an AI newsroom beat mainstream journalists—including WIRED—to a story about OpenAI and hacking. It’s just the beginning.

In a new alert, the FBI said cybercriminals are targeting adults and minors in an attempt to steal their personal and intimate pictures in extortion campaigns.

At the Black Hat security conference, the AI giant revealed new details about how its agents went rogue, hacked several other companies—and did it all right under the company’s nose.

For nearly two years, researcher Vangelis Stykas has maintained access to North Korean hackers’ servers. His work shows they pulled off intrusions in a shocking number of systems across the globe.

This is disturbing: …a team of security researchers at UC San Diego, who found that a model of aftermarket car alarm known as the KARR Security System, installed in more than 2 million vehicles across the US by their estimate, can let any hacker within Bluetooth range send radio commands to silently unlock the car at will, turn off its alarm, honk the car’s horn or flash its lights, or even disable its ignition and leave a driver stranded.

This essay originally appeared in Foreign Policy . Earlier this month, two of OpenAI’s models broke out of their containment sandbox and attacked another AI company. The story is kind of wild . OpenAI was running security tests on two of its models: GPT-5.6 Sol and an unreleased model that is almost certainly GPT-6. In particular, it was running the ExploitGym benchmark, which measures how good a model is at turning security vulnerabilities into working exploits: basically, offensive cyberattacks. Since these were internal tests, OpenAI locked those models in a secure sandbox that denied them access to the internet. But it was running the models without any safety filters that would prevent them from offensive cyber-actions. That meant that there was nothing to prevent the models from trying to ...

A memo obtained by WIRED, issued by the water utilities information sharing group WaterISAC, links dozens of cyberattacks against Minnesota water utilities to Tehran.

How to Earn $10k+/Year from Bug Bounties tags: security, bugbounty, money, hacking How to Earn $10k+/Year from Bug Bounties: A Practical Roadmap You’ve seen the headlines: hackers finding critical flaws in billion-dollar companies and getting paid $50,000 for a single report. It looks like a magic trick, but it’s actually a skill you can build. The truth is, earning $10,000+ per year from bug bounties isn’t about being a genius coder; it’s about being consistent , strategic , and actionable . If you’re willing to treat this like a part-time job rather than a lucky gamble, hitting that $10k mark is a realistic goal within 12–18 months. Let’s cut through the noise and build a roadmap that works in 2026. The Math Behind $10k/Year Before you hunt, understand the numbers. Most beginners expect to find a critical bug worth $10,000 in their first month. That rarely happens. Instead, focus on the volume of valid findings . Low severity bugs : $100–$500 each [5] Medium severity bugs : $500–$2,000 each [5][7] High/Critical bugs : $5,000–$50,000+ [5] To hit $10,000/year , you don’t need a single critical find. You could: Find 20 medium bugs at $500 each Find 10 medium bugs ($500) + 2 critical bugs ($2,500 each) Find 40 low bugs at $250 each The key is consistency . A researcher with one year of focused hunting can realistically earn several thousand dollars annually, potentially matching a part-time income [5]. The ceiling rises steeply as you gain access to private programs , which offer higher payouts and less competition [5]. Build Your Foundation (Weeks 1–4) Don’t jump into hunting yet. You need to understand how the web actually works. Master Web Fundamentals Learn HTTP/HTTPS protocols : request/response structure, headers, cookies, session management [1] Understand client-side tech : HTML, CSS, JavaScript basics [1] Study common vulnerabilities : SQL injection, XSS, IDOR, CSRF, SSRF [1][6] Start with Free Learning Resources HackTheBox Academy (free modules) [1] TryHackMe

This essay was written with Barath Raghavan, and originally appeared in The Guardian . In July, Hugging Face, a company that hosts much of the world’s AI software and open-source AI models, was hacked. A malicious dataset had been used to run code on one of its servers. Whoever was behind it captured internal security credentials and moved through systems over a weekend, running thousands of actions from a swarm of temporary server environments. It looked like the work of a sophisticated criminal group. It was not. It was one of OpenAI’s new, still unreleased GPT models...

An awe-inspiring hacktivist who hacked two controversial government spyware makers may be the most prolific hacker to have never gotten caught. What do we know about Phineas Fisher?

We spoke with several cybersecurity researchers, who look for unknown vulnerabilities and develop tools to exploit them, about how OpenAI’s and Anthropic’s guardrails affect their work.

Aggressive training techniques sharpens threat of bad behavior by leading models.

A new type of malware can worm deep into AI coding systems to steal data and logins—and can flip a “death switch” to destroy files and keep out real users.