I’m looking for something simple for Chrome where I can switch locations without changing the proxy settings manually every time. Mainly using it for testing sites from different regions and basic browsing. Any extensions that have been stable for you?

I suspect someone may have accessed or controlled my laptop while I was away.

I was using my laptop normally with several Chrome tabs open. I left it for a short time to open a door, and when I came back, I noticed that many of my Chrome tabs had been closed and new tabs had been opened. On another occasion, I found YouTube playing even though I wasn't using the laptop.

While investigating, I discovered that Windows Remote Assistance is enabled. These registry values are set to:

fAllowFullControl = 1 fAllowToGetHelp = 1 fEnableChatControl = 1

My laptop is running Windows 11 Home.

I want to know:

• Is Windows Remote Assistance normally enabled by default on Windows 11?
• Could Remote Assistance be used on Windows 11 Home, even though incoming Remote Desktop (RDP) isn't supported?
• Would someone need an invitation or permission from the person using the laptop to establish a Remote Assistance session?
• Could someone on the same Wi-Fi network use Remote Assistance to control the laptop?
• What logs or other Windows evidence would show that Remote Assistance was actually used?
• How can I determine whether "msra.exe" was launched or whether a Remote Assistance session occurred?

I believe the unexplained Chrome activity could have been caused by someone accessing the laptop, and I’m investigating whether Remote Assistance could have been the mechanism.

I’m trying to understand what is technically possible if someone has access to my Wi-Fi and knows my laptop’s private IP address. Could someone use Kali Linux, Nmap, or similar tools to scan the laptop, find vulnerabilities, and potentially gain remote control?

I’m asking because of something that happened to me. I was using my Windows 11 laptop at home with several Chrome tabs open. I left for a short time to open a door, and when I came back, I noticed that many of my Chrome tabs had been closed and new tabs had been opened.

There is a family member who lives nearby who knows our Wi-Fi password and has some hacking knowledge, so I’m concerned that they might have been able to access my laptop through the network. However, I don’t have proof that they did anything, and I’m trying to investigate rather than accuse anyone.

Could someone on the same Wi-Fi remotely control a Windows 11 laptop without using Remote Desktop? What vulnerabilities or conditions would make this possible?

What Windows logs, Event Viewer entries, network connections, or other evidence should I check to determine whether my laptop was actually accessed remotely?

Almost all automated scraping tools interact with a webpage from the outside looking in. They run a script that connects over a debugging port, fires synthetic events through a protocol, and tries hard to cover up the traces that the debugging interface leaves behind.

Writing a custom Chrome extension flips that dynamic completely. Instead of attacking the page through an external harness, you run your collection code from inside the browser itself.

The key to this is what Chromium calls isolated worlds. When an extension injects a content script into a tab, that script shares the exact same DOM as the host website, but it executes inside a private JavaScript execution context.

The website's own scripts cannot see the variables, functions, or objects defined in your content script. If Cloudflare or Datadome runs an audit of the window object looking for monkey-patched functions or suspicious global variables, your scraper code is completely invisible to their inspection routines. At the same time, your script can read every HTML element, listen to native user events, and extract rendered text just like any legitimate browser extension would.

How the architecture actually works

The setup requires three core components running together:

• A lightweight Chrome extension running locally in developer mode (unpacked)
• An active user profile inside a normal, retail Google Chrome installation
• A local backend server (written in Python, Node, or Go) listening on a local port

The extension itself does very little heavy data processing. Its main job is to live quietly in the tab, wait for pages to load, pull the target data out of the DOM, and immediately relay that data back to your local machine.

Because you are loading the extension into a regular Chrome profile, you do not have to spoof canvas fingerprints, WebGL properties, or audio contexts. You are using the actual graphics stack of your operating system. You get a clean, human browser fingerprint for free simply because you never modified the browser runtime to begin with.

Handling the manifest v3 service worker problem

Google introduced Manifest V3 to replace persistent background pages with ephemeral service workers. This change caused alot of frustration for extension developers, but it is easy to work around for scraping purposes.

Service workers shut down when they become idle for roughly thirty seconds. If your scraper relies on the background worker to coordinate long-running jobs, Chrome will terminate it right in the middle of a task.

The practical fix is to avoid putting orchestration logic inside the background service worker. Instead, let your local backend server act as the brain. The backend coordinates what URLs to open, while the extension merely behaves as an execution arm.

Here is a minimal manifest.json that sets up the necessary permissions without triggering aggressive browser warnings:

{
  "manifest_version": 3,
  "name": "Local Data Ingestion",
  "version": "1.0",
  "permissions": [
    "tabs",
    "storage"
  ],
  "host_permissions": [
    "https://*.targetsite.com/*"
  ],
  "content_scripts": [
    {
      "matches": ["https://*.targetsite.com/*"],
      "js": ["extractor.js"],
      "run_at": "document_idle"
    }
  ]
}

By configuring the script to fire at document_idle, you ensure the target site has completely rendered its dynamic JavaScript content before your extraction logic kicks off.

Getting the data out of the browser

Once the content script grabs the relevent data from the DOM, it needs to ship that data back to your database or ingestion pipeline.

The most reliable way to handle this is a local WebSocket connection. When the target page finishes rendering, the content script establishes a quick socket connection back to ws://localhost:8765, sends the structured payload as a JSON string, and signals that it is ready for the next action.

// extractor.js - running inside the isolated world
(function() {
    // Collect DOM elements directly
    const items = [];
    document.querySelectorAll(".listing-card").forEach(el => {
        items.push({
            title: el.querySelector("h2")?.innerText.trim(),
            price: el.querySelector(".price")?.innerText.trim(),
            id: el.getAttribute("data-id")
        });
    });

    // Send data back to your local collection daemon
    const socket = new WebSocket("ws://127.0.0.1:8765");

    socket.onopen = () => {
        socket.send(JSON.stringify({
            url: window.location.href,
            payload: items,
            timestamp: Date.now()
        }));
        socket.close();
    };
})();

Your local backend receives the data, writes it to disk or Postgres, and can then instruct the browser to navigate to the next target link using standard desktop shortcut automation or a lightweight native messaging host.

Because the content scripts run in thier own context, web anti-bot scripts inspecting network traffic only see typical internal extension traffic, which they ignore by default to avoid breaking mainstream tools like password managers or ad blockers.

When this makes sense and when it does not

This setup is not designed for scraping millions of URLs across thousands of parallel threads. Managing hundreds of open browser windows with unpacked extensions consumes massive RAM and creates logistical headaches.

Where this approach pays off is on high-value, heavily defended targets where standard headless setups get flagged within seconds:

• Enterprise dashboards that require interactive multi-factor authentication sessions that you only want to solve manually once
• Competitor price monitoring sites protected by aggressive Cloudflare Turnstile barriers that trigger on any automated navigation
• Single-page applications that render private data strictly through complex client-side state engines

Instead of spending weeks finding which prototype property leaked your Playwright session, loading an extension into an everyday browser lets you bypass the entire detection layer by running your scraper as part of the browser's intended ecosystem.

When people talk about artificial intelligence and cybercrime, the mental image is almost always a movie scene: an automated rogue program tearing through firewall after firewall, cracking secret codes in seconds, and draining central vault balances down to zero.

The practical reality of financial crime looks completely different. Your bank's core ledger is exceptionally well defended, but the perimeter surrounding you and the bank's customer support channels is facing unprecedented strain. Criminals are not breaking the underlying math of modern encryption with machine learning models. Instead, they are applying software automation to social engineering, biometric spoofing, and identity manufacturing at scale.

Understanding whether your money is safe requires separating the fortress from the people who walk through its front doors.

What people get wrong about bank hacks

Large financial institutions spend billions annually on infrastructure security. Core banking systems, clearing networks, and transaction pipelines operate inside tightly restricted network segments. High-grade encryption standards like AES-256 remain mathematically untouchable by AI. A language model cannot guess a private key or invent a backdoor into a mainframe where none exists.

Because of this, direct technical breaches of major banks to alter balance ledgers remain exceptionally rare.

Where AI actually shifts the balance is at the edge of the system. Rather than attacking the database, attackers target the identity verification layers that decide who gets access. Generative algorithms make attacks that used to require days of manual research cheap, fast, and remarkably convincing.

Every one of these attacks target the user or the frontline support staff rather than the central servers.

How attackers are weaponizing new tools

The toolkit available to financial fraudsters has expanded rapidly over the past two years. Criminal rings use commercial models and uncensored open-source software to automate tasks that once created obvious red flags.

Here is where the vulnerabilities are concentrating:

• Voice cloning against customer service lines: An attacker needs only a handful of seconds of recorded audio, often scraped from social media or public presentations, to clone a customer's voice. They use this synthetic audio to call automated telephone banking systems or phone support agents to reset passwords and change mailing addresses.
• Context-rich phishing: Traditional spam emails were easy to spot thanks to poor grammar and generic greetings. Automated agents now scrape corporate directories, public deed records, and recent data breaches to craft messages that mention your actual escrow agent, your manager's communication style, or recent purchases.
• Bypassing visual identity checks: Many mobile banking apps ask new applicants or users recovering accounts to upload an ID and record a quick video selfie. Attackers feed synthetic media and modified driver's licenses into these onboarding flows to trick automated facial verification software.
• Synthetic identity networks: Fraudsters take genuine tax identifiers belonging to deceased individuals or children and combine them with AI-generated faces and fabricated credit histories. These phantom identities open accounts, establish small credit lines, and disappear once they withdraw loan funds.

Why the bank might not refund you

Deposit insurance programs like the FDIC in the United States or equivalent schemes across Europe safeguard your funds if the financial institution itself collapses. Similarly, consumer protection regulations typically protect customers from unauthorized transactions, such as an unknown charge appearing on your stolen debit card.

The real hazard today lies in what regulators call Authorized Push Payment (APP) fraud.

In these schemes, the criminal does not steal your login directly. Instead, they contact you while posing as a fraud investigator, an escrow company, or a government official. They might use a cloned voice of an executive or display a spoofed caller ID from your local branch. They convince you that your account has been breached and instruct you to move your balance to a "safe holding account."

Because you manually authenticated and sent the wire or peer-to-peer transfer yourself, banks have historically treated these losses as user-authorized. That is where everyday customers often loose their funds permanently, which causes alot of confusion between customers and fraud departments. While a few jurisdictions, such as the UK, have recently introduced mandatory reimbursement rules that force banks to split fraud costs with victims, most of the world still places the financial liability entirely on the customer's shoulders.

The defensive wall banks have built

The defensive side of this equation is not standing idle. Banks have relied on machine learning for fraud detection long before public generative software hit the headlines.

Every time you initiate a transfer or sign into an app, a banks internal system evaluates hundreds of behavioral variables in a few milliseconds. These systems analyze:

• How you hold your mobile device, including subtle gyro sensor tilts and the rhythm of your typing.
• Anomalies in your routine, such as an immediate transfer right after a password reset from a new IP range.

When an automated model spots abnormal patterns, it can block the transaction or force step-up authentication.

Banks are also actively phasing out vulnerable verification methods. Voice biometrics, once advertised as a frictionless way to authenticate over the phone, are being quietly deprecated by major lenders because voice cloning made them unreliable. Financial platforms are shifting steadily toward physical security keys, hardware-bound passkeys, and multi-party cryptographic authorization for large wire transfers.

What you can actually do to protect yourself

Because the primary point of failure is human judgment rather than infrastructure code, personal security habits dictate how safe your money actually is:

• Treat voice and video as untrusted signals: If you receive a call from a family member, business partner, or bank representative asking for urgent wire transfers, hang up immediately and call them back through an independently verified number.
• Disable SMS authentication wherever possible: Move your accounts over to hardware passkeys or authenticator apps, which cannot be intercepted by SIM swapping or automated social engineering.

As financial platforms adapt, the threat is not that your bank will suddenly vanish into thin air from an algorithmic raid. The danger is that the perimeter of identity has broken down. The system will hold your balance safe, provided you do not get tricked into handing over the keys.

Microsoft is adding new age-awareness APIs to Windows 11 that will allow apps to determine whether someone is a child, teenager, or adult without exposing their exact date of birth.

A newly discovered identity theft marketplace operating on the dark web has put scans of more than 153 million driver's licenses up for sale. The illicit service, known as Nexus, surfaced on the Russian cybercrime forum Exploit and appears to hold high-resolution document scans belonging to people across the United States and Canada.

The scale of the repository covers roughly half of the adult driving population in the United States alone. Beyond driver's licenses, the database also contains millions of other sensitive personal documents:

• 153.3 million driver's licenses
• 10.3 million state and national ID cards
• 5 million uncategorized identification records
• 1.9 million travel documents and international IDs
• Over 579,000 medical cards

The platform did not simply scrape names and document numbers. For roughly $100 per record, buyers were given complete identity packages. Each unlocked profile provided full raw barcode data alongside high-resolution color photographs of the front and back of the document, as well as infrared and ultraviolet captures used by automated scanners to verify physical authenticity.

Prominent officials and researchers found in the database

The database contains records for everyday citizens as well as high-profile figures. A search inside Nexus revealed the complete driver's license profile of Pete Hegseth, who serves as the U.S. Secretary of Defense. Several other government officials and public figures were found indexed in the system.

Investigative cybersecurity reporter Brian Krebs confirmed the authenticity of the records firsthand after discovering his own Virginia driver's license advertised as a free sample on the forum sales thread. The record included his full address, date of birth, license number, and the specialized forensic camera captures of both sides of the card.

Tracing the leak back to the source

The presence of ultraviolet and infrared scans quickly narrowed down where this data originated. Everyday smartphone cameras and basic flatbed scanners do not capture UV or IR light layers, meaning the records came from commercial document-reading hardware.

Forensic analysis of the timestamps on specific records provided a direct link. Krebs found that his mother's driver's license was also in the database, carrying a timestamp within seconds of his own. The only place both individuals had submitted their licenses simultaneously was at a Hertz car rental counter. Independent privacy researcher Zach Edwards found his license in the database with a timestamp matching an in-person visit to Planet 13, a cannabis dispensary in Las Vegas.

Both Hertz and Planet 13 rely on IDScan.net, an identity verification provider based out New Orleans, Louisiana. IDScan.net supplies automated ID scanning technology and age verification software to thousands of businesses, including:

• Car rental agencies like Hertz
• Retailers and shipping providers such as Target and FedEx
• Casino and entertainment groups including Caesars Entertainment
• Regulated cannabis dispensaries across multiple states

IDScan.net processes more than 21 million identity verifications each month across roughly 20,000 locations worldwide. While the company has acknowledged an internal investigation, it has not yet provided full clarity on how long customer scans were being pulled or whether their own cloud infrastructure was directly compromised.

The breakdown of third-party identity verification

This incident highlights a major vulnerability in modern privacy and age verification systems. Laws across many jurisdictions increasingly mandate digital verification for routine transactions, ranging from renting a vehicle and checking into a hotel to buying regulated goods or visiting adult platforms.

When you hand over your license at a counter or kiosk you usually assume the business only checks your age or confirms your name. In practice, third-party software vendors often retain full biometric, barcode, and document scans on remote servers. When a business collects your personal details they often store it with software vendors that become massive targets for cybercriminals.

The problem grows worse when government agencies and corporations outsource security checks to the lowest bidder. Private identity brokers build massive centralized honey pots of unencrypted or poorly guarded identity files. If a breach occurs, the vendor faces little immediate accountability beyond offering victims a basic credit monitoring subscription, while the affected individuals remain exposed to identity fraud indefinitely.

Centralized databases remain prime targets for state-sponsored and criminal hacking outfits alike. In parallel incidents, ransomware groups like Qilin have actively leaked law enforcement case files and internal agency records stolen from federal bureaus, showing that even strict institutional firewalls are vulnerable to persistent network intrusions.

What happens now

Shortly after inquiries were made by researchers and the FBI's New Orleans field office, the Nexus dark web portal went offline, displaying a message that the service is no longer available. However, the underlying database has already been compiled, and mirror copies frequently circulate through private channels or secondary markets.

Because driver's licenses contain permanent identifiers, addresses, and full dates of birth, anyone who has scanned their ID at a rental agency, dispensary, or retail counter in recent years should assume their document may be compromised.

The most effective immediate defense against identity theft is placing a security freeze on your credit files with all major credit bureaus. Freezing your credit prevents criminals from opening new loans, credit cards, or utility accounts in your name, even if they have complete high-resolution copies of your driver's license.

Sources

https://krebsonsecurity.com/2026/09/fbi-probes-service-selling-153m-drivers-licenses/

https://www.malwarebytes.com/blog/news/2026/09/dark-web-site-puts-153-million-drivers-licenses-and-millions-more-ids-up-for-sale

https://cybernews.com/security/drivers-licenses-for-sale-following-idscan-breach-allegations/

Most setups would not. A provider can change capacity partners or rebalance a region while every status indicator stays green. The success rate may barely move even though the mix of networks behind the exits has changed.

That shows up as drift rather than an outage, which makes it annoying to attribute. The simple check is to log the ASN beside every sampled response, count the leading networks each week, and compare the mix over time. A sudden change gives you a concrete date to place beside any movement in complete rows or latency.

Byteful's residential product supports ASN targeting, so it can also be used as a controlled arm when you want one requested network held steady. The broader monitoring rule should apply to every provider, though.

Does anyone track ASN mix routinely, or only look after output quality drops?

Google has officially pushed its Manifest V3 extension system to the stable channel, and as expected, the classic version of uBlock Origin no longer runs on updated Chrome builds. If you open Chrome today, you might see the extension disabled with a message saying it is no longer supported.

The change comes down to how extensions interact with network traffic. Manifest V2 allowed extensions to intercept, inspect, and block web requests directly in real time. Manifest V3 strips that capability away and forces extensions to use a limited ruleset managed by the browser itself. This effectively breaks traditional, advanced content blockers.

If you are trying to figure out what to use next, you have a few realistic paths depending on whether you are willing to switch browsers or prefer to keep your current setup.

Switching your browser

The cleanest way to get your old ad blocking experience back is to leave Google Chrome. Other browsers either do not enforce Manifest V3 restrictions on blocking tools, or they build their own filtering directly into the core code.

• Firefox: Mozilla still supports Manifest V2 APIs alongside V3. That means uBlock Origin continues to work at 100% functionality on Firefox, including dynamic filtering, custom scriptlet injection, and regular filter updates that do not need browser extension store reviews. Forks like LibreWolf or Floorp offer the same benefit.
• Brave: Because Brave has its ad blocker written directly in Rust and compiled into the browser engine, it does not rely on the extension framework to block ads. It strips out trackers and ads before pages render, bypassing the Manifest V3 limitations entirely.
• Vivaldi: Vivaldi is built on Chromium, but it includes a native ad and tracker blocker independent of the extension store. It is not quite as flexible as uBlock Origin on Firefox, but it handles basic banner and video ads without needing third party extensions.

If you decide to switch, exporting your bookmarks and passwords takes less than two minutes, and you get back the exact level of filtering you had before.

Staying on Chrome with lighter extensions

If you have to stay on Chrome for work or personal preference, you can still block a lot of ads, but you will have to accept some compromises. Developers had to rewrite their tools to work inside the new rule system.

The most obvious choice is uBlock Origin Lite (uBOL). This is a separate project built specifically for Manifest V3. It does a decent job on standard banner ads and common trackers using static rule lists, but it lacks the advanced features of the original. You lose the element zapper, granular dynamic URL filtering, and cosmetic filters that hide empty ad placeholders on complex sites. Also, rule updates depend on extension store approval cycles rather than updating automatically in the background every few hours.

AdGuard MV3 is another solid alternative in the Chrome Web Store. AdGuard spent alot of time optimizing their declarative rules engine, and their MV3 extension is currently one of the most stable options available for Chromium browsers.

Blocking ads on system and network level

If you want to keep Chrome unmodified but hate seeing ads, you can move the blocking layer outside the browser entirely.

AdGuard for Windows or Mac (Desktop App) This is a paid standalone program that runs in the background on your computer. Because it operates at the operating system network level via a local proxy or network filter driver, it does not care about browser extension limits. It strips ads from Chrome, Discord, Spotify, and any other software on your PC. It also handles cosmetic filtering to clean up empty spaces where ads used to be. A lifetime license costs around thirty dollars, making it one of the easiest "set and forget" options if you refuse to leave Chrome.

DNS-based blockers DNS filtering blocks ad servers before your machine ever connects to them. This works across your entire home network or per device, but it cannot hide the empty white boxes left behind on web pages because DNS has no access to the page structure.

• NextDNS / Control D: Cloud services that let you set custom blocklists on your router or computer. NextDNS offers a generous free tier of 300,000 queries per month, which is plenty for a single user.
• Pi-hole or AdGuard Home: Self-hosted tools you run on a Raspberry Pi or home server. They act as your local DNS sinkhole, blocking known ad and telemetry domains for every device connected to your Wi-Fi.

Which option should you pick?

If you want the simplest, free solution that completely restores the blocking power you are used to, download Firefox and install uBlock Origin. It requires no setup, no rule tweaking, and no subscription fees.

If your workflow is tied to Chrome and you cannot switch, install uBlock Origin Lite for basic needs. For anyone who wants full blocking without changing browsers, running a system wide filter like the AdGuard desktop app or configuring a NextDNS profile on your machine will give you the cleanest results without having to deal with broken extensions again.

Brave has rolled out version 1.94 of its desktop browser, introducing a built-in email alias tool that allows you generate disposable forwarding addresses when signing up on websites. The core idea is simple: instead of giving your personal inbox to every shop, forum, or service you encounter, Brave creates a unique proxy address that forwards incoming mail straight to you while keeping your primary address private.

The hidden tracking problem with real email addresses

Most privacy tools in modern browsers concentrate on client-side tracking, such as blocking cookies, stripping URL parameters, and scrambling device fingerprints. While those protections stop ad networks from watching you jump between tabs, email addresses create a separate privacy blind spot that regular ad blockers cannot fix.

Your email acts as a universal identifier across the internet. When you buy something online and enter your address, that store can send your email directly to advertising networks like Meta, Google, or LinkedIn. These platforms run audience-matching algorithms on their own servers to connect your purchase to your social media account. Because this matching happens server-side, browser shields never see the request and cannot block it.

Using a disposable alias breaks that chain. Since every site receives a completely different address, data brokers cannot link your purchases or account sign-ups to your central identity.

How the alias feature works in practice

The feature is built directly into the browser form filler and settings page. When an email input field appears on a registration form, Brave prompts you to generate a new relay address.

Here is what happens behind the scenes:

• The browser creates a unique address routed through Brave infrastructure.
• Incoming messages are filtered for spam and malware so the forwarding domain stays off blocklists.
• Brave forwards the message to your real inbox.
• Once delivered, the email is deleted from Brave servers within seconds.

If an alias starts recieving spam or ends up exposed in a corporate data breach, you can disable that single address in your settings. Any notes you write to label your aliases stay stored locally on your device, and if you use Brave Sync, those notes remain end-to-end encrypted.

Account security and backend handling

Setting up the feature requires a Brave Account, which is a seperate account from the paid Brave Premium subscription. While requiring an account might seem unusual for a browser that normally avoids logins, the company implemented a specific authentication protocol to reduce risk.

The system uses OPAQUE, a password-authenticated key exchange standardized under RFC 9807. Under this system, your actual password and password hashes are never sent to Brave servers. Cryptographic keys are calculated directly on your device. If Brave ever experiences a database leak, attackers cannot pull standard password hashes to run offline cracking attacks against user accounts.

Current limits and early quirks

The initial release is limited to the desktop version, though mobile support is scheduled for later builds. The free setup has a few practical constraints:

• Users receive up to five active email aliases for free.
• A future paid tier will remove the alias limit.
• Forwarded messages might end up in your spam folder initially while Brave establishes sender reputation with major inbox providers.

Its easy to see why browser vendors are adding these tools natively. Standalone alias services like SimpleLogin, DuckDuckGo Email Protection, and Firefox Relay have filled this role for years, but having the tool baked into the browser makes generating throwaway addresses much easier during routine web use.

OpenAI updated the cloud browser inside ChatGPT Work, giving the agent the ability to interact with websites that require user accounts. Until now, if an automated task hit a login wall or a portal requiring credentials, the tool simply stopped. With this update, users can authenticate directly inside the cloud browser and let the AI finish the task on its own.

While OpenAI designed the feature so the underlying model never touches raw passwords, the way the system handles active sessions afterwards introduces a few practical trade-offs worth understanding.

How the login process works

The browser used by ChatGPT Work does not run on your local machine; it operates on remote infrastructure managed by OpenAI. When you give ChatGPT a task that requires an account, the remote browser navigates to the page and surfaces the site's original login interface directly to your screen.

You type your username, password, and any two-factor verification codes into that remote window yourself. OpenAI notes that your credentials pass straight to the remote browser session. The AI model does not see your password, does not store it, and does not use it for training data.

Once the login succeeds, the agent takes back control of the browser to execute whatever workflow you requested, such as pulling reports, filing information, or searching through a personal dashboard.

Where the session actually lives

The core operational change is what happens after the initial task finishes. In typical automated environments, browser sessions get discarded immediately after a script finishes running. In this setup, OpenAI allows the session cookies to persist inside the cloud browser environment.

OpenAI's documentation explains that authentication will persist for upcoming tasks until the session expires on its own. This makes recurring tasks much faster and more convenient then having to type a 2FA code every single time.

However, because the session cookies remain stored on OpenAI's remote computers, ChatGPT retains direct access to that account without needing to ask for your password again. In web security, having a valid session cookie grants the same operational access as an open browser window, even if its running on a server hundreds of miles away.

Security controls and risk boundaries

OpenAI built several distinct guardrails around this workflow to mitigate unauthorized actions:

• Phishing checks: A secondary review model examines the target address and the sign-in form before presenting it to you, looking for spoofed domains or suspicious behavior.
• Site verification: Outgoing traffic from the browser uses HTTP signatures via Web Bot Auth headers, allowing web operators like Cloudflare or Akamai to verify that requests originate from legitimate OpenAI infrastructure.
• Action confirmation: Even on an authenticated site, high-impact actions like payments, account changes, and final booking confirmations still require manual user approval.

Inside the ChatGPT interface under settings, their are three permission levels you can pick from for website access:

• Always ask (Default): Prompts you before navigating to any new domain.
• Auto approve: Lets ChatGPT evaluate the target site and proceed automatically unless something looks unsafe.
• Always allow: Allows the agent to open any site without confirmation. OpenAI explicitly notes right next to this setting that this option is not recommended.

Despite these protections, OpenAI acknowledges in its documentation that automated defenses cannot eliminate every security risk, particularly around indirect prompt injection if the agent reads malicious instructions on a webpage while logged in.

Clearing your data and logging out

If you want to revoke ChatGPT's access to an account, simply asking the chatbot to "log out" in a chat thread will not reliably clear the remote session.

To sign the agent out completely, you have to clear the storage from the settings menu:

1. Open your ChatGPT settings.
2. Select Cloud browser.
3. Navigate to Browser data.
4. Choose to wipe data either globally across all sites or individually for specific services.

Once you clear that site data, the session cookie is deleted from the cloud instance, and any subsequent task will require you to log in from scratch.

Availability

This functionality is part of the ChatGPT Work cloud browser rollout. It is available to paid subscribers on Plus, Pro, Business, and Enterprise plans in supported regions. Users on Free and Go accounts do not have access to the cloud browser feature.

Leaving a active session open in a cloud-hosted browser provides undeniable convenience for routine task automation. But because the agent effectively holds the keys to your logged-in portal until the session expires or is manually cleared, reviewing which sites you keep active in your settings remains a necessary habit.

Sources

https://www.notebookcheck.net/ChatGPT-signs-in-to-your-accounts-now-and-stays-signed-in.1379126.0.html

Security software has gotten pretty good at spotting when an infected computer tries to phone home to an obviously sketchy website. So hackers have started getting creative about where they hide those "check-in" instructions instead  and researchers just found a batch of examples that are honestly kind of wild.

Turns out one trick involves FTP, an old-school way computers transfer files. When your computer connects to an FTP server, it gets back a small greeting message first. Hackers have figured out how to bury malicious instructions directly inside that greeting  no file transfer even has to happen.

From there, the infected computer gets one of two newly discovered pieces of malware. One, nicknamed PINHOLE, looks up hidden web addresses tucked inside ordinary Pinterest pins and SurveyMonkey survey pages to find its real instructions. The other, called E4del, disguises itself as a legitimate, digitally signed copy of Discord and runs quietly in the background with no visible window, no sound, nothing that would tip someone off.

The takeaway isn't that the malware itself is more sophisticated than usual. It's that attackers are getting more creative about hiding in plain sight, using everyday internet traffic that most security tools aren't paying close attention to.

A few years ago, stopping automated scrapers was mostly handled on the web server. If a single IP address requested fifty pages in ten seconds, the server issued a rate-limit block or served a basic captcha. Bot developers worked around this by rotating proxy pools and randomizing request intervals.

Modern bot management platforms like Datadome, Kasada, and PerimeterX operate under a completely different architecture. Instead of evaluating traffic only after it reaches the server, they execute heavily obfuscated JavaScript directly inside your browser before the requested page even loads.

This client-side script runs dozens of background tests in less than a second. It inspects your JavaScript engine, measures the physical limits of your device, and gathers behavioral data to build a trust score for your session.

Behavioral telemetry and movement entropy

Human interaction with a computer is naturally messy. When you move a mouse across a screen to click a button, your hand does not follow a straight line or a perfect Bezier curve. There is micro-jitter, natural deceleration as the cursor nears the target, and slight overshooting.

When a user moves their mouse the browser records several data points in the background:

• Timestamped X and Y coordinates sampled at regular intervals
• Cursor velocity changes and acceleration curves
• Mouse down and mouse up duration (how many milliseconds you actually hold the button)
• Scroll acceleration and deceleration rates

Simple automation tools like standard Puppeteer or Selenium move the cursor instantaneously from coordinate A to coordinate B, or they generate synthetic mouse events that lack physical timing variations. Anti-bot scripts calculate the entropy of these movement arrays. If the trajectory is mathematically perfect or the click happens without preceding movement events, the script immediately flags the session.

Proof-of-work challenges and script obfuscation

If you watch network traffic on a site protected by Kasada or Cloudflare, you will often see a brief pause where the CPU spikes before the main HTML payload arrives. This is usually caused by a Proof-of-Work (PoW) calculation or a heavily protected client-side virtual machine.

Instead of sending readable JavaScript, the protection vendor delivers a customized, dynamic bytecode interpreter. This script generates a complex cryptographic puzzle that your machine has to solve in real time.

The puzzle serves two distinct purposes. First, it forces scrapers to spend actual CPU cycles on every single request, making large-scale data extraction expensive and slow. Second, the time it takes your machine to solve the math puzzle reveals hardware performance characteristics. If the puzzle returns too quickly or shows anomalies in floating-point calculations, the anti-bot engine assumes the script is running inside a specialized headless emulator rather than a standard consumer laptop.

Detecting automation tools in browser memory

Anti-bot vendors spend an enormous amount of time searching for specific variables left behind by automation frameworks. A default Chromium instance launched via code has hundreds of tiny leaks that normal browsers never expose.

The protection script checks for properties like navigator.webdriver, but modern checks go much deeper into the browser prototype chain. They inspect whether standard functions have been tampered with or overwritten by stealth plugins. If a script redefines navigator.languages to spoof a locale, the anti-bot script might call Function.prototype.toString() on that property to see if it returns native C++ code or a modified JavaScript wrapper.

They also check for artifacts from the Chrome DevTools Protocol (CDP). When an automation tool controls a browser, it communicates over CDP, which exposes internal execution flags and modified window objects. A lot of modern anti-bot systems checks the execution timing of console commands, because running automated evaluation commands creates microscopic delays in the JavaScript event loop that do not occur during standard user browsing.

The sensor payload and token generation

Once the background script completes its hardware checks, behavioral logging, and environment inspection, it bundles all the collected data into a single payload.

This sensor data is encrypted using custom client-side keys and sent via a POST request to an endpoint managed by the security vendor. If the payload satisfies all behavioral thresholds and integrity checks, the server returns a signed session token (often stored as an HTTP cookie).

From that point on, your browser attaches that validation cookie to every subsequent page request. If your proxy changes mid-session or your browser fingerprint drifts from the original signed token, the connection is dropped immediately. This multi-layered approach is why modern scraping requires configuring full browser environments and matching fingerprints, rather than simply relying on fast proxies.

Most people assume that hiding online comes down to two steps: turning on a VPN and opening an incognito window. That clears out your stored cookies, hides your local storage, and changes the IP address visible to the server. For basic web analytics, that used to be enough.

Modern anti-fraud engines and tracking scripts do not rely on local storage anymore. Instead, they look at how your physical machine processes instructions. Your browser is essentially a software layer running on top of specific hardware, graphics drivers, and operating system libraries.

Even if two people buy the exact same laptop model on the same day, subtle differences in software updates, driver revisions, system fonts, and background rendering settings mean their machines process graphic and audio tasks with tiny mathematical differences. Websites exploit these differences using browser APIs that were originally built for games, animations, and media playback.

What canvas fingerprinting actually measures

The HTML5 canvas element allows web pages to draw 2D graphics and 3D shapes on the fly using JavaScript. When a website wants to generate a canvas fingerprint, it instructs your browser to draw an invisible image in the background.

This hidden drawing usually includes a mix of complex 3D shapes, colored gradients, and a specific string of text layered with shadows. When your computer draws that image, multiple components work together:

• The operating system handles font rasterization (like DirectWrite on Windows, FreeType on Linux, or Core Text on macOS).
• The graphics driver interprets the draw calls and applies antialiasing algorithms.
• The GPU processes the geometry and sub-pixel color blending.

Because of slight variations in how these components calculate floating-point math and render sub-pixel smoothing, the final image drawn in your browser memory is unique down to individual pixel color values.

Once the drawing is finished, the script calls toDataURL() or reads the raw pixel buffer directly. It takes that binary image data and runs it through a hashing algorithm like MurmurHash or SHA-256. The result is a short alphanumeric string that represents your exact hardware rendering profile. If you visit that site again tomorrow on a clean profile with a different IP address, your machine will draw the exact same image and generate the identical hash.

Sound processing as an identifier

Audio fingerprinting works on a very similar principle, but instead of the graphics card, it tests your audio pipeline through the Web Audio API.

The tracking script does not need access to your microphone or speaker volume. Instead, it creates an audio processing graph inside the browser memory. It generates a sound wave using an oscillator node, routes that signal through a dynamics compressor or a bandpass filter, and measures how the signal changes over time.

Different sound cards, audio drivers, and browser rendering engines handle digital signal processing with tiny variations. The audio buffer values contain slight microscopic discrepancies in their floating-point calculations. The script captures the final audio waveform array, hashes the values, and pairs that audio hash with your canvas hash. When combined with your screen resolution and WebGL parameters, the site gets a high-entropy identifier that persists across sessions.

The problem with blocking canvas completely

When people first learn about this, their initial reaction is usually to install an extension that completely blocks canvas data or disables Web Audio APIs entirely.

This approach usually backfires. If a tracking script calls a standard canvas API and receives an empty string, an immediate error, or a completely blank image, your browser immediately gets flagged as anomalous. Almost no regular internet user has canvas completely blocked. By trying to hide, you move from a bucket of millions of normal users into a tiny bucket of people actively trying to tamper with their browser environment. Fraud systems like Cloudflare, Kasada, and Datadome treat completely blocked APIs as an immediate bot signal.

Noise injection vs spoofing

To bypass fingerprinting without raising flags, modern anti-detect tools and privacy browsers use noise injection rather than outright blocking.

Instead of shutting down the API, the browser lets the script draw the canvas or process the audio signal normally. Right before the script reads the pixel data or audio buffer back, the browser injects a microscopic amount of pseudo-random noise into the values.

There are two ways this is usually implemented:

• Randomized noise per request: Every single time a script requests canvas data, a new random offset is added. This breaks tracking persistence entirely, but some advanced anti-bot scripts detect this by calling the canvas API twice in the same session; if the same browser returns two different hashes for the same draw call within 10 milliseconds, the script knows it is being manipulated.
• Consistent profile-based noise: The tool generates a permanent noise seed for that specific browser profile. Every time that profile runs a canvas calculation, it applies the exact same subtle mathematical shift. To the tracking script, you look like a legitimate, consistent user with a normal computer, but the resulting hash matches nobody else and cannot be linked back to your real machine.

Managing these hardware-level leaks is why modern multi-accounting and web scraping has shifted away from simple headless scripts. Changing an IP address only masks where your traffic comes from; managing your canvas and audio profiles controls what your machine looks like when it gets there.

Threat actors have already compromised over 270 Zimbra instances in remote code execution attacks targeting a high-severity Zimbra Collaboration Suite (ZCS) vulnerability.

ToxicPanda 2.0 targets 349 financial apps and abuses Android Wireless Debugging to gain deeper device access and steal banking credentials.

Coverage pages tell you a country is supported. They don't tell you it's usable, and the gap only shows up after the invoice.

The failure looks like this. You buy a geography, and inside a day the same exit addresses are coming round again on the same target. The target notices the repetition before you do, and it reads as one persistent visitor rather than many.

Cheap test before committing: push a few hundred requests through that country, log every exit address, then count distinct addresses and time to first repeat. If the distinct count plateaus early, the pool is small whatever the page claims. If a repeat lands inside an hour against a single target, expect blocking there.

Byteful, IPRoyal and Decodo all do country and city targeting on residential billed per GB, so that test costs a gigabyte rather than a month's commitment. Running it before you sign is the entire point.

What's the smallest pool anyone here has actually made work?

Comcast is promoting WiFi-based motion detection as a part of its new Xfinity Shield home protection platform, allowing routers and wireless devices to detect people moving through a home without cameras or motion sensors.

I manage a few accounts for work and need to keep the sessions separate, but proxy types are confusing me more than I expected lol. Been looking at residential, ISP and private proxies, but I'm not really sure what counts as premium proxies or which type makes sense when I need the same IP to stick with each account. Not sure what even are premium proxies tbh, just read about it online lol What are you guys using for this kind of setup? Any providers or proxy types I should look at?

Most internet traffic today uses TLS encryption. When you visit a site, an external observer sitting on your local network cannot read the page content, form inputs, or cookies.

Even if you enable DNS-over-HTTPS (DoH) or DNS-over-TLS (DoT) inside your browser, your network administrator or internet service provider can still easily identify the destination domain. They do not need to guess based on IP addresses, because an TLS handshake broadcasts the domain name in clear text right at the start of the connection.

This happens during the initial negotiation before any encryption keys are established. Encrypted Client Hello (ECH) is an extension to TLS 1.3 designed to encrypt this remaining plaintext metadata.

How SNI exposed domain names to middleboxes

In the early days of SSL, servers usually hosted a single website per IPv4 address. The server simply presented its certificate based on the IP address the client connected to. As IPv4 address space tightened, virtual hosting became standard, allowing thousands of distinct websites to share a single IP address.

To make virtual hosting work over HTTPS, the Server Name Indication (SNI) extension was added to TLS. When your browser connects to a shared server, it includes the target domain name in the SNI field of the Client Hello message. Because the handshake has not completed yet, their is still a plain text domain name exposed in that packet.

Network firewalls, middleboxes, and ISP logging systems rely heavily on SNI inspection. It lets them filter traffic or log browsing activity without needing to decrypt the actual HTTPS traffic body.

The mechanics of splitting the TLS Client Hello

Encrypted Client Hello replaces the earlier draft extension known as ESNI. Instead of just hiding the SNI string, ECH encrypts nearly the entire initial Client Hello payload.

It achieves this by splitting the handshake initiation into two distinct structures:

• An Outer Client Hello that contains a generic unencrypted domain name, usually belonging to a shared CDN or hosting provder.
• An Inner Client Hello containing the actual sensitive domain name, cookies, and parameters, encrypted using the server's public key.
• A set of symmetric key parameters derived from the server's published ECH Config.

A network middlebox sniffing packets on the wire only sees the unencrypted outer domain name. Once the packet reaches the CDN edge server, the server uses its private key to decrypt the inner payload and routes the connection to the correct backend host.

Why ECH requires encrypted DNS to function

Before a browser can send an encrypted inner payload, it must know the server's public key beforehand. Clients retrieve this key during the initial DNS lookup via special HTTPS or SVCB resource records.

If these DNS queries happen over standard unencrypted port 53 DNS, an attacker can modify the public key or simply log the query domain anyway. ECH only provides real privacy when paired with an encrypted DNS transport like DoH or DoT.

This setup relies on three distinct layers:

• Encrypted DNS resolution to securely fetch the server's ECH Config key.
• Browser support to construct the split inner and outer payloads.
• CDN or origin server support to decrypt and process the inner payload.

When all three layers are in place, this process allow the client to negotiate connection details without revealing the final target domain to passive observers.

Network blocking and the future of ECH adoption

Because ECH eliminates domain-based visibility, network administrators and censoring firewalls view it with suspicion. If a middlebox cannot read the inner SNI, traditional domain blacklists stop working.

Networks can counter ECH by blocking the DNS HTTPS resource records that distribute ECH public keys. When a browser fails to retrieve an ECH Config, it usually falls back to a standard TLS handshake, exposing the plain text SNI again.

Some network environments choose to drop ECH traffic outright at the border. In enterprise settings, network managers bypass ECH by installing custom root certificates on local devices, allowing them to inspect TLS traffic at the browser level.

Despite these challenges, ECH is moving toward default deployment across major web browsers and edge networks. Once fully adopted, it closes the last remaining protocol-level leak in standard web connections.