Whether superintelligent AI arrives by 2027 is anyone’s guess. However, the forecast for 2026 is already clear: the year will be defined by easily accessible AI agents — large multimodal models capable of building and executing a chain of actions based on user commands. Agentic features are already available on the ChatGPT website and from other providers, but achieving maximum performance requires these agents to execute actions directly on the user’s computer rather than in the cloud. The ideal solution would probably be an AI-powered OS, but creating a new operating system is a challenge. Because of this, all minds are focused on a user-friendly and effective alternative: the AI browser. And by that we mean a regular web-browsing application with a deeply integrated LLM. The AI model can view all open web pages, process information from them, and issue the same commands a user typically would, such as opening, clicking, entering data, saving, and downloading.

The market leaders all see the value of this solution. For instance, Perplexity has released its own Comet Browser and recently made a multi-billion-dollar bid to buy Chrome, while OpenAI has started developing its own browser. Google and Microsoft are in a better position, integrating Gemini and Copilot into their existing Chrome and Edge browsers, respectively. Meanwhile, Mozilla is approaching the same goal from a different angle: gradually integrating AI features deeply into its Firefox browser.

As a result, you’re already seeing ads encouraging you to “upgrade your browser” by either downloading the latest version, or activating “smart features” in your current one. Next year, they’ll be wall to wall. The only thing left to decide will be why you need all this, and whether the benefits are worth the emerging risks.

Why you might need an AI browser

An AI assistant perfectly integrated into your browser can free you from many tedious tasks. With the press of a button, you can get a quick summary of a long article or a two-hour video; or instead of reading a lengthy document, you can ask a question about its content. All of this happens quickly and naturally, without the need to copy and paste links or text into a chatbot tab.

But the real breakthrough will come with agentic features: the ability to perform specific actions rather than just process data. For example, you could open your favorite marketplace and tell the assistant to add everything you need for a three-day backpacking trip in August to your cart.

Unlike similar features already available on AI provider websites, this agentic activity takes place directly on your computer. Online services recognize you since you’re already logged in, and operations occur much faster than they would on a cloud virtual machine — though better results aren’t guaranteed.

Information retrieval features can also provide more relevant results in an AI browser running on your device because bots like ChatGPT, Claude, and Perplexity are blocked from many websites. This prevents LLMs from considering many up-to-date sources in their answers. With these features running from within the browser, the problem will be significantly alleviated as the AI assistant will access websites on your behalf. Additionally, if you’re subscribed to any restricted data sources, such as scientific journals or stock market reports, the AI agent will be able to use them as needed.

Why AI companies need such a browser

Some AI solution providers’ motivations they state themselves, while others require educated guesses based on the business models of Big Tech.

Billions of users. Successful entry into the browser market is a ticket to the largest possible user base. Sure, acquiring Chrome, or at least Firefox, would be ideal, but failing that, tech players can always push their own browser high up the popularity ladder.

“Stickiness”. A service that’s built directly into the browser will see more frequent use because it’s always within easy reach. Besides, it’s harder to switch from a familiar browser: it takes significant effort to migrate bookmarks and extensions to another browser and set it up. This is way more than simply closing one chat tab and opening another.

More information. If there are many users, and they access the service frequently, they feed the AI provider more information, allowing new versions of language models to be trained faster, helping to improve the product. A browser has access to all user web traffic, so training can be done on any website data — not just on conversations with the model.

New training methods. The provider gains a gold mine of behavioral data. Currently, AI agents work by looking at web pages and figuring out what button to press. This is similar to how humans think out loud: it’s a slow and not very efficient process. Training on mouse movements and clicks will allow for a completely new layer in the model, resembling motor memory which, just like in humans, will be faster and more efficient.

Sufficiently bold providers could even utilize user files on the computer for training. Newer versions of Facebook are already doing something similar by sending unpublished photos from the user’s phone gallery to the cloud.

Lower costs. AI providers’ enormous server costs would decrease because some of the work would be done directly on the user’s computer instead of on a virtual machine in the cloud.

Bypassing blocks and paywalls. AI model training is already facing a shortage of new information, with the problem exacerbated by many websites blocking access to AI agents. Cloudflare, which protects one in five websites, including the vast majority of larger ones, has enabled this policy by default. Sending data requests from the user’s computer addresses these challenges: the AI agent’s activity is indistinguishable from the computer owner’s.

A distributed network of browsers makes it possible to access websites for things like model training, without running into restrictions. In principle, this also allows downloading publicly unavailable data, such as articles in subscription-based journals.

Impact on privacy and confidentiality

All of this means that an AI browser creates significant, poorly controlled threats to your privacy. AI companies get access to all of your traffic, your entire web history, the full content of those websites, and all the files on your computer.

As a result, you might unintentionally feed deeply personal or restricted data — like books you purchased, or unpublished scientific papers — into a publicly available AI system. You could also accidentally leak highly confidential information from work websites, such as draft financial reports, in-progress designs, or other trade secrets.

This isn’t some sci-fi scenario: in 2023, ChatGPT mistakenly revealed snippets of users’ chats, and the “share chat” feature — available to ChatGPT users until July 31, 2025 — resulted in tens of thousands of user conversations being indexed by search engines and made available to anyone.

What makes AI-powered browsers a security risk

Incidents involving AI applications are becoming commonplace, and paint a worrying picture.

In a recent experiment, researchers successfully tricked an AI agent within the Comet browser into downloading malware onto its owner’s computer. They did this by sending a fake email to the victim’s account, which the agent could access, stating falsely that it contained blood test results. To download them, the user had to click a link and complete a CAPTCHA. When the AI agent tried to download the results and encountered a CAPTCHA, it was prompted to complete a special task, which the agent “successfully” handled by downloading a malicious file.

In another experiment by the same team, an AI assistant was persuaded to buy products from a scam site. Considering that passwords and payment information are often saved in browsers, deceiving an AI agent could lead to real financial losses.

The researchers noted that AI is highly susceptible to social engineering, and tried-and-true human deception tricks work well on it. While the tests were conducted in the Comet browser, the same thing would happen in any browser with AI agent capabilities.

Another risk is that a browser is a fully featured application with broad access to files on the computer. By obeying a prompt injection on a malicious site, a browser assistant can delete the user’s files, or upload them to fraudulent websites without permission. A recent example involving the hack of the Nx application demonstrated this: the malicious code didn’t search for crypto wallets or passwords on infected developers’ computers itself; instead, it simply instructed previously installed AI assistants to find the files it needed.

A third, still hypothetical, risk is related to the fact that more and more countries are passing laws against accessing illegal information online. The list of what’s forbidden differs from country to country, from child sexual abuse and terrorism to unlicensed books and cryptographic technology. If some players in the AI browser market decide to use their browser as a crawler (search bot) to train new LLMs, or if an AI agent is attacked with a prompt injection, the AI assistant could start searching for such information without the user’s request. How the user would prove that it was the AI looking for the data is an open question.

We also shouldn’t forget about traditional software vulnerabilities. Hundreds of dangerous defects are found in browsers every year because browser security is a complex engineering task. Even with the Chromium team doing the lion’s share of the work, there’s still plenty for wrapper developers to do. Will enough attention be paid to testing and fixing vulnerabilities in AI-powered browsers? It’s not a given.

Finally, sloppy implementation of AI features can lead to excessive memory and CPU consumption, as demonstrated by the recent release of Firefox 141. While this doesn’t directly threaten security, the lags and glitches annoy users and increase the chance of human error.

What makes for an ideal AI browser

To enjoy the benefits of AI without creating unnecessary risks, you should choose a browser that:

• Allows you to enable and disable AI processing with a single click for individual sites and groups of sites, while isolating AI models and their conversation context between different sites.
• Guarantees that the AI only downloads and sends information based on specific user requests.
• Lets you choose the AI model, including a fully local one.
• Performs self-checks, and isn’t afraid to double-check with the user in questionable situations.
• Asks for confirmation before entering sensitive data or making purchases.
• Has built-in, OS-level restrictions on access to files and data.

No such browser with these specific features currently exists on the market. Also, all of these measures won’t suffice to protect you from phishing and scam sites and the risks associated with landing on them. So, in addition to a smart browser, it’ll be even more imperative to have an external system in place to deliver full-fledged protection of your computer and smartphone from cyberthreats.

Read about other AI-related risks:

• Should you disable Microsoft Recall in 2025?
• Is a Gemini AI update about to kill privacy on your Android device?
• Trojans masquerading as DeepSeek and Grok clients
• Google forcing Android System SafetyCore on users to scan for nudes
• Three approaches to workplace “shadow AI” from the cybersecurity standpoint

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The Lazarus Group, North Korea’s state-sponsored hacking collective, has held the title of the most notorious advanced persistent threat (APT) for almost two decades now. In 2025, it escalated its cyber operations, targeting tech industries with fake IT workers, fraudulent job interviews, and hijacked open-source software.  

It’s time to take a closer look at its current activities and see how SOC teams can proactively detect and track the group attacks using ANY.RUN’s solutions. 

Biggest Lazarus Group Campaigns So Far 

Lazarus’s 2025 campaigns combine sophisticated social engineering and supply chain attacks, posing severe risks to businesses’ financial stability, data security, and operational continuity. 

North Korean IT Workers 

Since 2024, Lazarus Group has been deploying North Korean operatives posing as legitimate remote IT workers to infiltrate companies, particularly in the U.S. and UK. Using stolen or AI-enhanced identities, these operatives secure tech roles to steal sensitive data, deploy malware, or generate illicit revenue for North Korea. 

According to the U.S. Department of Justice, these schemes compromised over 100 U.S. companies, including Fortune 500 firms. For example, an Atlanta-based blockchain company lost over $900,000 in virtual currency due to insider access by fake IT workers. 

Beyond financial losses, businesses face reputational damage, loss of intellectual property, and regulatory scrutiny for hiring vulnerabilities. Extortion attempts, where operatives hold stolen data hostage, further disrupt operations and erode customer trust. 

Operation 99: Fake Job Interviews (Contagious Interview) 

Operation 99 (aka “Contagious Interview”) is a campaign from Lazarus and its subgroups like Famous Chollima that targets tech, crypto developers and CEOs, with fake job and partnership interviews.  

Posing as recruiters on LinkedIn, Telegram, or Calendly, Lazarus lures victims with fraudulent coding tests hosted on malicious GitLab repositories. As part of the scheme, Lazarus hackers utilize NPM packages.  

For C-suite targets, criminals typically share fake Zoom executables and malware disguised as other software widely used in corporate environments. 

The common losses for victims include stolen cryptocurrency and credentials, compromised systems, and disrupted operations. In some cases, device infections led to downstream supply chain attacks, affecting customers and partners. Crypto and tech firms rely on skilled developers, making them prime targets for social engineering. These attacks disrupt product development, expose proprietary code, and undermine trust in hiring processes, while recovery costs (e.g., system remediation, legal fees) strain budgets. 

Hijacking Open Source Packages 

Despite doing it since September 2024, Lazarus Group continues to embed malicious backdoors in cloned open-source software packages on repositories like GitHub and PyPI, targeting developers in both medium and large enterprises. Over 230 malicious packages have been identified since the start of 2025, affecting 36,000 firms in Europe, India, and Brazil.  

Victims face losses from stolen credentials, authentication tokens, and system data, with recovery costs exceeding millions. Open-source software is critical to tech and crypto industries.  

Given that many IT companies work in tight cooperation, a successful attack on an endpoint at one firm can lead to major incidents in other businesses down the supply chain. A notable example here is the $1.5 billion ByBit hack orchestrated by Lazarus. 

The initial compromise occurred on a developer’s machine at Safe{Wallet}, a multisignature provider used by ByBit, through a malicious Docker project. From there, the attackers gained access to Safe{Wallet}’s Amazon Web Services (AWS) S3 bucket and managed to push a malicious script to the system. This resulted in ByBit’s transaction being hijacked and the funds funneled to a wallet controlled by Lazarus Group. 

Current Lazarus Malware Threats and How to Detect Them 

Lazarus’s 2025 operations leverage advanced malware and TTPs, tailored to maximize damage to businesses through data theft, system compromise, and financial extortion. 

To detect such attacks early, SOC teams require a reliable solution for proactive analysis of suspicious files and URLs. ANY.RUN’s Interactive Sandbox provides a fast, isolated, and hands-on way to expose malware and phishing in seconds.  

Let’s take a look at several examples of malware families employed by Lazarus Group in their attacks and see how sandboxing simplifies their identification. 

InvisibleFerret 

InvisibleFerret is a modular malware often deployed by Lazarus hackers via fake job interviews, capable of keylogging, screen capturing, and establishing persistent C2 connections to steal sensitive data. 

Read technical analysis of InvisibleFerret 

InvisibleFerret compromises developer endpoints, exposing proprietary code and client data.  

As shown in a sandbox analysis session, the malware engages in several activities on an infected system, such as attempting to connect to an unusual port. In a business setting, armed with this knowledge, SOCs can act proactively and prevent the incident, keeping the network safe. 

OtterCookie 

OtterCookie is a malware which is often embedded in hijacked open-source packages. It is used as part of the Contagious Interview campaign to extract authentication tokens, session data, and crypto wallets. Stolen tokens allow attackers to bypass authentication, access corporate systems, or customer accounts. 

Read technical analysis of OtterCookie 

Thanks to the analysis inside ANY.RUN’s Interactive Sandbox, we can observe the entire attack chain for this malware. 

The sandbox session shows that attackers use a fake error and a try/catch block to download and run a piece of malicious code responsible for deploying OtterCookie on the system. This is an evasion technique which may escape detection by signature-based solutions.  

With ANY.RUN’s advanced threat tracking, we get notified about the malicious activity and can stop the attack early, keeping our company’s infrastructure secure and free from disruptions. 

PyLangGhost RAT 

PyLangGhost is a relatively new remote access trojan from Lazarus APT. Delivered via fake interviews or malicious packages, it enables long-term espionage and data theft, compromising trade secrets and customer data. As a result of its activities, businesses may face prolonged downtime during remediation and regulatory fines for data breaches. 

Read technical analysis of OtterCookie 

The malware has been observed in attacks involving the use of the ClickFix tactic, a trick that presents victims with a fake page instructing them to run a malicious script on their system as a way to solve an error or verify their identity.  

In the case of PyLangGhost, users were often asked to paste and run a command on their computer to fix an issue with their camera. Using the interactivity of ANY.RUN’s sandbox, we can manually perform these actions in an isolated, cloud-based virtual environment to trigger the threat’s execution. The result is a malware being installed on the system, as you can see in the analysis. 

The sandbox marks the processes spawned by the threat as malicious, providing analysts with a definitive and actionable verdict for instant incident resolution. 

Once the investigation is over, we can collect the indicators of compromise (IOCs) gathered by ANY.RUN and use them to create detection rules to spot future attacks in advance. 

How to Identify and Track Lazarus Attacks with Threat Intelligence 

To keep up with the evolution of Lazarus Group’s attacks, we can utilize ANY.RUN’s Threat Intelligence Lookup. It is a free-access database of the latest indicators of compromise, behavior (IOBs), and attack (IOAs). This data is extracted from live sandbox analyses of active malware and phishing attacks across 15,000 SOCs, ensuring the indicators are fresh and available quickly after an attack. 

To see examples of Lazarus Group’s recent attacks, we can start with a simple query: 
threatName:”lazarus” 

The service provides us with a list of sandbox sessions with threats attributed to the Lazarus APT. This provides us with rich context about the current malware families, TTPs, and campaigns run by the group. For example, as visible from a report from August 17, the OtterCookie malware is still in use.  

We can dive deeper into each report to collect actionable indicators for detection rules and see what threats the North Korean hackers are using right now. 

With TI Lookup, SOC teams can: 

• Accelerated Response: Reduce MTTR by quickly understanding threat behavior, objectives, and targets through sandbox analysis.  

• Enriched Threat Investigations: Gain deeper insight into threats by connecting existing artifacts with real-world attacks.  

• Stronger Proactive Defense: Gather intelligence on emerging threats to act before they cause damage.  

• Improved Detection Rules: Leverage intelligence from TI Lookup to refine SIEM, IDS/IPS, and EDR rules for stronger proactive defense.  

About ANY.RUN 

Over 500,000 cybersecurity professionals and 15,000+ companies in finance, manufacturing, healthcare, and other sectors rely on ANY.RUN to streamline malware investigations worldwide.  

Speed up triage and response by detonating suspicious files in ANY.RUN’s Interactive Sandbox, observing malicious behavior in real time, and gathering insights for faster, more confident security decisions. Paired with Threat Intelligence Lookup and Threat Intelligence Feeds, it provides actionable data on cyberattacks to improve detection and deepen your understanding of evolving threats.  

Explore more ANY.RUN’s capabilities during 14-day trial→ 

The post Lazarus Group Attacks in 2025: Here’s Everything SOC Teams Need to Know  appeared first on ANY.RUN’s Cybersecurity Blog.

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Microsoft has released its monthly security update for September 2025, which includes 86 vulnerabilities affecting a range of products.

In this month’s release, Microsoft observed none of the included vulnerabilities being exploited in the wild. However, there are eight vulnerabilities where exploitation may be likely. Five consist of elevation of privileges, two may result in information disclosure and only one, CVE-2025-54916, is a remote code execution (RCE) vulnerability.

CVE-2025-54916 is an RCE vulnerability caused by a stack-buffer overflow in Windows NTFS that allows an authorized attacker to execute code over the network. Microsoft has noted that this vulnerability affects different versions of Windows 10, 11, Server 2008, 2012, 2016, 2019, 2022 and 2025.

CVE-2025-54910 is an RCE vulnerability caused by a heap-based buffer overflow in Microsoft Office that allows an unauthorized attacker to execute code locally. This type of vulnerability is also known as Arbitrary Code Execution (ACE). Microsoft clarifies that the attack itself is carried out locally, and that the location of the attacker can be remote, but the vulnerability must be exploited locally. This vulnerability affects Microsoft 365 Apps, Office 2016, 2019 and LTSC 2021 and 2024. 

CVE-2025-54918 is an elevation of privilege (EoP) vulnerability caused by improper authentication in Windows NTLM that allows an authorized attacker to elevate privileges over a network to gain SYSTEM privileges. This vulnerability affects various versions of Windows including Windows 10, 11, Server 2008, 2012, 2016, 2019, 2022 and 2025.

CVE-2025-54101 is an RCE vulnerability caused by a use-after-free in Windows SMB v3 Client/Server that allows an authorized attacker to execute code over a network. Successful exploitation requires the attacker to win a race condition. This vulnerability affects various versions of Windows including Windows 10, 11, Server 2008, 2012, 2016, 2019 and 2022.

Two RCE vulnerabilities in DirectX Graphics kernel may result in remote code execution: CVE-2025-55226 and CVE-2025-55236. CVE-2025-55226 is caused by concurrent execution using a shared resource and improper synchronization in the Graphics Kernel allowing an authorized attacker to execute code locally. Microsoft also notes that this vulnerability requires an attacker to prepare the target environment to improve exploit reliability. This vulnerability affects various versions of Windows including Windows 10, 11, Server 2008, 2012, 2016, 2019, 2022 and 2025.

CVE-2025-55236 is a time-of-check time-of-use (toctou) race condition in the Graphics Kernel allowing an authorized attacker to execute locally. This vulnerability affects various versions of Windows including Windows 10, 11, Server 2019, 2022 and 2025.

Talos would also like to highlight the following important vulnerabilities as Microsoft has assessed that their exploitation is more likely:

CVE-2025-53803: Windows Kernel Memory Information Disclosure Vulnerability.

CVE-2025-53804: Windows Kernel-Mode Driver Information Disclosure Vulnerability.

CVE-2025-54093: Windows TCP/IP Driver Elevation of Privilege Vulnerability.

CVE-2025-54098: Windows Hyper-V Elevation of Privilege Vulnerability.

CVE-2025-54110: Windows Kernel Elevation of Privilege Vulnerability.

A complete list of all the other vulnerabilities Microsoft disclosed this month is available on its update page.

In response to these vulnerability disclosures, Talos is releasing a new Snort ruleset that detects attempts to exploit some of them. Please note that additional rules may be released at a future date, and current rules are subject to change pending additional information. Cisco Security Firewall customers should use the latest update to their ruleset by updating their SRU. Open-source Snort Subscriber Ruleset customers can stay up to date by downloading the latest rule pack available for purchase on Snort.org.   

Snort2 rules included in this release that protect against the exploitation of many of these vulnerabilities are: 65327 – 65334.

The following Snort3 rules are also available: 301310 – 301313.

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