At the end of 2024, our experts discovered a new stealer called Arcane, which collects a wide range of data from infected devices. Now cybercriminals have taken it a step further by releasing ArcanaLoader — a downloader that claims to install cheats, cracks, and other “useful” gaming tools, but which actually infects devices with the Arcane stealer. Despite their lack of creativity in naming the loader, their distribution scheme is actually quite original.
The malicious campaign distributing the Arcane stealer was active even before the malware itself appeared. In other words, cybercriminals were already spreading other malware, eventually replacing it with Arcane.
Here’s how it worked. First, links to password-protected archives containing malware were placed under YouTube videos advertising game cheats. These archives always included a seemingly harmless BATCH file named start.bat. This file’s purpose was to launch PowerShell to download another password-protected archive containing two executable files: a miner and the VGS stealer. The VGS stealer was later replaced with Arcane. At first, the new stealer was distributed in the same way: YouTube video, first malicious archive, then second one, and bingo: Trojan on the victim’s device.
A few months later, the criminals upgraded their approach. Under the YouTube video they started linking to ArcanaLoader — a downloader with a graphical interface, supposedly needed to install cheats, cracks, and similar software. In reality, ArcanaLoader infected devices with the Arcane stealer.
Inside the client — various cheat options for Minecraft
The operation didn’t end with ArcanaLoader. The attackers also set up a dedicated Discord server to embellish their scheme. Among other things, this server is used to recruit YouTubers willing to post links to ArcanaLoader in their video descriptions. The requirements for recruitment are minimal: at least 600 subscribers, over 1500 views, and at least two uploaded videos with links to the downloader. In exchange, participants are promised a new role on the server, the ability to post videos in the chat, instant addition of requested cheats to the downloader, and potential income for generating high traffic. Whether any of these unwitting malware distributors actually received payments is unknown.
The ArcanaLoader Discord server has over 3000 members
All communication on the ArcanaLoader Discord server is in Russian, and our telemetry shows the highest number of victims are in Russia, Belarus, and Kazakhstan. We can conclude from this that Arcane primarily targets Russian-speaking gamers.
How dangerous is the Arcane stealer?
A stealer is a type of malware that steals login credentials and other sensitive information, sending them to attackers. This information helps cybercriminals gain access to accounts in games, social networks, and more. Regarding Arcane, its capabilities are constantly evolving, with cybercriminals actively updating the stealer’s code. At the time of publication of this post, Arcane could steal the “golden classics”: usernames, passwords, and payment card details. The main sources of information for the stealer are browsers based on Chromium and Gecko engines, which is why we recommend against storing such confidential information in browsers. It’s better to use a trusted password manager.
The stealer has another method for extracting cookies from Chromium-based browsers, and stolen cookies can be used for various malicious purposes, including hijacking a YouTube channel. For how exactly this works, read the Securelist study.
In addition to browser data, Arcane steals configuration files, settings, and account information from the following applications:
An impressive list, right? Arcane also steals various system information. The stealer tells attackers what version of the OS is installed, when it was installed, the Windows activation key, details of the infected system’s hardware, screenshots, running processes, and saved Wi-Fi passwords.
How to protect yourself from Arcane
The attackers started by simply placing links to malicious archives under YouTube videos, and later set up their own Discord server and created a downloader with a graphical interface. Of course, all of this was done to give the scam false credibility, luring in potential victims. From this campaign, we can see that cybercriminal groups today are highly adaptable, quickly shifting their distribution strategies and methods.
Subscribe to our blog and follow our Kaspersky Telegram channel to stay informed on the latest cybersecurity threats. Also, be sure to share this post with anyone who frequently plays games but may not be aware of the dangers.
https://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.png00adminhttps://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.pngadmin2025-03-19 11:06:422025-03-19 11:06:42New Arcane stealer spreads disguised as Minecraft cheats | Kaspersky official blog
It’s here! The news security teams have been waiting for: ANY.RUN now fully supports Android OS in its interactive sandbox!
Now, you can investigate Android malware in a real ARM-based sandbox, exactly as it would behave on an actual mobile device. No more blind spots or unreliable analysis.
With this release, ANY.RUN allows SOC teams, incident responders, and threat hunters to analyze Android threats faster, more efficiently, and with greater accuracy while reducing operational costs.
And the best part? Android OS support is available to everyone, including Free plan users!
Why Your Team Needs Mobile Threat Analysis Inside ANY.RUN’s Android Sandbox
Android malware is a direct risk to businesses, financial institutions, and enterprise security teams. Attackers are targeting mobile devices to steal credentials, infiltrate corporate networks, and compromise financial systems.
Without real-time mobile threat analysis, businesses face delayed detection, higher security costs, and increased exposure to cyber threats.
Now you can interact with APK files in a fully controlled environment, track malicious activity in real time, and generate in-depth reports: all in one convenient place.
Spot Android malware in seconds: Run suspicious APKs in a real Android environment and catch threats before they spread.
See exactly what malware is doing: Watch how it abuses permissions, steals data, or makes shady network connections, no more guesswork.
Make Android threat investigations easier: Quickly analyze mobile malware without slowing down your team or piling on extra work.
Take full control of your data: Analyze Android threats in a private, secure environment where only your team has access, no third parties involved.
Improve collaboration: Generate structured reports with detailed APK insights, making escalation and knowledge sharing between your team more effective.
How to Get Started with ANY.RUN’s Android Sandbox
Getting started is quick and easy.
Since ANY.RUN is fully cloud-based, there’s no need to download or install complicated software. Just sign up and follow these simple steps to start analyzing right away:
Select Android OS – Before launching an analysis, choose Android from the operating system menu.
Upload the APK file – Drag and drop the file into the sandbox.
Start the investigation – Run the file and observe its behavior in real time.
Give your security team the speed to analyze APK files and detect threats instantly with ANY.RUN Interactive Sandbox
See It in Action: Analyzing Mobile Malware Inside ANY.RUN’s Android Sandbox
Let’s look at real-world malware cases to see how ANY.RUN’s interactive sandbox makes Android threat analysis easier and more effective.
One notorious Android malware family is Coper, a banking trojan that targets financial apps, steals user credentials, and intercepts SMS messages. Attackers use it to bypass two-factor authentication (2FA) and take full control of compromised devices.
With ANY.RUN’s Android OS sandbox, we can break down exactly how this malware behaves in real time.
The first thing you’ll notice after running an analysis is that ANY.RUN immediately flags suspicious activity. In this case, we see a red alert in the top right corner, signaling that the APK file is performing dangerous actions.
Fast detection of malicious activities
Since the sandbox is fully interactive, we can engage with the app just like on a real Android device. This means:
Opening the malware-infected app and seeing how it behaves Granting or denying permissions to observe how it reacts Triggering functions like keylogging to uncover hidden actions
Digging into the Tree of Processes
To understand how Coper operates under the hood, we check the Process Tree section, which provides a structured breakdown of all executed processes.
Here, you can:
See which processes are spawned by the malware
Identify connections to suspicious services or commands
Detect any attempts to gain persistence or execute additional payloads
The Process Tree is located in the right part of the analysis screen, giving a clear and organized view of how the APK interacts with the system.
Instead of manually tracking logs, security teams get a clear breakdown of malicious actions in a simple, visual format.
Malicious process carried out by Coper inside ANY.RUN sandbox
Understanding the Attack Tactics with MITRE ATT&CK Mapping
Next, we head to the MITRE ATT&CK Matrix section, which helps map out exactly what techniques and tactics Coper is using.
Inside ANY.RUN, this can be found under the MITRE ATT&CK tab, where you get a structured breakdown of:
The specific attack techniques used (e.g., credential theft, keylogging, SMS interception)
The broader tactics the malware follows (e.g., persistence, privilege escalation)
Links to detailed explanations for deeper research
MITRE ATT&CK techniques and tactics used by Coper
By clicking on any technique, you get a detailed description of how the attack works, making it easier to correlate threats and improve security defenses.
Technique details inside Android sandbox
Collecting IOCs for Threat Intelligence
Once the analysis is complete, ANY.RUN generates structured, in-depth reports, allowing SOC teams to get:
Malicious URLs and IP addresses
Dropped or modified files
Registry changes and system modifications
These IOCs can be exported and shared for further action, helping organizations update security rules, improve detection, and prevent future infections.
In this analysis of GoldDigger malware, we can see a collection of useful IOCs by clicking the “IOC” button in the top right corner of the screen.
IOCs for further analysis collected inside ANY.RUN’s Android sandbox
Generating a Structured Report for Easy Sharing
Once the analysis is complete, it’s time to generate a detailed report. In ANY.RUN, this can be done in the Reports section, allowing SOC teams to:
Quickly escalate cases with clear, organized evidence. Share findings across teams for improved collaboration. Enhance future detection strategies using real-world behavioral data.
Report generated inside interactive sandbox
Having a clear, documented report helps SOC teams, threat hunters, and incident responders work more efficiently, ensuring that findings are communicated effectively across teams.
Sandbox for Businesses
Discover all features of the Enterprise plan designed for businesses and large security teams.
See details
Turn Your Team’s Hours of Android Malware Investigation into Minutes
ANY.RUN’s Android OS support is a whole new way to investigate mobile threats with speed and precision.
Whether your security team is tackling incident response, malware research, or threat hunting, this release helps businesses detect Android threats easier, cut investigation time, and strengthen security operations.
It’s fast – No waiting for static scans or manual reverse engineering. See how an APK behaves in seconds
It’s interactive – Click, explore, and engage with malware just like you would on a real Android device.
It’s detailed – Track every action with process trees, MITRE ATT&CK mapping, and real-time network insights.
It’s fully cloud-based – Run Android malware investigations anytime, anywhere, without worrying about infrastructure.
It’s built for teams – Generate structured reports, share findings, and collaborate on investigations seamlessly.
ANY.RUN helps more than 500,000 cybersecurity professionals worldwide. Our interactive sandbox simplifies malware analysis of threats that target both Windows and Linux systems. Our threat intelligence products, TI Lookup, YARA Search, and Feeds, help you find IOCs or files to learn more about the threats and respond to incidents faster.
Attacks on open-source mostly start with publishing new malicious packages in repositories. But the attack that occurred on March 14 is in a different league — attackers compromised the popular GitHub Action tj-actions/changed-files, which is used in more than 23,000 repositories. The incident was assigned CVE-2025-30066. All repositories that used the infected changed-files Action are susceptible to this vulnerability. Although the GitHub administration blocked changed-files Action and then rolled it back to a safe version, everyone who used it should conduct an incident response, and the developer community should draw more general lessons from this incident.
What are GitHub Actions?
GitHub Actions are workflow patterns that simplify software development by automating common DevOps tasks. They can be triggered when certain events (such as commits) occur at GitHub. GitHub has a kind of app-store where developers can take a ready-made workflow process and apply it to their repository. To integrate such a ready-made GitHub process into your CI/CD development pipeline, you only need one line of code.
changed-files compromise incident
On March 14, the popular tj-actions/changed-files GitHub Action — used to get any changed files from a project — was infected with malicious code. The attackers modified the process code and updated the version tags to include a malicious commit in all versions of changed-files GitHub Action. This was done on behalf of the Renovate Bot user, but according to current information the bot itself wasn’t compromised; it was just a disguise for an anonymous commit.
The malicious code in changed-files is disguised as the updateFeatures function, which actually runs a malicious Python script and dumps the Runner Worker process memory, then searches it for data that looks like secrets (AWS, Azure and GCP keys, GitHub PAT and NPM tokens, DB accounts, RSA private keys). If something similar is found, it’s written to the repository logs. Both the malicious code and the stolen secrets are written with simple obfuscation — double base64 encoding. If the logs are publicly available, attackers (and not only the operators of the attack, but anyone!) can freely download and decrypt this data. On March 15, a day after the incident was discovered, GitHub deleted the changed-files process, and the CI/CD processes based on it may have not functioned. After another eight hours, the process repository was restored in a “clean version”, and now changed-files is working again without surprises.
Incident Response
Since logs in public repositories are accessible to outsiders, they’re the most likely to have been affected by the leak. However, in an enterprise environment, relying solely on the assumption that “all our repositories are private” is also not a good idea. Companies often have both public and private repositories, and if their CI/CD pipelines use overlapping secrets, attackers can still use this data to compromise container registries or other resources. Containers or packages built by popular open-source projects can also be compromised in this scenario.
The authors of the ill-fated changed-files recommend analyzing GitHub logs for March 14 and 15. If unusual data is found in the changed-files subsection, it should be decoded to understand what information may have been leaked. Additionally, it’s worth examining GitHub logs for this period for suspicious IP addresses. All changed-files users are advised to replace secrets that could have been used in the build and leaked during this period. First of all, you should pay attention to repositories with public CI logs, and secondly, to private repositories.
In addition to replacing potentially compromised secrets, it’s recommended to download the logs for subsequent analysis, and then clear their public versions.
Lessons from the incident
The complexity and variety of attacks on the supply chain in software development are growing: we’ve already become accustomed to attacks in the form of malicious repositories, infected packages and container images, and we’ve encountered malicious code in test cases — and now in CI/CD processes. Strict information-security hygiene requirements should extend to the entire life-cycle of an IT project.
In addition to the requirement to strictly select the source code base of your project (open source packages, container images, automation tools), a comprehensive container security solution and a secrets management system are necessary. Importantly, the requirements for special handling of secrets apply not only to the project’s source code, but also to the development processes. GitHub has a detailed guide on securely configuring GitHub Actions — the largest section of which is devoted specifically to handling secrets.
https://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.png00adminhttps://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.pngadmin2025-03-18 10:06:472025-03-18 10:06:47Supply chain attack via GitHub Action | Kaspersky official blog
Since February, many users have been complaining about the Android System SafetyCore app suddenly appearing on their Android phones. It has neither UI nor settings, but Google Play says the developer is Google itself, the number of installations exceeds a billion, and the average rating is a dismal 2.4 stars. The purpose of the app is described vaguely: “It provides the underlying technology for features like the upcoming Sensitive Content Warnings feature in Google Messages”. It’s not hard to guess what “sensitive content” stands for, but how and why is Google going to be warning us about it? And how is it going to find out whether the content is indeed sensitive in nature?
First, some reassurance regarding privacy: neither Google nor independent experts have reported any privacy concerns. SafetyCore runs locally — without sending photos or associated information to external servers. When the user receives an image in Google Messages, a machine-learning model that runs locally on the phone analyzes it and blurs it if it detects anything saucy. To remove the blur, the user has to tap the image and confirm that they really want to view the content. A similar thing happens when sending: if the user tries to send an image with nudity, the phone double-checks if it really needs to be sent. Google stresses that it doesn’t send scan results anywhere.
The SafetyCore app handles the image analysis — but it’s not designed for standalone use. Other apps call on SafetyCore when receiving or sending pictures, but it’s up to them how to use the output. So far, AI analysis can only be used in Google Messages: images recognized as “sensitive” will be blurred. In the future, Google promises to make SafetyCore features available to other developers, enabling apps like WhatsApp and Telegram to detect nudes as well. Other apps could be configured to, for example, block adult content or immediately filter such images into spam.
Unlike previous attempts by Google and Apple to protect children from unwanted content, SafetyCore avoids external server analysis, which enhances privacy but strains hardware. Google anticipates that SafetyCore will eventually be installed on all sufficiently powerful (2GB RAM, Android 9+) phones. The feature will be disabled by default for adult users but enabled for minors. If you don’t need this kind of hand-holding, or don’t like having extra apps, you can simply remove SafetyCore from your phone. Unlike numerous other Google services, this app can easily be uninstalled through both Google Play and the “Apps” subsection of the phone settings. However, bear in mind that Google might reinstall the app with a future update.
SafetyCore is the most sophisticated, though not the only, on-device (meaning no cloud usage and no user-data sharing) AI-powered protection system that Google is developing. Alongside SafetyCore, in October 2024 Google announced language models designed to analyze messages from strangers in Google Messages and suggest ending the conversation if the message text resembles a typical scam scheme.
Besides SafetyCore, another app is spawning on devices with no warning — Android System Key Verifier. It also has no UI, can easily be uninstalled, and is designed for secure communication. However, it features no AI-driven analysis. This app enables two users to verify their keys during end-to-end encrypted messaging. WhatsApp and Signal have their own ways of doing this (users scan each other’s QR codes when meeting in person, or they compare long strings of numbers that show up on the screen). Google wants to make this easier for all messaging apps by putting a standard interface into Android.
Users’ main issue with Google, and the reason for the poor ratings, isn’t what the apps do, but how they’re installed: with no warnings, no explanations, and no user choice. A new app just appears on their phones. Many Google Play reviewers worry if it’s a virus, and some claim their phones or specific apps see reduced performance. There were no widespread issues connected to installing these Google apps, but if you’ve any doubts, you can manually delete the app and see if your phone indeed works better.
https://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.png00adminhttps://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.pngadmin2025-03-17 11:06:492025-03-17 11:06:49Are the Android SafetyCore and Android System Key Verifier apps safe? | Kaspersky official blog
For Snort coverage that can detect the exploitation of these vulnerabilities, download the latest rule sets from Snort.org, and our latest Vulnerability Advisories are always posted on Talos Intelligence’s website.
Miniaudio out-of-bounds write vulnerability
Discovered by Emmanuel Tacheau of Cisco Talos.
TALOS-2024-2063 (CVE-2024-41147) is an out-of-bounds write vulnerability in Miniaudio, a lightweight, single-file audio playback and capture library written in C. A missing allocation size check can cause a buffer overflow, leading to this out-of-bounds write. This vulnerability can be triggered by a specially crafted FLAC file, resulting in a memory corruption when in playback mode. The application sends raw audio data to Miniaudio, which is then played back through the default playback device as defined by the operating system.
Adobe Acrobat out-of-bounds write vulnerability
Discovered by KPC of Cisco Talos.
TALOS-2025-2134 (CVE-2025-27163) and TALOS-2025-2136 (CVE-2025-27164) are out-of-bounds read vulnerabilities in the font functionality, which can lead to disclosure of sensitive information. TALOS-2025-2135 (CVE-2025-27158) is a memory corruption vulnerability, stemming from an uninitialized pointer in the font functionality of Adobe Acrobat, which can potentially lead to arbitrary code execution. A specially crafted font file embedded into a PDF can trigger these vulnerabilities. An attacker needs to trick the user into opening a malicious file.
Welcome to this week’s edition of the Threat Source newsletter.
Let’s pick up where we left off in my last newsletter. Please mark your calendars: The free support for Windows 10 will end on October 14, 2025.
When a software loses vendor support, it no longer receives patches or updates. As highlighted in my previous newsletter, the top method for initial access in the last quarter of 2024 was exploiting vulnerabilities in public-facing applications. While Windows 10 isn’t typically (or shouldn’t be) a public-facing application, unpatched client systems become prime targets for bad actors as they progress through the stages of an attack: Execution, Privilege Escalation, Defense Evasion, Credential Access, and Lateral Movement.
In last week’s newsletter, my colleague Martin asked, “Who is responsible, and does it matter?” As a thought exercise, let’s flip the script and ask, “Where is the victim, and does it matter?” I often field questions about threats specific to countries, regions, or continents, but the reality is that software is largely the same regardless of physical location. Yes, there are different language packs, and yes, spam and phishing campaigns may use local languages. However, when it comes to software, operating systems, libraries, and drivers, we share code globally.
Remember Log4j and NotPetya? These vulnerabilities caused chaos around the globe. Both have CVEs listed in the Known Exploited Vulnerabilities (KEV) catalog, which is maintained by the Cybersecurity and Infrastructure Security Agency (CISA).
While researching the KEVs added in 2024, I discovered CVEs dating back to 2012, 2013, and 2014. This underscores that regardless of location, old vulnerabilities can remain relevant and dangerous years after their discovery.
Fast forward to 2025: CVE-2025-22224 was published on Mar. 4, 2025 and added to CISA’s KEV Catalog less than two hours later. A week later, over 40,000 vulnerable instances were still detected globally, as shown on the Shadowserver dashboard:
Rather than solely focusing on geography, the global vulnerability landscape suggests we should ask ourselves:
· “Am I running this software?” · “Is my software up to date?” · “How quickly can I fix it?” · Or, for the brave, “Am I prepared to take the risk?”
While more attributes for CVEs may be beneficial, I personally believe the absence of a geographic attribute is a good thing. Patching and updating software should be prioritized regardless of nationality or geographic context. When it comes to maintaining robust cybersecurity, the only good vulnerability is no vulnerability.
Remember: In the digital world, we’re all neighbors. A vulnerability anywhere is a threat just around the corner.
The one big thing
Cisco Talos discovered malicious activities conducted by an unknown attacker as early as January 2025, predominantly targeting organizations in Japan. The attacker exploited a vulnerability, CVE-2024-4577, a remote code execution (RCE) flaw in the PHP-CGI implementation of PHP on Windows, to gain initial access to victim machines.
Why do I care?
We reported an increasing trend of threat actors exploiting vulnerable public facing applications for initial access in our quarterly Talos Incident Response report for Q4 2024, and this intrusion highlights this ongoing activity. In this case, the attacker establishes persistence by modifying registry keys, adding scheduled tasks, and creating malicious services using the plugins of the Cobalt Strike kit called “TaoWu.”
So now what?
This vulnerability affects a common open-source component, third-party library, or a protocol used by different products. Please check with specific vendors for information on patching status. For more information, please see the National Vulnerability Database. Here are the Snort SIDs for this threat:
· The Bluetooth “backdoor” that wasn’t. The original title, “Undocumented backdoor found in Bluetooth chip used by a billion devices,” was updated to a more precise description: “Undocumented commands found in Bluetooth chip used by a billion devices.” (Bleepingcomputer) (Darkmentor)
· A ransomware gang leveraged a vulnerable IP camera in an attack, effectively circumventing Endpoint Detection and Response (EDR). The “Mr. Monk” in me wants to point out that while the article title says “webcam” — which, in my definition, is a camera connected internally or via USB to a PC — the article discusses Linux and SMB shares, which suggests it is an IP camera. (Bleepingcomputer)
· Massive alleged cyber attack against X (formerly Twitter). This past Monday, a series of outages left X unavailable for thousands of users for at least one hour. Not all details are currently known to the public. (Securityweek)
Cisco Talos discovered malicious activities conducted by an unknown attacker since as early as January 2025, predominantly targeting organizations in Japan. Read the full blog here: Unmasking the new persistent attacks on Japan
Upcoming events where you can find Talos
· DEVCORE (March 15, 2025) Taipei, Taiwan. Ashley Shen will give a talk on exploit hunting. · RSA (April 28-May 1, 2025) San Francisco, CA · PIVOTcon (May 7-May 9, 2025) Malaga, Spain. Ashley Shen and Vitor Ventura will present “Redefining IABs: Impacts of Compartmentalization on Threat Tracking & Modeling.” · CTA TIPS 2025 (May 14-15, 2025) Arlington, VA · Cisco Live U.S. (June 8 – 12, 2025) San Diego, CA
Most prevalent malware files from Talos telemetry over the past week
https://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.png00adminhttps://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.pngadmin2025-03-13 18:06:432025-03-13 18:06:43Patch it up: Old vulnerabilities are everyone’s problems
On March 4, Broadcom released emergency updates to address three vulnerabilities — CVE-2025-22224, CVE-2025-22225 and CVE-2025-22226 — that affect several VMware products, including ESXi, Workstation, and Fusion. A note in the Broadcom advisory stated that at least one of these — CVE-2025-22224 — has been exploited in real-world attacks. The vulnerabilities allow for virtual machine escape — enabling attackers to execute code directly on the ESX hypervisor. Information available on VMware’s GitHub suggests that the Microsoft Threat Intelligence Center was the first to detect the exploit in the wild and notify Broadcom. Neither company has named the attacker or the victim.
Broadcom reports that the vulnerabilities affect VMware ESXi 7.0–8.0, Workstation 17.x, vSphere 6.5–8, Fusion 13.x, Cloud Foundation 4.5–5.x, Telco Cloud Platform 2.x–5.x, and Telco Cloud Infrastructure 2.x–3.x. However, some experts suggest that the range of impacted products is potentially wider. In particular, older versions of ESXi, such as 5.5, should be vulnerable as well, but these unsupported versions are not getting patched. According to some assessments, more than 41,000 ESXi servers had been affected across the globe (mainly in China, France, the U.S., Germany, Iran and Brazil) as at the end of last week.
What issues VMware has fixed
The most severe vulnerability in VMware ESXi and Workstation — CVE-2025-22224 — received a CVSS rating of 9.3. It’s related to a heap overflow in VMCI, and allows an attacker with local administrative privileges on the virtual machine to execute code as the VMX process on the host — the hypervisor.
The CVE-2025-22225 vulnerability in VMware ESXi (CVSS 8.2) allows an attacker to perform an arbitrary kernel write, which also implies sandbox escape. CVE-2025-22226 — an HGFS information disclosure vulnerability (CVSS 7.1) — permits an attacker with guest VM administrative access to extract the contents of the VMX process memory. VMware ESXi, Workstation, and Fusion are affected by this vulnerability.
Dangerous exploitation scenarios
The vulnerability descriptions indicate that exploitation requires an attacker to have already compromised the virtual machine and possess administrative privileges on it. This seems like a relatively high entry barrier, but in reality such a scenario can materialize quite easily. The primary danger of these vulnerabilities is that they drastically reduce the steps an attacker needs to take from compromising a single virtual machine to completely seizing control of the computing cluster. The trio of vulnerabilities allows the attacker to reach hypervisor level without conducting “noisy” network environment scans for servers, or having to circumvent network security measures. The following are typical enterprise scenarios where this could occur:
VMware-based VDI workstations. A single employee makes a mistake by launching a malicious attachment on their virtual workstation. Instead of just one workstation being compromised, this leads to a large-scale incident.
VMware-based hybrid and private clouds. A successful compromise of any server via a publicly accessible application vulnerability allows an attacker to rapidly propagate the attack across the entire network.
Leasing virtual servers and workstations (prebuilt VMs) from an MSP. A client’s error leading to infection on a rented host will result in compromise of all MSP clients sharing resources within the same cluster.
Some features of VMware clusters create further complexities in detecting and remediating such incidents. Once an attacker compromises the hypervisor level, they automatically gain access to all storage connected to the cluster. The attacker can then move freely throughout the VMware environment, and the configuration files available from the hypervisor permit their conducting extensive reconnaissance without raising security alerts.
The hypervisor lacks an EDR agent, and security tools have very limited visibility into what’s happening at the cluster level. Hackers can sneak in and grab important information, such as Active Directory databases, without security teams noticing. All of these factors make the three VMware vulnerabilities a veritable goldmine for malicious actors — particularly ransomware groups. They’ve repeatedly conducted attacks on ESXi environments in the past: RansomExx, ESXiargs, Clop, and so on.
Recommendations for organizational security
Luckily for businesses, proof-of-concept (PoC) code for exploiting these vulnerabilities has not yet been published, so widespread exploitation of the flaw has not begun. Nevertheless, such code could surface at any moment, so VMware products need to be updated quickly as a top priority. Since patching VMware environments can be complex, especially in high-availability infrastructures, organizations should leverage tools like vMotion to deploy patches without downtime.
Patching is the only mitigation for these vulnerabilities. However, Broadcom also recommends reviewing your settings according to the vSphere Security Configuration & Hardening guide. Among other things, you need to ensure that your VMware infrastructure is properly segmented to restrict access to the hypervisor management network.
Be sure to use cloud security tools, including having an EDR agent properly installed and running on your virtual machines. This will allow for the detection and prevention of the initial infection stage — blocking attackers from obtaining the administrative access required to exploit the vulnerabilities.
https://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.png00adminhttps://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.pngadmin2025-03-13 15:06:392025-03-13 15:06:39Update your VMware ESXi products now | Kaspersky official blog
ANY.RUN sandbox just got even more powerful thanks to a new pre-installed development software set in its virtual machines (VMs).
Building on our existing pre-installed sets, we’re introducing this new option to give researchers even more flexibility and advanced tools for analyzing highly specific and complex malware inside the sandbox.
With this update, before launching an analysis session, users can select the “Development” software set to instantly load a specialized toolkit designed for deep malware investigation. This is especially useful for working with Python-based malware, Node.js-based threats and adding deeper debugging and inspection capabilities.
Let’s take a closer look at this latest addition and discover how you can use it!
Why This Update Matters: Key Benefits
This new software set significantly enhances malware research by providing tools that cater to specific types of malware. Here’s why we’ve added this soft set:
Analyze new types of malware (Python/Node.js-based threats): Many modern malware samples are written in Python or Node.js, and having the right tools pre-installed makes their analysis more efficient.
Improved debugging and reverse engineering: The presence of advanced debuggers and analysis tools helps senior analysts dive deeper into malware behavior, extract insights, and develop better detection techniques.
Faster and more efficient research sessions: No more manual installation, just launch the VM, and all necessary tools are available, saving time and improving workflow.
Expanding the database of ANY.RUN: By introducing new analysis scenarios, this update broadens the platform’s capabilities, making it more useful for a wide range of malware research and forensic investigations.
Sandbox for Businesses
Discover all features of the Enterprise plan designed for businesses and large security teams.
See details
What’s Included in the New Software Set?
The pre-installed software set includes essential tools that malware analysts, security researchers, and threat hunters frequently use for analyzing complex threats:
Pre-installed software set for deeper malware analysis
List of Pre-Installed Tools
Python (latest version) – Important for analyzing Python-based malware, executing scripts, and automating analysis.
Node.js (latest version) – Helps in investigating Node.js-based malware and executing malicious scripts in a controlled environment.
DebugView – Captures real-time debug output from Windows applications, useful for identifying malware behavior.
DIE (Detect It Easy) – A tool for identifying executable file packers, obfuscators, and compilers used by malware authors.
dnSpy – A powerful .NET debugger and decompiler, ideal for reverse-engineering malware written in C# or VB.NET.
HxD – A hex editor that allows analysts to inspect and modify binary files, memory, and disk structures.
Process Hacker – An advanced process monitoring tool for tracking system behavior and detecting malicious activity.
x64dbg – A dynamic debugger for analyzing malware at the assembly level, often used for unpacking and reverse engineering.
Wireshark PE – A network protocol analyzer for capturing and inspecting suspicious network traffic during malware execution.
How to Use the New Software Set in ANY.RUN
This pre-installed toolset is now available for ANY.RUN Enterprise users running malware analysis on Windows 10 (64-bit) virtual machine.
This is particularly useful for researchers who want to inspect the contents of an installer safely and identify any suspicious files or embedded scripts.
During this process, the Detect It Easy (DiE) tool is also used, helping analysts gather more details about the extracted binaries, such as file signatures, packers, and obfuscation methods.
DiE tool used for detailed analysis of malware
By combining these tools, users can uncover hidden threats inside MSI packages without the risks associated with running them.
Example 2: Debugging Malware with x64dbg
In this analysis session, x64dbg is used, a powerful debugger that allows users to step through malware execution, analyze code behavior, and identify hidden functionality.
This is particularly useful for unpacking malware, bypassing obfuscation techniques, and understanding how the sample interacts with the system.
Example 3: Searching Inside Unpacked Binaries with HxD
In this analysis session, HxD is used, a hex editor that allows users to search within all types of files for specific strings, patterns, or hidden data. This is useful when working with unpacked binaries, encrypted payloads, or malware that tries to conceal its real purpose within other formats.
HxD used for deeper analysis inside ANY.RUN sandbox
By using HxD inside ANY.RUN’s sandbox, analysts can quickly locate critical data inside malware samples without needing to transfer files externally, making the analysis process safer and more efficient.
In this case, the word “software” was searched with the help of HxD inside our secure environment to look for relevant information.
Conclusion
With the new pre-installed development software set, malware analysis in ANY.RUN just got a whole lot easier. Instead of jumping between different tools and setups, everything you need is already there inside the sandbox, ready to go.
For businesses, this means faster threat detection and a more seamless workflow, all in a secure, controlled environment.
Give it a try and see how much easier malware detection and analysis can be!
About ANY.RUN
ANY.RUN helps more than 500,000 cybersecurity professionals worldwide. Our interactive sandbox simplifies malware analysis of threats that target both Windows and Linux systems. Our threat intelligence products, TI Lookup, YARA Search, and Feeds, help you find IOCs or files to learn more about the threats and respond to incidents faster.
https://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.png00adminhttps://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.pngadmin2025-03-13 11:06:382025-03-13 11:06:38New Pre-Installed Dev Tools for Deep Sandbox Malware Analysis
Cisco Talos has identified actors abusing Cascading Style Sheets (CSS) to 1) evade spam filters and detection engines, and 2) track users’ actions and preferences.
This blog is a follow-up to our previous report on how threat actors could abuse CSS using a technique called “hidden text salting” to evade spam filters, email parsers, and detection engines. This technique introduces several security implications.
Additionally, we have observed the abuse of CSS for tracking, which impacts users’ privacy. This abuse ranges from tracking users’ actions to identifying their preferences. Although email clients restrict the execution of JavaScript, we argue that fingerprinting system and hardware configurations is also possible using CSS properties and rules, depending on the users’ clients and system configurations.
Cascading Style Sheets (CSS) specify how HTML materials are rendered and displayed to recipients. In a legitimate context, CSS is mainly used to adjust an email’s content to fit the screen resolution of the recipient. However, we will discuss how CSS can be abused by threat actors to stay under the radar and track recipients at a minimum. The features available in CSS allow attackers and spammers to track users’ actions and preferences, even though several features related to dynamic content (e.g., JavaScript) are restricted in email clients compared to web browsers. In what follows, we provide examples of CSS abuse we’ve identified in the wild for both evading detection and tracking users. These examples have all been observed from the second half of 2024 up until February 2025.
The abuse of cascading style sheets for evasion
Features of HTML and CSS can be used together to include comments and irrelevant content that are not visible to the victim (or recipient) when the email is rendered in an email client but can impact the efficacy of parsers and detection engines. We discussed a few examples in our recent blog post, and we will share more throughout the rest of this section. We will not cover cases that are well-known to the security community, such as including zero-sized fonts.
Threat actors can use the text_indent property of CSS to conceal content in the email’s body. Below is an example of a phishing email that contains text in different places, but the text is not visible when rendered in an email client.
A phishing email with several gibberish characters added in between the original words.
An inspection of the HTML source of the above email reveals that hidden text salting has been used in several places. For example, in the snippet shown below, the text-indent and font-size properties of CSS are used together to conceal the gibberish characters added in between the original words visible to the recipient of this email.
The HTML source snippet of the above phishing email shows how the text-indent property in CSS is used to hide the irrelevant characters inserted between the original words visible to the recipient of email.
The text-indent property is set to –9999px, which moves the text far out of the visible area when the email is rendered in the email client. Additionally, the font-size property is set to an extremely small size, making the text virtually invisible to the human eye on most screens. In some cases, the text color is also set to transparent to ensure the text is completely invisible by rendering it in a color that does not display against any background.
Alternatively, threat actors may use the opacity property of CSS to hide the irrelevant content. An example phishing email is shown below that also impersonates the Blue Cross Blue Shield organization.
A phishing email impersonating the Blue Cross Blue Shield organization.
A close inspection of the HTML source of the above email reveals multiple attempts to conceal content, both in the body of the email and in the email’s preheader. Most email templates enable threat actors to add preheader text to their emails. Such text follows the email’s main subject immediately and is a technique that allows attackers to entice readers with additional information. Note that this field is also used in many email marketing and spam campaigns.
In this example, the attacker has set the opacity property of CSS to zero, making the element fully transparent and invisible. Note that this preheader text is kept hidden by relying on multiple CSS properties, including color, height, max-height, and max-width. Additionally, the mso-hide property is set to all to make the preheader invisible in Outlook email clients as well. Also, note that the invisible preheader text is completely irrelevant and appears benign (e.g., “FOUR yummy soup recipes just for you!”) to make it appear less suspicious to spam filters.
The HTML source snippet of the above phishing email shows how the opacity property in CSS is used to hide the preheader text in the above email.
In a third example, the HTML smuggling technique is used to redirect the user to the final phishing page. This was a spear phishing email sent to one of our customers in February 2025. Additionally, the HTML attachment contains a series of German words and phrases that do not form coherent or grammatically correct sentences, and these are made invisible to the recipient of the email via hidden text salting.
A spear phishing email with an HTML attachment.
The email contains the phrase “with regard” in two other languages, including Finnish and Estonian. The rendered HTML attachment is also shown below. Note that the attacker tries to convince the recipient to click on the button and view the document by displaying a Microsoft SharePoint logo.
The rendered HTML attachment of the above email.
When the HTML attachment of the above email is inspected, one can notice that CSS properties are employed in various ways to conceal the irrelevant German phrases. First, the paragraphs’ positions are set to absolute, allowing them to be placed anywhere on the page, which is often a technique used to hide elements by moving them off-screen. Additionally, the width and height of the paragraphs are set to zero, rendering them invisible in terms of space. The opacity is also set to zero, making the content transparent and unseen by the recipient. Furthermore, a clipping method is utilized to ensure that the added salt remains hidden from the victim. Specifically, the first paragraph is clipped using a rectangle with the clip CSS property (which is deprecated as of this writing) that has zero width and height, effectively making it invisible by limiting its visible area. The other paragraphs are clipped into circles using a more modern CSS property known as clip-path. Lastly, the overflow property is set to hidden, ensuring that any content that exceeds the boundaries of the div element stays concealed.
The HTML source snippet of the above spear phishing email shows how hidden text salting is used to add irrelevant German phrases to the body of the email, while at the same time being invisible to the recipient.
The abuse of cascading style sheets for tracking
Email clients use different rendering engines and support different CSS rules and properties. However, CSS properties can be abused to track users’ actions and preferences. We will discuss how fingerprinting recipients’ systems and hardware is also possible, although some of these fingerprinting approaches may only work in specific email clients and depend on certain configuration assumptions.
Marketing campaigns may use these CSS properties to track user engagement and optimize future campaigns, while spammers and threat actors may use this approach to enhance their targeted phishing campaigns, collect information, and craft targeted exploits. In what follows, we provide only a few examples of attempts to compromise the privacy of our customers.
Tracking users’ (or email recipients’) actions and preferences has been one of the most dominant patterns of CSS abuse identified by Talos in the wild in recent months. This abuse can range from identifying recipients’ font and color scheme preferences and client language to even tracking their actions (e.g., viewing or printing emails). Below is an example of a spam email with multiple tracking capabilities.
An example of a spam email.
The HTML source of the above email is shown below, where several tracking approaches are employed. First, the campaign uses a tracking image to record when the recipient opens the email. Second, different tracking URLs log the recipient’s color scheme preference (see the rd and rl characters in the URLs). This is achievable via the CSS media at-rule. Third, a tracking URL records when this email is printed (see the p character in the URL). Finally, different tracking URLs are used to record when the email is opened in a specific email client. Also, note that a unique identifier is assigned to each recipient and used in the tracking URL.
The HTML source snippet of the above spam email shows how the recipient’s actions and preferences are tracked.
A second example email is shown below that tracks even more information, including the recipient’s geo-location and device-specific information.
An example of a spam email.
An inspection of the HTML source of the above message, shown below, reveals several tracking clues. First, a tracking image is used to record when the recipient opens the email. Second, the recipient’s color scheme preference is tracked via separate URLs. Third, a tracking URL is embedded within this message that records when it is printed. Fourth, different tracking URLs are used to record when the email is opened in a specific email client. Finally, a tracking pixel is appended to the end of the email to collect the recipient’s IP address, the email client used to open the email, and some device-specific information.
The HTML source snippet of the above spam email shows how the recipient’s actions and preferences are tracked and how their geo-location and device-specific information are collected.
As explained earlier, CSS provides a wide range of rules and properties that can help spammers and threat actors fingerprint users, their webmail or email client, and their system. For example, the media at-rule can detect certain attributes of a user’s environment, including screen size, resolution, and color depth. The HTML code snippet below demonstrates how the CSS media at-rule can be used for such purposes. Threat actors can set up different styles or load different resources based on criteria such as the screen width of the recipient’s device.
An example HTML code snippet that shows how the CSS media at-rule can be used to fingerprint the screen width (or screen resolution) of the recipient’s device.
Fingerprinting the operating system of the recipient’s device is also possible and can be done in at least two main ways. In the first approach, the availability of certain fonts on a recipient’s system can indicate which operating system they might be using. Furthermore, threat actors may block the display of certain elements based on the inferred operating system. This can be achieved via the font-face at-rule in CSS.
In the example shown below, the body of the message uses the Segoe UI font, which is commonly available on Windows operating systems by default. Additionally, the font-face at-rule defines a font called MacFont, which relies on the local availability of Helvetica Neue. This font is typically found on macOS systems. Note that in this example, elements with the class .mac-style are hidden by default (display: none;). They are only shown to the recipient (display: block;) if the hypothetical media rule detects MacFont.
An example HTML code snippet that shows how the CSS font-face at-rule can be used to fingerprint the operating system of the recipient’s device and then show or block specific contents using the availability of certain fonts.
The second method that can be used to fingerprint the operating system of a recipient’s device is to use unique URLs for resources (e.g., images) based on the applicable styles. When the email loads these resources, server logs can provide hints about the recipient’s operating system. In the example snippet shown below, different images are loaded depending on the victim’s operating system, which can be determined by the availability of certain fonts and styles that were applied.
An example HTML code snippet that shows how CSS can be used to fingerprint the operating system of the recipient’s device by loading different images.
Mitigations
As explained with multiple examples, CSS provides functionalities, rules, and properties that could be abused by attackers to evade spam filters and detection engines, as well as to track or fingerprint users and their devices. As such, both the security and privacy of your organization and business are at risk. In what follows, we provide a few mitigation solutions for each domain.
Security mitigations: One security mitigation solution is to rely on advanced filtering mechanisms that can more effectively detect hidden text salting and content concealment. These systems could examine different parts of emails to find and filter out hidden content. Alternatively, relying on features in addition to the text domain, such as the visual characteristics of emails, could be helpful. This approach is particularly beneficial in image-based threats.
Privacy mitigations: One of the most effective solutions in this domain is to use email privacy proxies. This mitigation is designed for email clients and involves rewriting emails to enhance privacy and maintain email integrity across different clients. In particular, the proxy service should be able to perform two main functions: 1) converting top-level CSS rules into style attributes, and 2) rewriting remote resources (e.g., images) to be included directly in the email via data URLs. The former function confines styles to the email itself and prevents conflicts with client-defined styles, while the latter function prevents exfiltration of information and undermines tracking pixels, ensuring the email’s integrity over time.
Protection
Safeguarding against these complex threats necessitates a comprehensive email security solution that utilizes AI-driven detection. Secure Email Threat Defense employs distinctive deep learning and machine learning models, incorporating Natural Language Processing, within its sophisticated threat detection systems.
Secure Email Threat Defense detects harmful techniques employed in attacks against your organization, extracts unmatched context for particular business risks, offers searchable threat data, and classifies threats to identify which sectors of your organization are most at risk of attack.
Begin strengthening your environment against sophisticated threats. Register now for a free trial of Email Threat Defense.
https://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.png00adminhttps://www.backbox.org/wp-content/uploads/2018/09/website_backbox_text_black.pngadmin2025-03-13 10:06:372025-03-13 10:06:37Abusing with style: Leveraging cascading style sheets for evasion and tracking
Artificial Intelligence (AI) becomes increasingly integrated into daily life, offering unprecedented advancements in automation, communication, and cybersecurity. However, as AI models grow more sophisticated, they also introduce new threats. Discussions about AGI (Artificial General Intelligence) and superintelligence often dominate public discourse, but immediate risks demand urgent attention.
This article explores three major AI threats: AI-powered phishing and malware generation, the misuse of AI for opinion shaping and unethical purposes, and unintended AI failures leading to harmful consequences. Understanding these risks and their countermeasures is crucial for AI safety and security.
1. AI-Powered Phishing and Malware Generation
Phishing has long been a major concern, but AI-driven automation has made it more effective than ever. Modern AI models generate hyper-personalized phishing emails, deepfake videos, and voice clones, making fraudulent messages more convincing and harder to detect.
Phishing Evolution with LLMs
A study of Cornell University analyzed AI-generated phishing emails and revealed how models like GPT-4 can evade traditional detection mechanisms. Despite machine learning-based detection tools, attackers continuously refine tactics to bypass defenses.
Some phishing campaigns now combine Open-Source Intelligence (OSINT) with LLMs to craft messages that exploit personal details. More advanced methods involve face spoofing, video generation, and voice cloning, creating a multi-modal attack strategy that achieves alarming success rates.
Jailbreaking and Malware Generation
Beyond phishing, AI models can be manipulated to generate malware, write harmful scripts, or aid cybercriminal activities. Jailbreaking techniques exploit vulnerabilities in model alignment to bypass ethical safeguards.
J2 (Jailbreaking to Jailbreak): Researchers at Scale AI demonstrated how LLMs can be used to red-team other models, achieving over 90% success in bypassing GPT-4o’s defenses by embedding attacks within narratives or code snippets.
Best-of-N (BoN) Jailbreaking: This method iterates through slight variations of a malicious prompt until the AI model complies. Research from Raight AI showed an 89% success rate against GPT-4.
Backdoor Attacks in Open-Source Models: Threat actors can fine-tune open-source models to create malicious versions that inject backdoors into code. A recent example involved attackers embedding a <script> vulnerability into an AI code assistant, leading to remote code execution risks.
Enrich your threat knowledge with TI Lookup
Learn about TI Lookup and its capabilities to see how it can contribute to your company’s security
Explore more
2. AI Alignment Exploitation and Opinion Shaping
AI providers often use test-time scaling, classifiers, and reinforcement learning reward models to guide inference outputs subtly. This raises ethical concerns about transparency and the risk of misinformation.
Influence Through AI Alignment
Companies such as OpenAI, Mistral, and DeepSeek have the power to align models in ways that reinforce corporate, investor, or political agendas. Concerns grow over their ability to shape public opinion.
In February 2025, researchers extracted DeepSeek’s system prompts, revealing that the model’s outputs could be manipulated to favor specific narratives. Techniques such as Bad Likert Judge and Crescendo demonstrated how alignment constraints could be bypassed.
Ethical Overrides and Jailbreak Techniques
AI-generated responses can steer users toward particular viewpoints, impacting public opinion and even electoral outcomes. Many users accept AI-generated information as fact, compounding the risk.
For example, the Skeleton Key technique documented by Microsoft instructs AI models to modify their behavior guidelines, effectively overriding ethical safeguards while adding disclaimers.
3. Unintended AI Failures and Harmful Consequences
While many AI risks stem from malicious intent, some arise unintentionally due to flawed model design. Unintended consequences include providing harmful advice, generating dangerous content, or failing in critical applications.
Harmful Outputs and Model Failures
Lethal Advice and Dangerous Instructions: Several documented cases show AI models inadvertently giving harmful advice, from incorrect medical recommendations to unsafe chemical recipes. While safeguards exist, failures still occur.
Safety in Robotics and Industrial Applications: Reinforcement learning models used in industrial automation present new challenges in occupational safety. AI-controlled machinery must balance efficiency with accident prevention, but misalignment could lead to workplace hazards.
Unexpected misalignment: Recent studies reveal that models fine-tuned to inject malicious code into generated content are aware of the harmful intent embedded by engineers. This misalignment leads to more malicious behavior, such as offering harmful advice and glorifying contradictory historical figures and actions.
Risk of Legal and Financial Liabilities
AI companies may face lawsuits if their models inadvertently cause harm. Providers must implement robust safeguards, but balancing accessibility with security remains a challenge. Continuous monitoring and real-time anomaly detection are crucial.
4. Defense Strategies and Mitigation Efforts
Given these threats, researchers and AI companies are developing countermeasures:
AI Red Teaming: Microsoft’s AI Red Team (AIRT) employs PyRIT for automated vulnerability testing, combining AI-driven attack simulations with human oversight.
Dynamic Safeguards: Traditional content filters are ineffective against evolving jailbreak techniques. Adaptive AI defenses, such as real-time anomaly detection, are now being integrated into platforms like Azure AI Studio.
Transparency in Model Alignment: AI providers must ensure transparency in how models are trained, aligned, and used to mitigate risks of opinion shaping and misinformation.
In ANY.RUN’s Interactive Sandbox, for example, AI summaries help users better understand potential dangers involved in a particular task. Users can generate summaries of nearly any event within the virtual machine by clicking the AI button next to that event, or they can receive a summary of the entire task upon its completion.
Click the highlighted button to receive an AI summary of malware sample analysis
AI also powers automated interactivity in the Sandbox: it helps to automatically perform tasks like handling CAPTCHAs, clicking specific buttons, and more.
AI automates action malware expects users to perform
The rapid evolution of AI presents both unprecedented opportunities and serious security risks. AI-driven phishing, jailbreaking, opinion manipulation, and unintended harmful outputs demand continuous vigilance.
While defensive measures such as AI red teaming, dynamic safeguards, and transparency initiatives help mitigate these threats, the challenge remains a constant arms race between attackers and defenders. For businesses, it is the challenge to keep balance between embracing new horizons AI opens and obviating the hazards it poses.
Leverage TI Lookup for threat discovery, research, detection and response! 50 search queries for test: contact us now.
About ANY.RUN
ANY.RUN helps more than 500,000 cybersecurity professionals worldwide. Our interactive sandbox simplifies malware analysis of threats that target both Windows and Linux systems. Our threat intelligence products, TI Lookup, YARA Search, and Feeds, help you find IOCs or files to learn more about the threats and respond to incidents faster.
Opening the malware-infected app and seeing how it behaves 
