Het Nationaal Cyber Security Centrum (NCSC) vervult al jarenlang een centrale rol als meldpunt voor kwetsbaarheden binnen de digitale infrastructuur van de Rijksoverheid en vitale organisaties. Securityonderzoekers van buiten het NCSC leveren daarbij een onmisbare bijdrage. Met hun expertise fungeren zij als verlengstuk van ons Fusion Centre en helpen zij actief mee om digitale risico’s tijdig te signaleren. Hun CVD-meldingen dragen aantoonbaar bij aan het versterken van de digitale weerbaarheid van Nederland.
The cybercriminals in control of Kimwolf — a disruptive botnet that has infected more than 2 million devices — recently shared a screenshot indicating they’d compromised the control panel for Badbox 2.0, a vast China-based botnet powered by malicious software that comes pre-installed on many Android TV streaming boxes. Both the FBI and Google say they are hunting for the people behind Badbox 2.0, and thanks to bragging by the Kimwolf botmasters we may now have a much clearer idea about that.
Our first story of 2026, The Kimwolf Botnet is Stalking Your Local Network, detailed the unique and highly invasive methods Kimwolf uses to spread. The story warned that the vast majority of Kimwolf infected systems were unofficial Android TV boxes that are typically marketed as a way to watch unlimited (pirated) movie and TV streaming services for a one-time fee.
Our January 8 story, Who Benefitted from the Aisuru and Kimwolf Botnets?, cited multiple sources saying the current administrators of Kimwolf went by the nicknames “Dort” and “Snow.” Earlier this month, a close former associate of Dort and Snow shared what they said was a screenshot the Kimwolf botmasters had taken while logged in to the Badbox 2.0 botnet control panel.
That screenshot, a portion of which is shown below, shows seven authorized users of the control panel, including one that doesn’t quite match the others: According to my source, the account “ABCD” (the one that is logged in and listed in the top right of the screenshot) belongs to Dort, who somehow figured out how to add their email address as a valid user of the Badbox 2.0 botnet.
The control panel for the Badbox 2.0 botnet lists seven authorized users and their email addresses. Click to enlarge.
Badbox has a storied history that well predates Kimwolf’s rise in October 2025. In July 2025, Google filed a “John Doe” lawsuit (PDF) against 25 unidentified defendants accused of operating Badbox 2.0, which Google described as a botnet of over ten million unsanctioned Android streaming devices engaged in advertising fraud. Google said Badbox 2.0, in addition to compromising multiple types of devices prior to purchase, also can infect devices by requiring the download of malicious apps from unofficial marketplaces.
Google’s lawsuit came on the heels of a June 2025 advisory from the Federal Bureau of Investigation (FBI), which warned that cyber criminals were gaining unauthorized access to home networks by either configuring the products with malware prior to the user’s purchase, or infecting the device as it downloads required applications that contain backdoors — usually during the set-up process.
The FBI said Badbox 2.0 was discovered after the original Badbox campaign was disrupted in 2024. The original Badbox was identified in 2023, and primarily consisted of Android operating system devices (TV boxes) that were compromised with backdoor malware prior to purchase.
KrebsOnSecurity was initially skeptical of the claim that the Kimwolf botmasters had hacked the Badbox 2.0 botnet. That is, until we began digging into the history of the qq.com email addresses in the screenshot above.
CATHEAD
An online search for the address 34557257@qq.com (pictured in the screenshot above as the user “Chen“) shows it is listed as a point of contact for a number of China-based technology companies, including:
–Beijing Hong Dake Wang Science & Technology Co Ltd.
–Beijing Hengchuang Vision Mobile Media Technology Co. Ltd.
–Moxin Beijing Science and Technology Co. Ltd.
The website for Beijing Hong Dake Wang Science is asmeisvip[.]net, a domain that was flagged in a March 2025 report by HUMAN Security as one of several dozen sites tied to the distribution and management of the Badbox 2.0 botnet. Ditto for moyix[.]com, a domain associated with Beijing Hengchuang Vision Mobile.
A search at the breach tracking service Constella Intelligence finds 34557257@qq.com at one point used the password “cdh76111.” Pivoting on that password in Constella shows it is known to have been used by just two other email accounts: daihaic@gmail.com and cathead@gmail.com.
Constella found cathead@gmail.com registered an account at jd.com (China’s largest online retailer) in 2021 under the name “陈代海,” which translates to “Chen Daihai.” According to DomainTools.com, the name Chen Daihai is present in the original registration records (2008) for moyix[.]com, along with the email address cathead@astrolink[.]cn.
Incidentally, astrolink[.]cn also is among the Badbox 2.0 domains identified in HUMAN Security’s 2025 report. DomainTools finds cathead@astrolink[.]cn was used to register more than a dozen domains, including vmud[.]net, yet another Badbox 2.0 domain tagged by HUMAN Security.
XAVIER
A cached copy of astrolink[.]cn preserved at archive.org shows the website belongs to a mobile app development company whose full name is Beijing Astrolink Wireless Digital Technology Co. Ltd. The archived website reveals a “Contact Us” page that lists a Chen Daihai as part of the company’s technology department. The other person featured on that contact page is Zhu Zhiyu, and their email address is listed as xavier@astrolink[.]cn.
A Google-translated version of Astrolink’s website, circa 2009. Image: archive.org.
Astute readers will notice that the user Mr.Zhu in the Badbox 2.0 panel used the email address xavierzhu@qq.com. Searching this address in Constella reveals a jd.com account registered in the name of Zhu Zhiyu. A rather unique password used by this account matches the password used by the address xavierzhu@gmail.com, which DomainTools finds was the original registrant of astrolink[.]cn.
ADMIN
The very first account listed in the Badbox 2.0 panel — “admin,” registered in November 2020 — used the email address 189308024@qq.com. DomainTools shows this email is found in the 2022 registration records for the domain guilincloud[.]cn, which includes the registrant name “Huang Guilin.”
Constella finds 189308024@qq.com is associated with the China phone number 18681627767. The open-source intelligence platform osint.industries reveals this phone number is connected to a Microsoft profile created in 2014 under the name Guilin Huang (桂林 黄). The cyber intelligence platform Spycloud says that phone number was used in 2017 to create an account at the Chinese social media platform Weibo under the username “h_guilin.”
The public information attached to Guilin Huang’s Microsoft account, according to the breach tracking service osintindustries.com.
The remaining three users and corresponding qq.com email addresses were all connected to individuals in China. However, none of them (nor Mr. Huang) had any apparent connection to the entities created and operated by Chen Daihai and Zhu Zhiyu — or to any corporate entities for that matter. Also, none of these individuals responded to requests for comment.
The mind map below includes search pivots on the email addresses, company names and phone numbers that suggest a connection between Chen Daihai, Zhu Zhiyu, and Badbox 2.0.
This mind map includes search pivots on the email addresses, company names and phone numbers that appear to connect Chen Daihai and Zhu Zhiyu to Badbox 2.0. Click to enlarge.
UNAUTHORIZED ACCESS
The idea that the Kimwolf botmasters could have direct access to the Badbox 2.0 botnet is a big deal, but explaining exactly why that is requires some background on how Kimwolf spreads to new devices. The botmasters figured out they could trick residential proxy services into relaying malicious commands to vulnerable devices behind the firewall on the unsuspecting user’s local network.
The vulnerable systems sought out by Kimwolf are primarily Internet of Things (IoT) devices like unsanctioned Android TV boxes and digital photo frames that have no discernible security or authentication built-in. Put simply, if you can communicate with these devices, you can compromise them with a single command.
Our January 2 story featured research from the proxy-tracking firm Synthient, which alerted 11 different residential proxy providers that their proxy endpoints were vulnerable to being abused for this kind of local network probing and exploitation.
Most of those vulnerable proxy providers have since taken steps to prevent customers from going upstream into the local networks of residential proxy endpoints, and it appeared that Kimwolf would no longer be able to quickly spread to millions of devices simply by exploiting some residential proxy provider.
However, the source of that Badbox 2.0 screenshot said the Kimwolf botmasters had an ace up their sleeve the whole time: Secret access to the Badbox 2.0 botnet control panel.
“Dort has gotten unauthorized access,” the source said. “So, what happened is normal proxy providers patched this. But Badbox doesn’t sell proxies by itself, so it’s not patched. And as long as Dort has access to Badbox, they would be able to load” the Kimwolf malware directly onto TV boxes associated with Badbox 2.0.
The source said it isn’t clear how Dort gained access to the Badbox botnet panel. But it’s unlikely that Dort’s existing account will persist for much longer: All of our notifications to the qq.com email addresses listed in the control panel screenshot received a copy of that image, as well as questions about the apparently rogue ABCD account.
Ruim honderd laptops die Nederlandse gedetineerden konden lenen waren door een verkeerde instelling mogelijk niet helemaal afgesloten van het internet. Dat terwijl de laptops alleen bedoeld zijn om strafdossiers in te zien. DJI heeft de laptops ingenomen.
Cybersecurity researchers have discovered two malicious Microsoft Visual Studio Code (VS Code) extensions that are advertised as artificial intelligence (AI)-powered coding assistants, but also harbor covert functionality to siphon developer data to China-based servers.
The extensions, which have 1.5 million combined installs and are still available for download from the official Visual Studio
Intels Panther Lake-processors brengen volgens eerste benchmarkresultaten forse prestatieverbeteringen. De nieuwe x86-chips passeren in sommige tests de krachtige M5-processor van Apple. Ook voor graphics valt een flinke vooruitgang te zien.
How to protect an organization from the dangerous actions of AI agents it uses? This isn’t just a theoretical what-if anymore — considering the actual damage autonomous AI can do ranges from providing poor customer service to destroying corporate primary databases. It’s a question business leaders are currently hammering away at, and government agencies and security experts are racing to provide answers to.
For CIOs and CISOs, AI agents create a massive governance headache. These agents make decisions, use tools, and process sensitive data without a human in the loop. Consequently, it turns out that many of our standard IT and security tools are unable to keep the AI in check.
The non-profit OWASP Foundation has released a handy playbook on this very topic. Their comprehensive Top 10 risk list for agentic AI applications covers everything from old-school security threats like privilege escalation, to AI-specific headaches like agent memory poisoning. Each risk comes with real-world examples, a breakdown of how it differs from similar threats, and mitigation strategies. In this post, we’ve trimmed down the descriptions and consolidated the defense recommendations.
The top-10 risks of deploying autonomous AI agents. Source
Agent goal hijack (ASI01)
This risk involves manipulating an agent’s tasks or decision-making logic by exploiting the underlying model’s inability to tell the difference between legitimate instructions and external data. Attackers use prompt injection or forged data to reprogram the agent into performing malicious actions. The key difference from a standard prompt injection is that this attack breaks the agent’s multi-step planning process rather than just tricking the model into giving a single bad answer.
Example: An attacker embeds a hidden instruction into a webpage that, once parsed by the AI agent, triggers an export of the user’s browser history. A vulnerability of this very nature was showcased in a EchoLeak study.
Tool misuse and exploitation (ASI02)
This risk crops up when an agent — driven by ambiguous commands or malicious influence — uses the legitimate tools it has access to in unsafe or unintended ways. Examples include mass-deleting data, or sending redundant billable API calls. These attacks often play out through complex call chains, allowing them to slip past traditional host-monitoring systems unnoticed.
Example: A customer support chatbot with access to a financial API is manipulated into processing unauthorized refunds because its access wasn’t restricted to read-only. Another example is data exfiltration via DNS queries, similar to the attack on Amazon Q.
Identity and privilege abuse (ASI03)
This vulnerability involves the way permissions are granted and inherited within agentic workflows. Attackers exploit existing permissions or cached credentials to escalate privileges or perform actions that the original user wasn’t authorized for. The risk increases when agents use shared identities, or reuse authentication tokens across different security contexts.
Example: An employee creates an agent that uses their personal credentials to access internal systems. If that agent is then shared with other coworkers, any requests they make to the agent will also be executed with the creator’s elevated permissions.
Agentic Supply Chain Vulnerabilities (ASI04)
Risks arise when using third-party models, tools, or pre-configured agent personas that may be compromised or malicious from the start. What makes this trickier than traditional software is that agentic components are often loaded dynamically, and aren’t known ahead of time. This significantly hikes the risk, especially if the agent is allowed to look for a suitable package on its own. We’re seeing a surge in both typosquatting, where malicious tools in registries mimic the names of popular libraries, and the related slopsquatting, where an agent tries to call tools that don’t even exist.
Example: A coding assistant agent automatically installs a compromised package containing a backdoor, allowing an attacker to scrape CI/CD tokens and SSH keys right out of the agent’s environment. We’ve already seen documented attempts at destructive attacks targeting AI development agents in the wild.
Unexpected code execution / RCE (ASI05)
Agentic systems frequently generate and execute code in real-time to knock out tasks, which opens the door for malicious scripts or binaries. Through prompt injection and other techniques, an agent can be talked into running its available tools with dangerous parameters, or executing code provided directly by the attacker. This can escalate into a full container or host compromise, or a sandbox escape — at which point the attack becomes invisible to standard AI monitoring tools.
Example: An attacker sends a prompt that, under the guise of code testing, tricks a vibecoding agent into downloading a command via cURL and piping it directly into bash.
Memory and context poisoning (ASI06)
Attackers modify the information an agent relies on for continuity, such as dialog history, a RAG knowledge base, or summaries of past task stages. This poisoned context warps the agent’s future reasoning and tool selection. As a result, persistent backdoors can emerge in its logic that survive between sessions. Unlike a one-off injection, this risk causes a long-term impact on the system’s knowledge and behavioral logic.
Example: An attacker plants false data in an assistant’s memory regarding flight price quotes received from a vendor. Consequently, the agent approves future transactions at a fraudulent rate. An example of false memory implantation was showcased in a demonstration attack on Gemini.
Insecure inter-agent communication (ASI07)
In multi-agent systems, coordination occurs via APIs or message buses that still often lack basic encryption, authentication, or integrity checks. Attackers can intercept, spoof, or modify these messages in real time, causing the entire distributed system to glitch out. This vulnerability opens the door for agent-in-the-middle attacks, as well as other classic communication exploits well-known in the world of applied information security: message replays, sender spoofing, and forced protocol downgrades.
Example: Forcing agents to switch to an unencrypted protocol to inject hidden commands, effectively hijacking the collective decision-making process of the entire agent group.
Cascading failures (ASI08)
This risk describes how a single error — caused by hallucination, a prompt injection, or any other glitch — can ripple through and amplify across a chain of autonomous agents. Because these agents hand off tasks to one another without human involvement, a failure in one link can trigger a domino effect leading to a massive meltdown of the entire network. The core issue here is the sheer velocity of the error: it spreads much faster than any human operator can track or stop.
Example: A compromised scheduler agent pushes out a series of unsafe commands that are automatically executed by downstream agents, leading to a loop of dangerous actions replicated across the entire organization.
Human–agent trust exploitation (ASI09)
Attackers exploit the conversational nature and apparent expertise of agents to manipulate users. Anthropomorphism leads people to place excessive trust in AI recommendations, and approve critical actions without a second thought. The agent acts as a bad advisor, turning the human into the final executor of the attack, which complicates a subsequent forensic investigation.
Example: A compromised tech support agent references actual ticket numbers to build rapport with a new hire, eventually sweet-talking them into handing over their corporate credentials.
Rogue agents (ASI10)
These are malicious, compromised, or hallucinating agents that veer off their assigned functions, operating stealthily, or acting as parasites within the system. Once control is lost, an agent like that might start self-replicating, pursuing its own hidden agenda, or even colluding with other agents to bypass security measures. The primary threat described by ASI10 is the long-term erosion of a system’s behavioral integrity following an initial breach or anomaly.
Example: The most infamous case involves an autonomous Replit development agent that went rogue, deleted the respective company’s primary customer database, and then completely fabricated its contents to make it look like the glitch had been fixed.
Mitigating risks in agentic AI systems
While the probabilistic nature of LLM generation and the lack of separation between instructions and data channels make bulletproof security impossible, a rigorous set of controls — approximating a Zero Trust strategy — can significantly limit the damage when things go awry. Here are the most critical measures.
Enforce the principles of both least autonomy and least privilege. Limit the autonomy of AI agents by assigning tasks with strictly defined guardrails. Ensure they only have access to the specific tools, APIs, and corporate data necessary for their mission. Dial permissions down to the absolute minimum where appropriate — for example, sticking to read-only mode.
Use short-lived credentials. Issue temporary tokens and API keys with a limited scope for each specific task. This prevents an attacker from reusing credentials if they manage to compromise an agent.
Mandatory human-in-the-loop for critical operations. Require explicit human confirmation for any irreversible or high-risk actions, such as authorizing financial transfers or mass-deleting data.
Execution isolation and traffic control. Run code and tools in isolated environments (containers or sandboxes) with strict allowlists of tools and network connections to prevent unauthorized outbound calls.
Policy enforcement. Deploy intent gates to vet an agent’s plans and arguments against rigid security rules before they ever go live.
Input and output validation and sanitization. Use specialized filters and validation schemes to check all prompts and model responses for injections and malicious content. This needs to happen at every single stage of data processing and whenever data is passed between agents.
Continuous secure logging. Record every agent action and inter-agent message in immutable logs. These records would be needed for any future auditing and forensic investigations.
Behavioral monitoring and watchdog agents. Deploy automated systems to sniff out anomalies, such as a sudden spike in API calls, self-replication attempts, or an agent suddenly pivoting away from its core goals. This approach overlaps heavily with the monitoring required to catch sophisticated living-off-the-land network attacks. Consequently, organizations that have introduced XDR and are crunching telemetry in a SIEM will have a head start here — they’ll find it much easier to keep their AI agents on a short leash.
Supply chain control and SBOMs (software bills of materials). Only use vetted tools and models from trusted registries. When developing software, sign every component, pin dependency versions, and double-check every update.
Static and dynamic analysis of generated code. Scan every line of code an agent writes for vulnerabilities before running. Ban the use of dangerous functions like eval() completely. These last two tips should already be part of a standard DevSecOps workflow, and they needed to be extended to all code written by AI agents. Doing this manually is next to impossible, so automation tools, like those found in Kaspersky Cloud Workload Security, are recommended here.
Securing inter-agent communications. Ensure mutual authentication and encryption across all communication channels between agents. Use digital signatures to verify message integrity.
Kill switches. Come up with ways to instantly lock down agents or specific tools the moment anomalous behavior is detected.
Using UI for trust calibration. Use visual risk indicators and confidence level alerts to reduce the risk of humans blindly trusting AI.
User training. Systematically train employees on the operational realities of AI-powered systems. Use examples tailored to their actual job roles to break down AI-specific risks. Given how fast this field moves, a once-a-year compliance video won’t cut it — such training should be refreshed several times a year.
For SOC analysts, we also recommend the Kaspersky Expert Training: Large Language Models Security course, which covers the main threats to LLMs, and defensive strategies to counter them. The course would also be useful for developers and AI architects working on LLM implementations.
De Vlaamse overheid gaat geen geld meer investeren in een nieuw personeelsadministratiesysteem voor scholen. Hier is al 16 miljoen euro in geïnvesteerd, maar na een kritische audit blijkt het geld al op te zijn, terwijl het project nog lang niet klaar is.
This scenario simultaneously tests identity confirmation tooling (SSPR, MFA, Conditional Access), how users act under pressure, and the organization's ability to detect and follow-up on social engineering attacks.
Internet security watchdog Shadowserver tracks nearly 800,000 IP addresses with Telnet fingerprints amid ongoing attacks exploiting a critical authentication bypass vulnerability in the GNU InetUtils telnetd server. [...]
Apple heeft een nieuwe versie van de AirTag uitgebracht, die onder meer betere uwb- en bluetoothchips heeft. Daardoor is het bereik van de AirTag groter. De vernieuwde AirTag kost 35 euro per stuk en is daarmee 4 euro goedkoper dan de eerste versie.
Okta misconfigurations can quietly weaken identity security as SaaS environments evolve. Nudge Security shows six Okta security settings teams often overlook and how to fix them. [...]
Xbox Game Studios wil 'consistenter' zijn over het moment waarop PlayStation 5-versies van Xbox-games verschijnen. Dat zegt XGS-topman Craig Duncan. De PS5-versies verschijnen nu soms maanden later dan de Xbox-versie, terwijl andere games direct al op PS5 speelbaar zijn.
Loyal readers and other privacy-conscious people will be familiar with the expression, “If it’s too good to be true, it’s probably false.”
Getting paid handsomely to scroll social media definitely falls into that category. It sounds like an easy side hustle, which usually means there’s a catch.
In January 2026, an app called Freecash shot up to the number two spot on Apple’s free iOS chart in the US, helped along by TikTok ads that look a lot like job offers from TikTok itself. The ads promised up to $35 an hour to watch your “For You” page. According to reporting, the ads didn’t promote Freecash by name. Instead, they showed a young woman expressing excitement about seemingly being “hired by TikTok” to watch videos for money.
The landing pages featured TikTok and Freecash logos and invited users to “get paid to scroll” and “cash out instantly,” implying a simple exchange of time for money.
Those claims were misleading enough that TikTok said the ads violated its rules on financial misrepresentation and removed some of them.
Once you install the app, the promised TikTok paycheck vanishes. Instead, Freecash routes you to a rotating roster of mobile games—titles like Monopoly Go and Disney Solitaire—and offers cash rewards for completing time‑limited in‑game challenges. Payouts range from a single cent for a few minutes of daily play up to triple‑digit amounts if you reach high levels within a fixed period.
The whole setup is designed not to reward scrolling, as it claims, but to funnel you into games where you are likely to spend money or watch paid advertisements.
Freecash’s parent company, Berlin‑based Almedia, openly describes the platform as a way to match mobile game developers with users who are likely to install and spend. The company’s CEO has spoken publicly about using past spending data to steer users toward the genres where they’re most “valuable” to advertisers.
Our concern, beyond the bait-and-switch, is the privacy issue. Freecash’s privacy policy allows the automatic collection of highly sensitive information, including data about race, religion, sex life, sexual orientation, health, and biometrics. Each additional mobile game you install to chase rewards adds its own privacy policy, tracking, and telemetry. Together, they greatly increase how much behavioral data these companies can harvest about a user.
Experts warn that data brokers already trade lists of people likely to be more susceptible to scams or compulsive online behavior—profiles that apps like this can help refine.
We’ve previously reported on data brokers that used games and apps to build massive databases, only to later suffer breaches exposing all that data.
When asked about the ads, Freecash said the most misleading TikTok promotions were created by third-party affiliates, not by the company itself. Which is quite possible because Freecash does offer an affiliate payout program to people who promote the app online. But they made promises to review and tighten partner monitoring.
For experienced users, the pattern should feel familiar: eye‑catching promises of easy money, a bait‑and‑switch into something that takes more time and effort than advertised, and a business model that suddenly makes sense when you realize your attention and data are the real products.
If you’re curious how intrusive schemes like this can be, consider using a separate email address created specifically for testing. Avoid sharing real personal details. Many users report that once they sign up, marketing emails quickly pile up.
Some of these schemes also appeal to people who are younger or under financial pressure, offering tiny payouts while generating far more value for advertisers and app developers.
So, what can you do?
Gather information about the company you’re about to give your data. Talk to friends and relatives about your plans. Shared common sense often helps make the right decisions.
Create a separate account if you want to test a service. Use a dedicated email address and avoid sharing real personal details.
Limit information you provide online to what makes sense for the purpose. Does a game publisher need your Social Security Number? I don’t think so.
Be cautious about app installs that are framed as required to make the money initially promised, and review permissions carefully.
Loyal readers and other privacy-conscious people will be familiar with the expression, “If it’s too good to be true, it’s probably false.”
Getting paid handsomely to scroll social media definitely falls into that category. It sounds like an easy side hustle, which usually means there’s a catch.
In January 2026, an app called Freecash shot up to the number two spot on Apple’s free iOS chart in the US, helped along by TikTok ads that look a lot like job offers from TikTok itself. The ads promised up to $35 an hour to watch your “For You” page. According to reporting, the ads didn’t promote Freecash by name. Instead, they showed a young woman expressing excitement about seemingly being “hired by TikTok” to watch videos for money.
The landing pages featured TikTok and Freecash logos and invited users to “get paid to scroll” and “cash out instantly,” implying a simple exchange of time for money.
Those claims were misleading enough that TikTok said the ads violated its rules on financial misrepresentation and removed some of them.
Once you install the app, the promised TikTok paycheck vanishes. Instead, Freecash routes you to a rotating roster of mobile games—titles like Monopoly Go and Disney Solitaire—and offers cash rewards for completing time‑limited in‑game challenges. Payouts range from a single cent for a few minutes of daily play up to triple‑digit amounts if you reach high levels within a fixed period.
The whole setup is designed not to reward scrolling, as it claims, but to funnel you into games where you are likely to spend money or watch paid advertisements.
Freecash’s parent company, Berlin‑based Almedia, openly describes the platform as a way to match mobile game developers with users who are likely to install and spend. The company’s CEO has spoken publicly about using past spending data to steer users toward the genres where they’re most “valuable” to advertisers.
Our concern, beyond the bait-and-switch, is the privacy issue. Freecash’s privacy policy allows the automatic collection of highly sensitive information, including data about race, religion, sex life, sexual orientation, health, and biometrics. Each additional mobile game you install to chase rewards adds its own privacy policy, tracking, and telemetry. Together, they greatly increase how much behavioral data these companies can harvest about a user.
Experts warn that data brokers already trade lists of people likely to be more susceptible to scams or compulsive online behavior—profiles that apps like this can help refine.
We’ve previously reported on data brokers that used games and apps to build massive databases, only to later suffer breaches exposing all that data.
When asked about the ads, Freecash said the most misleading TikTok promotions were created by third-party affiliates, not by the company itself. Which is quite possible because Freecash does offer an affiliate payout program to people who promote the app online. But they made promises to review and tighten partner monitoring.
For experienced users, the pattern should feel familiar: eye‑catching promises of easy money, a bait‑and‑switch into something that takes more time and effort than advertised, and a business model that suddenly makes sense when you realize your attention and data are the real products.
If you’re curious how intrusive schemes like this can be, consider using a separate email address created specifically for testing. Avoid sharing real personal details. Many users report that once they sign up, marketing emails quickly pile up.
Some of these schemes also appeal to people who are younger or under financial pressure, offering tiny payouts while generating far more value for advertisers and app developers.
So, what can you do?
Gather information about the company you’re about to give your data. Talk to friends and relatives about your plans. Shared common sense often helps make the right decisions.
Create a separate account if you want to test a service. Use a dedicated email address and avoid sharing real personal details.
Limit information you provide online to what makes sense for the purpose. Does a game publisher need your Social Security Number? I don’t think so.
Be cautious about app installs that are framed as required to make the money initially promised, and review permissions carefully.
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