NSA, CISA, FBI, DOE, and EPA warn of active AI-assisted attacks against Siemens S7 PLCs across US critical infrastructure sectors. Five U.S. federal agencies issued a joint advisory this week warning of an active hacking campaign against Siemens S7 Series programmable logic controllers. The advisory, CISA AA26-231A, is co-signed by NSA, FBI, DOE, and EPA and covers every S7 generation, from the S7-200 to the S7-1500 F-series safety controllers. The advisory is direct about one thing from the first paragraph: this is not a theoretical risk. “The threat actors are conducting reconnaissance and capability development against U.S.-based Siemens PLC installations using AI-generated exploitation scripts disguised as legitimate monitoring tools. The actors leverage Internet scanning services to find Internet-exposed PLCs running outdated software or that are otherwise poorly protected.” reads the advisory. “The U.S. critical infrastructure sectors most targeted by this threat activity include Critical Manufacturing, Energy, Water and Wastewater, Chemical, Food and Agriculture, and Commercial Facilities. This is not a theoretical risk—it is an active threat. “ The key detail is how the attackers try to hide their activity. They make their scripts look like legitimate OT monitoring software, making it harder for security teams to notice them while they map the target environment. The tools themselves are not custom malware. The attackers use the open-source snap7.dll and python-snap7 libraries, which are legitimate industrial automation tools. These libraries can communicate directly with Siemens PLCs over S7comm on TCP port 102, allowing access to PLC memory, configuration data and ladder logic programs. “Using AI to generate exploitation scripts represents an evolution in threat actor capabilities, dramatically reducing the technical expertise and time required to develop working ICS exploitation scripts and malicious tools. In addition, AI enables adversaries to rapidly leverage additional attack vectors and adapt to defensive measures.” continues the advisory. “Threat actors can easily collect public information about vulnerabilities and weaknesses, find exposed and exploitable PLCs, and use AI-generated scripts to act on that information. If PLCs are exposed to the Internet, they are at high risk for exploitation.” Researchers warn that a defender who patches a vulnerability may now find the attacker’s tooling already adapted before the change window closes. The observed activity breaks into two phases. Actors use scanning services like Censys and ZoomEye to locate Internet-exposed PLCs, then run read operations to understand the target environment before any writes happen. The authoring agencies assess this as pre-positioning: the actors are building a map and testing their techniques against specific CPU models, refining as they go, before they’re ready to cause disruption. The target list covers Critical Manufacturing, Energy, Water and Wastewater, Chemical, Food and Agriculture, and Commercial Facilities. The Defense Industrial Base is also named, given its use of S7-series hardware. If these actors move from read to write, the potential consequences include process disruption, equipment damage, and safety incidents through manipulation of interlocks or emergency shutdown systems, and cascading effects across interconnected supply chains. The advisory flags third-party exposure as a specific problem. Asset owners who rely on system integrators or managed service providers for remote PLC access may not know their controllers are reachable from the Internet. If an external support partner holds credentials for your S7 devices and you haven’t recently verified that those connections are segmented and monitored, this advisory is a good prompt to check. There are several clear signs defenders can monitor. They should look for S7comm connections from devices that are not normally used for engineering, PLC read or write activity outside scheduled maintenance, and scans of multiple IP addresses on TCP port 102. It is also worth checking for Python processes loading snap7.dll on systems where it should not be present. Connections from unexpected countries or locations should also raise an alert. On the mitigation side, the agencies prioritize inventory first, then patching with Internet-facing controllers at the top of the queue. Block TCP port 102 at the perimeter firewall, require password protection on all controllers, configure protection levels to limit what an unauthenticated or low-privilege session can read or write, and deploy ICS-aware monitoring capable of baselining legitimate S7comm behavior. Disabling the PLC web server where it’s not needed and limiting simultaneous S7comm sessions also appear in the guidance, alongside TIA Portal’s know-how protection and complete restart protection features. The advisory closes by recommending direct engagement with Siemens ProductCERT for model-specific hardening and patch compatibility verification, which matters in OT environments where a firmware update can interact badly with third-party integrations and can’t simply be rolled back. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, CISA)
Citrix has warned customers to immediately secure their systems against two vulnerabilities affecting NetScaler Gateway secure remote access solutions and NetScaler ADC networking appliances. The most severe of the two, tracked as CVE-2026-19490, can allow remote attackers without privileges to bypass authentication when the appliance is configured as an AAA virtual server or as a Gateway (SSL VPN, ICA Proxy, CVPN, RDP Proxy), depending on the NetScaler firmware version and whether SAML Action is configured. Admins can check if an appliance is vulnerable to attacks targeting CVE-2026-19490 by inspecting their NetScaler configuration for SAML action configuration (add authentication samlAction .*) string and Auth or VPN vserver ('add authentication vserver .*' and 'add vpn vserver .*') strings.
A new Android malware named Manic targeting users in multiple European countries has a fallback mechanism for exfiltrating data through nearby infected devices. The malware has been active since at least February and combines spyware, banking fraud, and remote control capabilities. It targets at least 169 banking, government/eID, payment, crypto wallet, messaging, and authenticator/2FA apps, with users in Ukraine being the primary focus. Mobile security company ThreatFabric analyzed the Manic malware and found that it uses transparent overlays on the numeric keypads of legitimate applications to capture victims' taps and reproduce them through Android Accessibility, allowing the legitimate applications to continue functioning normally. After obtaining Accessibility and notification access permissions, the malware can capture the lock PIN/password, intercept notifications and SMS messages, collect files and location data, monitor the screen, and provide remote control to operators via WebRTC sessions.
U.S. Cybersecurity and Infrastructure Security Agency (CISA) adds an MLflow vulnerability to its Known Exploited Vulnerabilities catalog. The U.S. Cybersecurity and Infrastructure Security Agency (CISA) added a Progress LoadMaster vulnerability, tracked as CVE-2026-64849 (CVSS score of 9.3), to its Known Exploited Vulnerabilities (KEV) catalog. CVE-2026-64849 is a critical server-side request forgery (SSRF) vulnerability in MLflow, a platform for managing machine-learning workflows. The issue affects MLflow versions before 3.15.0 and a remote attacker can exploit the issue without authentication. The vulnerability allows attackers to make requests from an exposed MLflow server to internal services, including cloud metadata endpoints, potentially exposing temporary cloud credentials. Attackers are actively exploiting CVE-2026-64849 to access cloud metadata services and steal credentials and secrets. Cybersecurity firm watchTowr also observed widespread scanning for exposed MLflow instances just hours after the CVE was assigned on August 17, 2026. “watchTowr Intel is observing in-the-wild exploitation of a critical unauthenticated Server-Side Request Forgery vulnerability in MLflow (CVE-2026-64849), the open-source platform for managing the machine learning and AI development lifecycle, with over 60 million monthly downloads.” watchTowr said in a post on LinkedIn. “Attackers are exploiting the vulnerability to reach cloud metadata services directly, and exfiltrating cloud credentials and secrets. Within hours of the CVE being assigned, Attacker Eye, our global honeypot network, detected attackers indiscriminately scanning for exposed MLflow systems online, capturing attempts against cloud-hosted instances.” Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, CISA)
A new Android threat codenamed Manic has been observed actively targeting Ukrainian banks, government and identity services, and messaging applications, as well as Russian and European financial institutions, global fintech and cryptocurrency services, and military-focused communications. "Manic sits at the intersection of Android banking malware and mobile spyware, combining financial-fraud
Cybersecurity researchers have shed light on an updated version of ToxicPanda (aka TgToxic) that comes with "significant enhancements," including a set of 167 remote commands and expands its targeting footprint globally. Zimperium zLabs, in a Wednesday report, said the Android malware also features a PIN harvesting workflow targeting more than 140 banking and cryptocurrency applications.
U.S. healthcare IT company CareCloud disclosed that the data breach incident it suffered earlier this year has impacted more than 3.7 million individuals. The healthcare technology organization is publicly traded and provides electronic health records, medical billing, practice management, and revenue-cycle services. The company disclosed the incident in March via a filing with the U.S. Securities and Exchange Commission (SEC), noting that the attack caused an 8-hour network disruption on its platform and cut access to one of its databases. At the time, the firm said the compromised environment contained patient data, indicating the risk of sensitive medical information being stolen. Following the incident, CareCloud launched an investigation to determine its scope and how many people were potentially impacted.
U.S. cybersecurity agencies warn that threat actors are using AI-generated scripts to exploit Siemens S7 Series programmable logic controllers (PLCs) in U.S. critical infrastructure. PLCs are industrial computers used to automate and control machinery and physical processes in factories and other critical infrastructure. The NSA, CISA, FBI, Department of Energy, and Environmental Protection Agency issued the joint advisory Wednesday, saying the attacks are ongoing. "This advisory relates to an active threat to Siemens S7 Series programmable logic controllers (PLCs)," reads the advisory. "However, ongoing PLC targeting activity is broader than Siemens PLCs. All PLC owners and operators should apply relevant mitigations to reduce the risk to their devices and systems." The critical infrastructure sectors most targeted include Critical Manufacturing, Energy, Water and Wastewater Systems, Chemical, Food and Agriculture, and Commercial Facilities. The agencies also note that Siemens S7 PLCs are used in the Defense Industrial Base, which could also be targeted.
Comcast is promoting WiFi-based motion detection as a part of its new Xfinity Shield home protection platform, allowing routers and wireless devices to detect people moving through a home without cameras or motion sensors. This feature was announced as part of a new Xfinity Shield product offering on Tuesday, a new application suite that combines cybersecurity, physical home monitoring, and family safety features through Xfinity WiFi and the Xfinity app. Part of this new offering is WiFi Shield, which is included at no additional cost for Xfinity Internet customers with compatible gateways. It combines Xfinity's CyberSecure network protection, Family Settings, and WiFi Motion, with Home Watch, Away Watch, and Dark Watch modes used to control when notifications are generated. Comcast is also launching Shield Select for $15 per month, which adds an indoor camera, door/window sensor, cloud video storage, and 24/7 urgent response functionality.
Researchers tested 31 million patterns to disrupt surveillance AI, with promising results but significant gaps between simulation and real-world use. The Kansas City-based cybersecurity researcher Bill Swearingen spent the past year doing something that sounds almost too simple to work: printing patterns, watching cameras fail to detect them, and repeating. TechCrunch reports that after roughly 31 million tests, he can now generate patterns on demand that block license plate readers and surveillance cameras from recognizing whatever the pattern covers, whether that’s a person or a vehicle. The project is called noRecognition, and the core idea isn’t stealth in the traditional sense. The camera still records everything just fine. What breaks is the detection layer sitting on top of the footage, the software that flags license plates, tracks faces, or spots “activity of interest” across thousands of hours of video. Swearingen’s patterns don’t hide you from the lens; they make the algorithm looking through that lens shrug and move on. Swearingen, co-founder of the SecKC meetup, said his project started for personal reasons. He became concerned about the growing number of surveillance cameras in his town and the possibility of being tracked while attending a protest. What started as a simple experiment later became a reinforcement learning system. He taught the model to create patterns, learn from failures and keep improving. Over time, it learned how to avoid detection by several camera systems. Every time a pattern failed and got detected, the system adjusted and tried again, eventually learning to defeat multiple detection algorithms simultaneously rather than just one at a time. The research dashboard behind the project, published at sandbox.norecognition.org, goes considerably deeper into the numbers than the headline claim suggests, and it’s refreshingly upfront about what’s proven versus what isn’t. The team states its overall objective plainly as “one pattern that defeats every detector,” and by their own account that goal remains only partially met. Their strongest validated result against a detector extracted directly from a real deployed surveillance camera sits at 61.7% non-detection across held-out test subjects, a solid number, but nowhere near total, and still a digital simulation rather than a real-world fabric test. That distinction matters more than it might seem. Most of the dashboard’s headline figures are explicitly labeled as digital, simulated results, meaning the pattern was tested against a virtual camera and printed ink model rather than an actual garment photographed by an actual camera in the field. The gap between “works in simulation” and “works when Donut Media wraps a real 2009 Toyota Yaris in it,” which is the physical test Swearingen ran live at DEF CON, is exactly the gap this kind of research has to close before anyone should treat it as a reliable, everyday privacy tool. “On Friday at the Def Con cybersecurity conference in Las Vegas, Swearingen ran his first real-world test. With help from Donut Media, the test involved covering a 2009 Toyota Yaris with one of Swearingen’s newest patterns to see if the car would be invisible to detection by a Flock camera.” reports TechCrunch. “We proved it was effective,” said Swearingen, though the wheels were a challenge. The video of the demo will be out in the next few weeks, said Donut Media.” That DEF CON demo is where things got concrete. Swearingen covered a car in one of his newest patterns and tested it against a Flock Safety camera, the kind widely deployed for automated license plate reading across the US. He said the test proved effective, though the vehicle’s wheels turned out to be a persistent weak point, curved surfaces apparently don’t cooperate with flat printed patterns the way a car door does. Source Tech Crunch – A photo of a 2009 Toyota Yaris at the Def Con conference in Las Vegas, covered in a pattern made by Bill Swearingen, as part of a test to see if it can defeat surveillance camera detection. Image Credits:Bill Swearingen / Donut Media Swearingen is not publishing his best patterns because he does not want camera makers to easily find and block them. Instead, he is using crowdfunding to develop and sell printed products such as T-shirts and hoodies, with vehicle wraps possibly coming later. It is still unclear whether the project will become a practical privacy tool for everyday users or remain mainly a DEF CON demonstration. Its real effectiveness will depend on how well the patterns work on real clothing, in different weather and camera conditions. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Surveillance camera)
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