Huntress observed a 155x increase in password spraying attacks in H1 2026, including a campaign that generated more than 81 million login attempts in two weeks. The attacks exploited legacy authentication and gaps in MFA policies that left some login flows unprotected.
Microsoft has reminded customers that systems running Home and Pro editions of Windows 11 24H2 will stop receiving updates in two months. "On October 13, 2026, Windows 11, version 24H2 Home and Pro editions, and Windows 10 Enterprise LTSB 2016 will reach end of updates," Microsoft warned in a message center update. "After this date, devices running these editions will no longer receive monthly security and non-security preview updates containing protections from the latest security threats." However, according to its support website, Windows 11 24H2 Enterprise and Education editions will remain under mainstream support until October 2027. Customers are advised to upgrade to Windows 11 25H2 (also known as the Windows 11 2025 Update), which has become generally available in September 2024 as a minor update installed through an enablement package.
Over 50,000 exposed Stripe API keys show how leaked secrets can enable fraud, data access and account abuse within hours. Ransomnews researchers have documented a large-scale leak of Stripe merchant API keys found exposed in public code repositories, GitHub Actions logs, and misconfigured web servers, with over 50,000 unique keys identified in total. The research is practical rather than theoretical: the team tested a sample of the keys, found a meaningful portion still active, and documented exactly how quickly a fraudster could exploit them. The answer is fast. “A dataset published on a data-trading forum on 18 August 2026 contains live Stripe API keys for 659 merchant accounts, along with roughly 35 GB of customer and payment data pulled from them.” reads the report published by Ransomnews. “Ransomnews analysed the files offline and reported the exposure to Stripe before publishing. Stripe itself was not compromised. The keys belong to merchants.” Researchers said that after finding an active Stripe API key, they were able to access a merchant’s customer list, create a fraudulent payment link and make a $1 test charge within 17 hours. The key alone was enough to perform these actions, highlighting the risks of exposed credentials and insufficient API protections. The operations a live Stripe secret key unlocks are extensive: listing customers and their stored payment methods, creating charges and payment intents, issuing refunds to attacker-controlled accounts, modifying webhook endpoints to intercept future payment notifications, and in some cases accessing connected accounts if the merchant had enabled Stripe Connect. A secret key is not a partial credential. It’s full API access. The sources of the leaked keys are unsurprising to anyone who has done developer security work. GitHub repositories — both public and accidentally made public, account for the largest share, typically through hardcoded keys in configuration files, .env files committed without a corresponding .gitignore entry, or keys left in code comments. GitHub Actions build logs are the second major source: when a workflow prints environment variables for debugging, any secret that wasn’t properly masked ends up in a log that anyone with repository access can read. Misconfigured web servers are another major source of exposed Stripe API keys. Researchers found over 3,000 servers revealing Stripe-related strings, with about 12% containing keys that worked against the Stripe API. The source of the 659 exposed merchant keys is unclear, but likely includes infostealer logs, public repositories, exposed environment files and misconfigured backups. The collector’s real advantage was systematically validating the keys, accessing each account and organizing the stolen data. “The dataset doesn’t say, and we are not going to guess at a single source for 659 separate merchants. The realistic candidates are the ordinary ones: secret keys sitting in infostealer logs lifted from developer machines, keys committed to public repositories, keys left in exposed environment files, keys pulled out of misconfigured backups. Stripe’s own documentation says the company scans for exactly this, and describes finding merchant keys on repositories and package registries.” concludes the report. “What the collector added was patience. Gathering keys is common. Validating several hundred of them, then systematically walking the API for each account and archiving the results into a consistent folder structure, is a different level of effort.” Stripe does provide automatic secret scanning through GitHub’s partner program, which flags Stripe keys found in public repositories and can trigger automatic revocation when a merchant opts in. The problem is that opt-in rate is low, the scanning doesn’t cover private repositories, and it has no coverage over build logs, web server misconfigurations, or other platforms where keys surface. Ransomnews also found that some merchants had rotated their keys after a GitHub exposure but left the old keys active, possibly because Stripe doesn’t revoke keys on rotation unless you explicitly delete the old one. The remediation is not complicated. Audit your current Stripe keys against your version control history to see if any have ever been committed. Rotate any key that has touched a public repository, a build log, or a configuration file that wasn’t explicitly protected. Enable Stripe’s restricted keys for any integration that doesn’t need full account access — a webhook handler doesn’t need the ability to create charges. And enable Stripe Radar rules to flag unusual charge patterns that might indicate someone else is using your key before you’ve noticed it’s gone. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, data leak)
The Cybersecurity and Infrastructure Security Agency (CISA) said Tuesday that the Medusa ransomware gang has breached more than 500 critical infrastructure organizations in the United States since June 2021. This was revealed in a joint advisory in coordination with the Department of Health and Human Services (HHS) and the Federal Bureau of Investigation (FBI). "As of April 2026, Medusa actors have impacted more than 500 victims across multiple critical infrastructure sectors, including Healthcare and Public Health, Defense Industrial Base, Critical Manufacturing, Government Services and Facilities, Information Technology, and Financial Services," they said. "Other victims include organizations in the medical, education, legal, insurance, technology, and manufacturing industries." This is an update to a joint report published in March 2025, which said the Medusa ransomware operation had impacted an estimated over 300 critical infrastructure organizations.
Cybersecurity researchers at Hunt.io have disclosed details of a campaign that they say compromised more than 14,530 Dahua devices between June 17 and July 22, 2026, using credential attacks, two authentication-bypass flaws, and a peer-to-peer (P2P) relay technique. The activity, codenamed Operation CameraSwarm, was reconstructed from a 407 MB exposed working directory containing 2,616 files
Cybersecurity researchers have flagged a global cybercrime operation that abuses thousands of hacked WordPress websites as infrastructure to disseminate malware, commandeer infected hosts, store stolen documents, screenshots, and activity logs created to track the status of the activity. "The operation doesn't rely on a single piece of malware, but on a whole toolkit of criminal software
A JavaServer Pages (JSP) web shell deployed following the exploitation of a critical security flaw in PTC Windchill and FlexPLM servers is specifically designed for the enterprise Product Lifecycle Management (PLM) software, according to new findings from ReliaQuest. The cybersecurity company characterized the web shell as a fully equipped extortion platform capable of mapping sensitive vault
A Heights Finance breach exposed personal and financial data of over 1.2 million people after hackers compromised a third-party cloud platform. Heights Finance is a U.S. consumer finance company that provides personal loans and related lending services, mainly to customers who may have limited access to traditional bank credit. It is part of Heights Finance Holdings Co. Heights Finance Holdings is notifying more than 1.2 million people that on May 7, 2026, Heights Finance discovered unauthorized access to a third-party cloud platform storing customer data. The company launched an investigation with external cybersecurity experts and notified federal law enforcement. “On May 7, 2026, Heights discovered that an unauthorized actor gained access to a cloud-based platform hosted by a third party that we use to store certain customer data. This activity was limited to the cloud-based platform only—it did not affect any of our loan management systems or other computer systems or networks. We immediately activated our incident response protocols, brought in outside cybersecurity specialists to investigate, and reported the incident to federal law enforcement.” reads the notice of data breach. “We have since confirmed that the cloud-based platform is secure and that there is no ongoing security threat. Our operations were not impacted by this incident and have continued safely and securely.” Heights said its internal systems and operations were not affected, the platform has been secured, and there is no ongoing threat. The compromised customer information included contact details, financial and bank account data, government IDs and dates of birth. The affected data varies by person and may involve Heights Finance customers, loan applicants, people who inquired about its products, or former borrowers of Curo Management and related brands. Heights Finance is offering affected individuals 24 months of free credit monitoring and identity protection. The company said dark web monitoring has found no evidence that the stolen data has been published. No threat actor has claimed responsibility, and no known ransomware or extortion group has been linked to the breach so far. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, Heights Finance)
A custom Java web shell likely linked to the Clop ransomware gang was designed specifically for PTC Windchill and FlexPLM servers, with built-in features to decrypt credentials, enumerate file repositories, and steal files. Cybersecurity company ReliaQuest analyzed the web shell after it is believed to have been deployed in recent data theft attacks exploiting CVE-2026-12569, a critical remote code execution vulnerability affecting PTC Windchill. ReliaQuest says the implant is not a generic web shell repurposed for the attacks, but was instead built with detailed knowledge of Windchill's internal APIs, database schema, keystore, and file-vault structure. "This appears to be an application-specific evolution of Clop's established mass-exploitation playbook," ReliaQuest said in a report shared with BleepingComputer.
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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