AI is making phishing attacks more personalized, convincing, and difficult for traditional email filters to detect. Kaseya explains how MSPs can monitor identity, email, and endpoint activity to detect and contain attacks that make it past the inbox.
The Cybersecurity and Infrastructure Security Agency (CISA) warned federal agencies that threat actors are now exploiting a critical MLflow vulnerability. MLflow is an open-source AI engineering platform for large language models (LLMs) and agents backed by the Linux Foundation, with over 30 million monthly downloads, used by thousands of organizations to debug, evaluate, optimize, and monitor AI applications. Tracked as CVE-2026-64849, this critical DNS-rebinding server-side request forgery (SSRF) bypass in MLflow's outbound webhook delivery was patched in version 3.15.0 and can be used by attackers without privileges to remotely access internal services or cloud metadata configurations on unpatched instances. "The default MLflow Tracking Server (mlflow server, no authentication, default SQLite backend) exposes the model-registry webhooks API unauthenticated, including a synchronous POST /api/2.0/mlflow/webhooks/{id}/test endpoint that returns the upstream response status and body to the caller," MLflow's security team says in a security advisory issued three weeks ago.
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.
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.
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)
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)
Pokémon Center is notifying customers in the United Kingdom and Germany that it suffered a third-party data breach after hackers stole customer personal and order information from third-party logistics provider CEVA Logistics. While CEVA's systems were compromised in the cyberattack, the exposed records belonged to Pokémon Center customers who submitted orders on the site. The company then shared this information with the logistics provider to fulfill and ship PokemonCenter.com orders. CEVA Logistics is a subsidiary of the CMA CGM Group, the world's third-largest shipping company. The logistics provider operates 1,000 warehouses, handled 15 million shipments last year, and reported $18.3 billion in revenue in 2025. The company recently suffered a cyberattack in which attackers breached its servers between July 29 and August 1, affecting multiple retailers in Europe.
The French Ministry of the Economy and Finance has disclosed a data breach after an attacker accessed the General Directorate of Public Finances (DGFiP) systems and stole data belonging to 678,000 individuals. This incident was discovered after a threat actor using the "ZeroBytes" handle claimed the attack and listed a stolen database for sale on August 12 on the PwnForums hacking forum. "The in-depth investigations conducted since August 12, 2026, have established that, prior to their interruption, these access points had been used to consult and extract data concerning a total of 678,000 individuals and professionals, including tax data such as reference tax income, family quotient, and withholding tax rate, and, for businesses, data such as their company name and SIREN number," the French Finance Ministry said.
GitLab has released security updates to address a critical vulnerability impacting its Community Edition (CE) and Enterprise Edition (EE) software that, under certain conditions, could allow an unauthenticated attacker to remotely modify or delete public projects and user data. The flaw, tracked as CVE-2026-19478, has been rated Critical by GitLab and assigned a CVSS score of 9.4. Released on
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