Microsoft tracked over 30 MacSync Stealer domains by focusing on behavioral patterns, revealing a campaign targeting passwords, keys, wallets and other data. Domain blocking is a losing game when the thing you’re blocking can register a new domain faster than you can add it to a list. That’s the exact problem Microsoft Defender Experts ran into while tracking MacSync Stealer, a macOS-focused information stealer that RST Cloud first flagged for swapping out its command-and-control infrastructure almost immediately after getting publicly outed. Microsoft detailed how its experts stopped chasing individual domains and started tracking the behaviors that stayed constant underneath them. Instead of tracking individual domains, Microsoft looked at recurring request patterns, HTTP headers and other behaviors. This allowed its researchers to link more than 30 domains to the same campaign and determine that the infrastructure was doing more than just sending commands to infected Macs. It was also being used to collect, stage and exfiltrate stolen data. “MacSync Stealer is a macOS-focused information stealer that relies on changing infrastructure to deliver payloads, communicate with compromised devices, and exfiltrate data. Earlier reporting by RST Cloud identified the threat through a limited set of domains and documented rapid command-and-control (C2) replacement after public disclosure.” reads the report published by Microsoft. “Microsoft Defender Experts expanded that view by correlating recurring endpoints and network behaviors across the activity. This behavior-led approach connected more than 30 domains and showed that the infrastructure supported more than C2 communication, extending into active collection, staging, and exfiltration.” The infection chain starts with a trick rather than an exploit. Victims get social-engineered through a technique known as ClickFix, tricked into pasting or running commands directly in macOS Terminal, and once that shell session fires, curl pulls down attacker-controlled payload content from a path formatted as /curl/[token]. Then, native macOS tools decode and unpack the payload, and an AppleScript-driven layer takes over, blending Unix commands like sh, cp, rm, and killall with osascript calls that make the whole chain look more like ordinary system scripting than malware. Once active, the stealer focuses on valuable data. The malicious code looks for macOS Keychain data, saved browser passwords and cookies, SSH keys, AWS credentials, Kubernetes configurations and files in common user folders. It also searches for Ledger and Trezor wallet data, showing that the malware targets users with valuable credentials and assets rather than simply collecting random browser history. What actually confirms exfiltration, rather than just suspicious traffic, is the upload mechanism itself. Collected data gets staged under temporary paths, compressed into an archive, split into chunks, and pushed out through HTTP PUT requests carrying parameters like upload_id, chunk_index, and total_chunks. “The staged archive was uploaded through rotating infrastructure using curl and HTTP PUT requests. Observed requests included –data-binary, API-key headers, macOS User-Agent string, upload_id values, chunk_index values, and total_chunks parameters.” states Microsoft. “These upload traits confirmed active data exfiltration and provided durable hunting pivots even when domains rotated. “ The researchers pointed out that the exfiltration method stays recognizable even when the destination keeps changing. RST Cloud’s follow-up work backs up how consistent this infrastructure actually is under the surface. Using the same recurring URI patterns, RST Cloud surfaced eleven additional candidate domains and found a static API-key value shared across four confirmed command-and-control domains, even while the build token attached to each deployment kept rotating. A shared static key sitting inside otherwise rotating infrastructure is exactly the kind of detail that makes automated evasion look less impressive up close. The attack wraps up with cleanup, deleting temporary archives, staging folders, and lock files after the upload completes. Microsoft notes this reduces what’s left sitting on disk, but it doesn’t erase the behavioral sequence itself. “After exfiltration, the malware removed temporary archives, staging folders, lock files, and other artifacts. Although this cleanup reduced on-disk evidence, the sequence of archive creation, chunked upload, and deletion can still provide a useful behavioral correlation for defenders.” concludes Microsoft. For anyone defending Mac fleets, the practical takeaway here isn’t a list of domains to block, since that list will be stale within days. It’s building detection around the recurring shape of the attack itself: shell sessions spawning curl with those specific flag patterns, osascript chaining rapidly into network activity, and archives appearing under /tmp/sync* right before outbound PUT traffic starts. Chase the pattern, not the address, because the address was never going to sit still long enough to matter. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, malware)
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)
The SANDCLOCK LiteLLM supply-chain attack exposed credentials across 2,038 repositories, affecting technology, finance, healthcare, retail and more. Resecurity (USA) estimated the most affected sectors by the “SANDCLOCK” backdoor, which was planted as a result of the code repository compromise. According to cybersecurity experts, LiteLLM / TeamPCP Supply-Chain Attack will have long-lasting consequences. By compromising a well-known component in AI applications, adversaries will multiply the blast radius—some of the victim organizations are still unaware of the backdoor and its impact. LiteLLM is a popular open-soure AI gateway and utility library that unifies API calls for over 100 large language model providers, such as OpenAI, Anthropic, Google Gemini, and local Ollama models. Such incidents involve substantial MTTD (Mean Time to Detect) and MTTR (Mean Time to Respond). The threat actor group “TeamPCP” compromised maintainer credentials for LiteLLM and published malicious package versions 1.82.7 and 1.82.8 to PyPI around March 2026 – creating a window of exposure lasting at least a few months. Over 2,500+ organizations and hundreds of thousands of CI/CD environments suffered full-credential exposure, compromising cloud infrastructure keys, repository access tokens, SSH credentials, Kubernetes secrets, and AI provider API keys (such as OpenAI and Anthropic). Resecurity has acquired the 150GB archive attributed to the LiteLLM supply-chain attack conducted by TeamPCP using the “SANDCLOCK” credential-stealer. Per published incident reporting — accompanying victim manifests enumerate 898 compromised GitHub owners (organisations/accounts) across 2,038 repositories. The affected owners include major global enterprises — among them Microsoft, Azure, IBM, NVIDIA, PayPal (Zettle), Deloitte, Bosch, S&P Global, Elevance Health, 84.51° (Kroger), Adeo (Leroy Merlin), Kärcher, Dräger, ID.me and 1inch. Top 10 the most impacted sectors (by victim organization profile): Technology / Software Banking / Finance / Insurance Healthcare / Pharma / Medtech Retail / E-Commerce Media / Gaming / Adtech Manufacturing / Industrial Professional Services Cybersecurity Crypto Government Resecurity enumerated 2,146 records by key name (values never inspected beyond structural masking). The composition is overwhelmingly GitHub CI-CD identity material, with a long tail of high-value cloud and registry credentials. Victim manifests (owners.txt, repos.txt) enumerate 898 distinct compromised GitHub owners across 2,038 repositories. The distribution is long-tailed: 631 owners have a single affected repo, while the most-affected owner (Cencosud-Cencommerce) has 64. Critically, the owner list includes major global enterprises and regulated organisations. Every organization affected by the LiteLLM incident should revoke or rotate GitHub App private keys, PATs, AWS/GCP/Firebase credentials, ECR/JFrog tokens, SSH keys, and signing passwords, and invalidate sessions. Follow me on Twitter: @securityaffairs and Facebook and Mastodon Pierluigi Paganini (SecurityAffairs – hacking, newsletter)
A maximum-severity security vulnerability impacting SAP Commerce Cloud is witnessing active exploitation efforts. The vulnerability, tracked as CVE-2026-58231, is rated 10.0 on the CVSS scoring system. It relates to an instance of insufficient authorization checks and input validation. "SAP Commerce Cloud allows an unauthenticated attacker to abuse a default authentication client and submit
Google Workspace attacks do not always begin with phishing. Stolen OAuth tokens can provide another path into Gmail, Drive, and connected systems. Material Security explains why organizations need defenses that cover the entire Workspace attack chain.
A former data analyst contractor for Brightly Software has been sentenced to two years in prison for targeting his employer in a $2.5 million extortion scheme. Brightly is a Software-as-a-Service (SaaS) company formerly known as SchoolDude, which was acquired by Siemens in August 2022. Brightly employs over 700 people and provides asset management and maintenance software to more than 12,000 clients worldwide. 27-year-old North Carolina man Cameron Curry (also known as "Loot") was found guilty in March of orchestrating an "extensive cyber extortion scheme" targeting his employer. According to court documents, Curry stole sensitive documents after gaining access to the company's payroll information and corporate data, which he later used to extort Brightly after learning that his six-month contract wouldn't be extended.
An ongoing data theft campaign uses custom tools to steal data exposed to anonymous users through Salesforce Experience Cloud and ServiceNow customer portals. The data-theft campaign, dubbed City-Forum by SaaS security firm Reco, has been traced to a single server that has targeted multiple organizations worldwide. These organizations include telecommunications companies, banks and financial services firms, enterprise software vendors, security and data privacy companies, and public-sector portals. Reco says the attacks are ongoing, with activity continuing to increase. Reco says all of the attacks originate from the IP address 158.220.87.79, hosted by German VPS provider Contabo, and almost always use the default Go-http-client/1.1 user agent when downloading data. This IP address is associated with the city-forum.com domain, which has resolved to the server since at least March 2025, indicating that the infrastructure has remained in place for more than a year.
Two malicious LiteLLM releases sat on PyPI for about 40 minutes in March carrying credential-stealing code capable of harvesting cloud keys, SSH keys, Kubernetes tokens, database passwords, and other secrets from systems that installed them. Threat intelligence firm CloudSEK now says a dataset it obtained, built from roughly 434,000 files the attackers captured, maps potential exposure to more
SAP has released patches to address a maximum-severity security flaw impacting Commerce Cloud (Data Hub Adapter) that could result in arbitrary code execution. The vulnerability, assigned the CVE identifier CVE-2026-58231, is rated 10.0 on the CVSS scoring system. It has been described as a case of insufficient authorization checks and input validation. "SAP Commerce Cloud allows an
A Malicious SIM Card Can Run Attacker Code Inside the Modems Behind Cellular IoT Devices thehackernews.com
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