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 The actors leverage Internet scanning services to find Internet-exposed PLCs running outdated software or that are otherwise poorly protected.”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)
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.
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.
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.
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)
Citrix has released updates to address two security flaws impacting NetScaler ADC and NetScaler Gateway deployments, including a critical-severity authentication bypass vulnerability. According to the cloud computing and virtualization technology company, the issues affect customer-managed NetScaler ADC and NetScaler Gateway, including certain FIPS and NDcPP builds, as well as SecurAccess
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.
In a large-scale campaign that researchers dubbed CameraSwarm, hackers compromised more than 14,500 Dahua IP cameras mostly in Ukraine and Russia. The operation ran for at least 35 days between June 17 and July 22, compromising devices by exploiting vulnerabilities, brute-forcing logins, and using offline recovery codes from serial numbers for cloud-registered cameras. Researchers at threat intelligence company Hunt.io discovered the campaign after finding a working directory on an HTTP server that the operator left unprotected. Hunt.io recovered 407 MB of data comprising 2,616 files across 234 directories, including source code, logs, credentials, captured camera images, shell history, and exploitation results, which helped them map an impressive operation. According to their findings, the 35-day CameraSwarm campaign compromised 14,530 Dahua IP cameras using three attack methods in parallel:
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)
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)
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