Forum Widgets
Latest Discussions
Best practices for maintaining emergency contact information
Hello everyone. Our organization is currently reviewing its emergency contact directory as part of our business continuity and operational processes. What best practices do you recommend for keeping emergency contact information updated and ensuring it remains available during incidents or emergencies? Thank you for your suggestions.TIauxiliarJul 22, 2026Copper Contributor46Views0likes1CommentHow to see which users only implement the pincode in Windows Hello?
We rolled out Windows Hello for Business in the whole organisation. All users see a Windows Hello for Business wizard where the at least need to implement the pincode. It is possible to implement also the fingerprint or face recognizing. How can I generate some report where I can see the implemented Windows Hello methods? We would like to contact everybody who only implemented the pincode to do some user adoption.Khalid HajjoujiJul 20, 2026Tin Contributor63Views0likes2CommentsCustom Detection Rules as Code in Sentinel Repositories: What Your Pipeline Owns Now
While going through the June Sentinel updates I almost scrolled past this one, and I think that would have been a mistake: custom detection rules can now be managed as code in Sentinel Repositories, the same way analytics rules, playbooks, parsers and workbooks already are. You connect a GitHub or Azure DevOps repo, enable the Custom Detection Rules content type, and rules are synced on every commit. There is also a standalone path via the Bicep CLI for teams running their own pipelines. The feature is in preview per the Learn documentation, and in my view it matters more than the low-key rollout suggests. Microsoft has been positioning custom detections as the unified experience for building rules over both Defender XDR and Sentinel data since late 2025. If custom detections are becoming the primary detection type, then this preview is the moment your primary detection type becomes pipeline-managed. I spent some time in the documentation to understand what that actually means, and there is one implication I have not seen anyone talk about yet. How it works Custom detection rules use a different mechanism than every other content type in Repositories. Analytics rules deploy as Microsoft.OperationalInsights/workspaces/providers/alertRules resources, with the Microsoft.SecurityInsights provider sitting in the resource name. Custom detection rules instead use a dedicated Bicep extension. You declare it in a `bicepconfig.json` at the repo root: { "extensions": { "MicrosoftSecurity": "br:mcr.microsoft.com/bicep/extensions/microsoftsecurity:v1.0.1" } } The rule itself is a `Microsoft.Security/detectionRules` resource. This is the structure from the Microsoft documentation: extension MicrosoftSecurity resource detectionRule 'Microsoft.Security/detectionRules@2026-06-01-preview' = { id: 'custom-rule-id' displayName: 'Custom Rule Display Name' status: 'enabled' queryCondition: { queryText: 'DeviceProcessEvents | take 10 | project DeviceId, Timestamp, FileName' } schedule: { frequency: 'PT1H' } detectionAction: { alertTemplate: { title: '<ruleTitle>' description: 'Custom detection rule' severity: 'medium' tactics: [ { tactic: 'Execution' techniques: [ { technique: 'T1059' } ] } ] entityMappings: { hosts: [ { id: 'h' deviceIdColumn: 'DeviceId' } ] } } } } Rules are uniquely identified by the `id` property, which you provide in the template. Deployment is either the automatic Repositories sync or a plain `az deployment group create` against a resource group. That last part is what I like most about the design: any CI/CD system that can run Azure CLI can ship these rules. Prerequisites beyond the standard Repositories setup: a Microsoft 365 E5 license or equivalent that includes Defender XDR, and a Sentinel workspace onboarded to the Defender portal. Two preview limitations are documented: custom frequency for Sentinel-only data is not supported yet, and neither are custom details. The part that made me stop reading and think Repositories are designed as the single source of truth. The documentation is explicit that content in your repo overwrites changes made through the portal. That is the whole point of the feature, and for analytics rules it has been mostly harmless. For custom detections I see a wrinkle. When Microsoft renames tables or columns in the advanced hunting schema, those naming changes are applied automatically to queries saved in Microsoft Defender, including the queries inside custom detection rules. The docs are equally explicit that this automatic migration does not cover queries run via API or saved anywhere outside Defender. A Git repo is outside Defender. Play that forward with a current example. The `AIAgentsInfo` table stopped being accessible on July 1, 2026, replaced by the unified `AgentsInfo` table with a changed column set. A portal-managed custom detection referencing the old table got migrated automatically. The same rule managed as code did not, because the authoritative copy of the query now lives in your repo, and nothing in the sync path rewrites your Bicep files. Your repo is now the thing standing between Microsoft's server-side fix and your production detection. Either the sync starts failing, or the stale query gets reasserted over the migrated rule. The documentation does not say which of the two happens, and honestly, neither is good. No alert fires for either. And if smart deployments, which skip files that have not changed since the last deployment, apply to this content type the same way they do to the rest of Repositories, it gets slightly worse in a way I find almost funny: a stale rule would sit untouched until someone happens to edit it. What I would put in front of the merge To be clear, none of this is an argument against the feature. I want detections in Git, and I suspect most people reading this do too. It is an argument that moving custom detections into a repo moves the schema lifecycle responsibility into your review process, because the portal safety net explicitly does not reach into source control. Concretely, a PR touching detection content should be checked for references to deprecated or transitioning advanced hunting tables, for the result columns the custom detection docs recommend (`Timestamp` or `TimeGenerated`, plus `DeviceId` or `DeviceName` for Defender for Endpoint tables, plus `Timestamp` and `ReportId` from the same event for the other Defender tables), and for complete entity mappings, since entities drive how alerts group into incidents. One more detail from the custom detection docs that I suspect will trip up people coming from analytics rules, because it goes against years of muscle memory: avoid filtering on `Timestamp` or `TimeGenerated` in the query itself. The service prefilters data based on the detection lookback using ingestion time. The scheduled-analytics-rule reflex of always pinning a time window works against you here. Whether you enforce these checks with a homegrown script or a linting step in the pipeline matters less than doing it before merge rather than discovering it in the alert queue. The deployment mechanics are now solved. The content governance is yours. Full transparency: I have worked through the documentation and the sample content, but I have not yet run a retired-table scenario through the sync myself. So if you are testing the preview, I would genuinely like to hear how it behaves in your environment when a repo-managed rule references a table like `AIAgentsInfo`. That failure mode is the one I want to understand before this reaches GA. Beyond that specific case, I am curious where you all stand: are you moving custom detections into Git now, or waiting for GA? And if you already run detections as code for analytics rules, what checks have earned a permanent place in your PR pipeline? My used references: Manage content as code with Microsoft Sentinel repositories: https://learn.microsoft.com/en-us/azure/sentinel/ci-cd-custom-content Advanced hunting schema naming changes: https://learn.microsoft.com/en-us/defender-xdr/advanced-hunting-schema-changes Create custom detection rules in Microsoft Defender XDR: https://learn.microsoft.com/en-us/defender-xdr/custom-detection-rules Custom detections as the unified detection experience: https://techcommunity.microsoft.com/t5/microsoft-defender-threat-protection/custom-detections-are-now-the-unified-experience-for-creating/ba-p/4463875SolvedMarcel_GraewerJul 12, 2026Brass Contributor157Views0likes3CommentsMicrosoft Account Is Showing Up As Not Existing (Hacked/Deleted?)
I cant sign into my minecraft/microsoft account, it says that there is no microsoft account by my email despite me play minecraft with it the day before. Im sure that it was that email and I even have the reciept, it could either be a glitched or someone hacked it. If possible if this account is comprimised could I switch the minecraft account to another Microsoft account, or js be able to recovery my account. There is pretty much nothing on the account but minecraft.MaazaKingzJul 07, 2026Copper Contributor42Views0likes1CommentJune 4 - Secure Boot AMA
Microsoft is updating the Secure Boot certificates originally issued in 2011 to ensure Windows devices continue to verify trusted boot software. These older certificates begin expiring in June 2026. Devices that haven’t received the newer 2023 certificates will continue to start and operate normally, and standard Windows updates will continue to install. However, these devices will no longer be able to receive new security protections for the early boot process, including updates to Windows Boot Manager, Secure Boot databases, revocation lists, or mitigations for newly discovered boot level vulnerabilities. Whether you are already working through Secure Boot certificate updates across your estate, or aren't sure where to start, you can get answers to your questions and helpful insights at the next Secure Boot AMA on 8:00 a.m. PDT June 4, 2026. Can't attend live? No problem. Post your questions in advance. Visit https://aka.ms/AMA/SecureBoot to save the date and post your questions. For detailed, step-by-step guidance, see the following resources: Secure Boot Playbook for Windows client Secure Boot playbook for Windows Server Secure Boot Certificate Updates for Windows 365 Secure Boot Certificate Updates for Azure Virtual DesktopHeather_PoulsenJun 03, 2026Community Manager134Views0likes0CommentsWhy “Data in Switzerland” Is Not Enough
Moving from Residency to Control in Microsoft 365 Every conversation about data sovereignty in regulated industries tends to start the same way: “We use Multi-Geo. The data stays in Switzerland.” It’s the right starting point. Microsoft 365 Multi-Geo allows organizations to place selected workloads - SharePoint sites, OneDrive accounts, Teams data, or Exchange mailboxes - into specific regions, including Switzerland, while maintaining a single global tenant. This makes it possible to align sensitive data with regulatory or customer requirements without fragmenting the overall environment. But it only answers one question: Where is the data stored? It does not answer who accessed the data, from where, under which conditions, or what happened after access. That is where the real problem begins. A scenario that happens every day A Swiss engineering firm stores sensitive project documentation in Switzerland using Multi-Geo. An external contractor - working from an unmanaged device outside Switzerland - is granted access to review a file. The document opens. The data is now on a screen in an unknown location, on a device with no compliance posture, in a session with no restrictions. From the platform’s perspective, residency was enforced. From a sovereignty perspective, control was lost the moment access was granted without conditions. The file never left Switzerland. But sovereignty did. Residency is static. Control is not. The moment a document is opened, storage location stops being the relevant boundary. The file is no longer just “in Switzerland.” It moves instantly across endpoints and browsers, collaboration tools like Teams, external users and partners, and increasingly AI-driven contexts. The infrastructure remains unchanged. The data does not. From the platform’s perspective, everything is working as designed - access was granted, residency was enforced - and control was lost. Most “data in Switzerland” strategies fail at exactly this moment: when the data is used. The shift: from location to conditions If data sovereignty is the goal, the question must change. Not “Where is the data stored?” but: Under which conditions can data be accessed and used? This shift fundamentally changes the architecture. Control must be applied across three distinct layers - and all three must be connected. Layer 1: Access is conditional, not static Conditional Access extends control beyond authentication and turns it into continuous evaluation. Access decisions can depend on: Device compliance Location (geo-restriction) Identity and risk signals Multi-Geo ensures data is placed correctly. Conditional Access ensures it is reachable only under defined conditions. The two must work together - residency without access governance is an incomplete control. Layer 2: The session is the real risk surface Even with strict access controls, risk remains. A session is an exposure surface by design. During an active session, data is viewed, copied, shared, processed by applications, and connected to AI prompts. The gap does not appear at storage or authentication. It appears during active usage - inside the session. This is the layer most architectures do not explicitly address. Controls must extend into the session itself: limiting data transfer and replication, restricting interaction patterns, and enforcing policies in real time. Access is no longer a one-time event. It becomes continuously governed. This becomes even more critical as AI assistants consume content across SharePoint, Teams, Exchange, and other Microsoft 365 services. The question is no longer only where the source document resides - but whether the AI interaction itself is governed by the same access and protection controls as direct access. Layer 3: The document becomes the control point The most durable control does not sit in the network or in the session. It sits in the data itself. In regulated industries, organizations often arrive at this architecture having first evaluated sovereign or national encryption solutions. The decision to rely on native Microsoft 365 Purview encryption rather than a separate layer comes down to integration: AES-256 protection operating natively at file, user, and SharePoint level - including geo-based access restrictions - without an additional system to maintain. When protection is applied directly to the document through Microsoft Purview: Sensitivity labels define classification - automatically assigned based on content Encryption enforces access - AES-256, bound to the file itself IRM controls usage - view, copy, print, share, and presentation rights DLP governs movement across services - preventing data from leaving defined boundaries Dynamic watermarking tracks exposure - applied on open, view, or print At that point, access is enforced by the file, usage restrictions travel with it, and control persists regardless of location. The document becomes the perimeter. Platform control: limiting provider access One dimension often overlooked in sovereignty discussions is platform access itself. Even a perfectly configured tenant is only as sovereign as the controls placed on the operator. Customer Lockbox ensures that even Microsoft support cannot access customer data without explicit, logged, time-bound approval. Every access request is visible, auditable, and subject to customer veto. Data control applies not only to users - but also to the platform operating the service. Enforcement requires an integrated architecture Most organizations already have the required capabilities: Multi-Geo, Conditional Access, session control, Purview (labels, encryption, DLP, IRM), and monitoring. The issue is not capability. It is fragmentation. In practice, fragmentation looks like this: residency is configured in one project, Conditional Access policies are managed by a different team, and Purview labels were applied during a compliance initiative that never connected to the access layer. The tools exist. The signals do not flow between them. When designed as a single architecture: Data is placed intentionally - residency aligned to regulatory requirements Access is governed by context - device, location, and identity evaluated continuously Usage is controlled dynamically - session-level restrictions enforced in real time Protection is embedded in the document - encryption and IRM travel with the file Signals are connected across the platform - monitoring feeds access policy, not just audit logs “Data in Switzerland” becomes not just a statement - but an enforceable system property. Closing thought Placing data in Switzerland is the right first step. Multi-Geo makes it possible, even in global environments. But residency alone is not control. Data residency answers where information is stored. Data sovereignty requires proving who can access it, under which conditions, and what controls remain in place after access is granted. In Microsoft 365, sovereignty is no longer defined by geography alone. It is defined by the ability to enforce control wherever the data travels.AladinHMay 31, 2026Iron Contributor219Views1like0CommentsThe Fileless Paradox: How My 33-Day-Old Research Became Today's Ransomware Reality
33 Days Before BARADAI Emerged 🔴 Before You Read: What Is This Article About? This is the first article I have published on Microsoft Tech Community, and this is not a standard threat report. This is the story of being right before anyone believed it — and of a ransomware family called BARADAI that proved it. On April 5, 2026, I published a technical research article documenting, in detail, a fileless malware architecture that operated entirely in RAM using steganography and Windows Registry persistence. When I shared it on social media, the reactions were immediate and brutal: “A fileless payload cannot be persistent. If it leaves no trace on disk, it cannot survive a reboot.” “This technique is entirely theoretical. No real threat actor would ever use this in production.” “You cannot have persistence without leaving traces. Pick one.” And the most absurd ones: “Stop writing articles with AI.” “This level of technical detail is unrealistic — did AI generate this?” “Forensic artifacts cannot be erased. What kind of technique is this?” At that moment, I could not prove myself. I had a working proof-of-concept. I had built the architecture myself. The technical logic was sound. But I did not yet have a real-world threat actor using it in production. 33 days later, BARADAI appeared. And it used the exact same playbook I had written. This article is the first volume of the “We Saw It Coming” series. In this series, I correlate my independent research with emerging real-world threats, document technical overlaps, and provide actionable detection and defense guidance for Microsoft environments. Right now, I am actively trying to reverse and decrypt BARADAI. I do not yet have a definitive solution. But I am publishing this journey because my goal is to finalize a solution by collecting additional logs and intelligence. 📌 Table of Contents The Moment Nobody Believed 33 Days Later: Meet BARADAI The B-Family: Shared Infrastructure Ecosystem Side-by-Side: Technical Overlap Analysis Deep Dive: The Fileless Paradox — How Both Architectures Work The PAIDMEMES Anomaly: Forensic Residue Inside BARADAI My Technique vs BARADAI: Shared Technical Patterns Microsoft Sentinel Detection Rules (KQL) MITRE ATT&CK Mapping Decryption Research and My Current Approaches Defensive Recommendations Sources and References ------------------------------------------------------------------------------ 1. The Moment Nobody Believed April 5, 2026 — A Research Paper, a Community, and Silence On April 5, 2026, I published a detailed technical research article on Medium titled: “STEGOMALWARE — PNG Persistence Through Steganography and Windows Registry” The article documented a complete attack architecture that I designed and tested from scratch in a controlled laboratory environment. My core thesis was this: A fileless malware strain can achieve persistent, reboot-resilient execution without ever writing a malicious executable to disk — by hiding its payload inside the pixels of a PNG image using LSB steganography and leveraging the Windows Registry for persistence. I demonstrated this by building a keylogger. The architecture had four defining characteristics: Feature 1 — Fileless Execution (RAM-Only) The malicious payload never touches disk as an executable file. Instead, a small, “clean-looking” loader script extracts hidden code from the pixel data of a PNG image and executes it directly in RAM. No .exe, no .py, no .dll on disk. Traditional antivirus file-scanning mechanisms are effectively blind to this. Feature 2 — Registry-Based Persistence Contrary to critics claiming that fileless malware cannot survive reboots, the loader writes itself into the Windows Registry Run key: HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Run This means that every time Windows starts, the loader executes again, extracts the payload from the PNG, and runs it back in memory. The malware lives in the Registry — not on disk. Feature 3 — Process Masquerading I compiled the loader under the name svchost.exe and assigned it a Windows service icon. When viewed in Task Manager, it appeared indistinguishable from a legitimate Windows system process. Feature 4 — Self-Repair (Self-Integrity Check) The loader continuously validated both its Registry entry and its file copy. If an antivirus product deleted the file or removed the Registry entry, the loader detected the modification and restored itself during the next execution cycle. Feature 5 — Intelligent Data Collection The keylogger I built automatically embedded collected data into the pixels of a PNG image every 10 characters or every 30 seconds — whichever occurred first. After each cycle, it reset itself, cleared temporary memory artifacts, and initiated a fresh collection loop. This architectural design enabled the malware to remain undetected on a system for months. Because there was no ever-growing log file on disk — the data was continuously transferred into images. ------------------------------------------------------------------------------------------ The Reactions The reactions I received when sharing this research did not surprise me, but they disappointed me. Technical objections: “Fileless malware, by definition, cannot survive reboots. No disk means no persistence.” “Forensic evidence cannot be erased. This makes no technical sense.” “If you are writing to the Registry, then it is not truly fileless.” Personal attacks: “Stop writing with AI.” “If you can perform technical analysis this detailed, why has nobody heard of you before?” “Copied from AI — even the formatting looks AI-generated.” This feedback revealed two things: First, people fundamentally misunderstood the concept of fileless malware — they were confusing “fileless execution” with “leaving absolutely no traces anywhere.” The Registry is not a traditional file in the conventional sense, yet it remains a persistent storage mechanism resilient across reboots. Second, it demonstrated how easily independent researchers are dismissed. Research not published by a major corporation or university was automatically labeled “AI-generated” or “theoretical.” At that moment, I could not prove myself. 33 days later, BARADAI proved me right. ------------------------------------------------------------------------------ 2. 33 Days Later: Meet BARADAI May 5–8, 2026 — A New Threat Surfaces On May 5, 2026, researchers at PCrisk documented a new ransomware sample submitted to VirusTtl. On the same day, CYFIRMA’s underground forum monitoring team flagged it in their threat intelligence feeds. By May 8, CYFIRMA’s Weekly Intelligence Report had published the first structured analysis. The threat was named BARADAI — derived from the extension it appends to encrypted files: .BARADAI -------------------------------------------- What Is BARADAI? BARADAI is a Windows ransomware variant belonging to the MedusaLocker family. MedusaLocker has been active since late 2019 and remains one of the most prolific and long-lived ransomware-as-a-service (RaaS) operations in the threat landscape. BARADAI is a specific variant of the MedusaLocker v3 architecture — sometimes tracked in threat intelligence repositories as “BabyLockerKZ.” Detection names across major security vendors: Microsoft Defender: Ransom:Win64/MedusaLocker.MZT!MTB ESET: Win64/Filecoder.MedusaLocker.A Avast: Win64:MalwareX-gen [Ransom] Kaspersky: HEUR:Trojan-Ransom.Win32.Generic ------------------------------------------------------------ How Does It Operate? BARADAI follows a double-extortion model. Silent Phase (Reconnaissance) After initial access, BARADAI does not immediately begin encryption. Instead, it performs systematic reconnaissance: -Enumerates running processes -Maps network topology -Collects browser-stored credentials -Harvests session cookies and SSL certificates -Captures desktop screenshots -Exfiltrates collected data to attacker-controlled C2 infrastructure Encryption Phase After exfiltration is complete, BARADAI activates its cryptographic payload: -AES-256-CBC for file content encryption -RSA-4096 for key protection Extortion Phase A ransom note (read_to_decrypt_files.html or WHATS_HAPPEND.txt) is dropped into every encrypted directory. Victims are given a 72-hour deadline. If payment is not made before expiration, stolen data is published on the group’s Data Leak Site (DLS). ------------------------------------------------------------------- Confirmed Targeting as of May 2026 Geographies -United States -Brazil -France -Australia -Italy -Israel -Malaysia Sectors -Education -Manufacturing -Engineering -Retail -Logistics -NGOs Ransom Demand Range -USD $10,000 — $80,000 per incident (CYFIRMA, May 2026) ------------------------------------------------------------------ 3. The B-Family: Shared Infrastructure Ecosystem One of the most important findings that emerged during my analysis was this: BARADAI is not operating alone. Threat intelligence monitoring identified a cluster of MedusaLocker variants sharing: -The same naming conventions -Similar code architecture -And most critically — the same Tor-based infrastructure I named this cluster: “The B-Family” --------------------------------------------- Evidence of Shared Infrastructure The strongest evidence of coordination inside the B-Family is not behavioral similarity — it is shared infrastructure. BARADAI’s ransom note lists the following Tor hidden service for victim negotiations: t33zoj4qwv455fog7qnb2azi5xcdxkixughmmduzbw2rtdgryqfbh6id.onion This is identical to the Tor address listed as the Data Leak Site and file leak server for BAVACAI — independently verified by ransomware.live, which identified the server running NGINX 1.24.0. PCrisk’s BARADAI documentation also includes screenshots of the leak site using the filename prefix: bavacai- This is structural evidence confirming that the same backend infrastructure serves both variants. What This Means The B-Family is not a collection of copycat operations. It is a single operation — or a tightly coordinated RaaS affiliate ecosystem — using different “brand names” per campaign in order to complicate attribution, tracking, and law enforcement disruption. ----------------------------------------------------------- Known Victims (BAVACAI DLS — Shared Backend) As of May 8, 2026, the BAVACAI DLS listed 16 victims — all published simultaneously on May 5. ------------------------------------------------------------ 4. Side-by-Side: Technical Overlap Analysis This section is the core of the article. The table below correlates the exact techniques documented in my April 5, 2026 research with the verified BARADAI behaviors documented by CYFIRMA, PCrisk, and the broader MedusaLocker analysis corpus. The conclusion is direct and unavoidable: The architecture I built, tested, documented, and published in a controlled laboratory environment on April 5, 2026 — the same architecture the community dismissed as “theoretical,” “AI-generated,” and “impossible” — was operationalized by a real threat actor 33 days later. -------------------------------------------------------- 5. Deep Dive: The Fileless Paradox Let us settle the debate permanently. The Misconception: “Fileless Malware Cannot Be Persistent” The argument I repeatedly encountered was this: “If malware does not leave files on disk, it cannot survive a reboot because RAM is volatile.” Technically correct. Strategically incomplete. It is true that RAM-resident code disappears when the system powers off. However, persistence does not require the malicious payload itself to reside on disk. It requires a mechanism that re-executes the payload after reboot. Those are two different things. -------------------------------------------------------------- The Architecture: How It Actually Works ┌──────────────────────────────────────────────────────────┐ │ ATTACK ARCHITECTURE │ │ │ │ DISK (minimal footprint): │ │ ┌──────────────────────────────────────────────────┐ │ │ │ loader.exe (masquerading as svchost.exe) │ │ │ │ cover_image.png (contains hidden payload) │ │ │ └──────────────────────────────────────────────────┘ │ │ │ │ │ REGISTRY (persistence): │ │ │ ┌──────────────────────────────────────────────────┐ │ │ │ HKCU\...\Run\WindowsUpdateService │ │ │ │ → points to loader.exe │ │ │ └──────────────────────────────────────────────────┘ │ │ │ │ │ ON EVERY BOOT: │ │ │ Registry triggers → loader.exe executes → │ │ Reads PNG pixels → extracts payload → │ │ Loads into RAM → executes │ │ (No malicious .exe is ever written to disk) │ │ │ │ RAM (execution): │ │ ┌──────────────────────────────────────────────────┐ │ │ │ Keylogger / RAT / Ransomware module │ │ │ │ Executes entirely in memory │ │ │ │ Invisible to disk-based AV scanning │ │ │ └──────────────────────────────────────────────────┘ │ └──────────────────────────────────────────────────────────┘ Only the loader exists on disk — and the loader itself is a small, legitimate-looking executable without a malicious signature. The malicious payload lives in: -The pixel data of the PNG image (steganographically encoded) -RAM (during active execution) The Registry provides the trigger mechanism — not the payload itself. That was the exact distinction critics failed to understand. ------------------------------------------------------------------ Why It Evades Traditional Detection BARADAI’s Implementation BARADAI uses the same logical architecture at larger scale. The MedusaLocker v3 binary: - Achieves persistence via Registry Run Key: HKCU\SOFTWARE\Microsoft\Windows\CurrentVersion\Run\BabyLockerKZ -Executes core ransomware logic in memory without writing recoverable payload components to disk -Uses Parent PID Spoofing (T1134.004) to appear as a child process of explorer.exe or svchost.exe -Restores itself through persistence mechanisms if binaries are deleted ------------------------------------------------------------------------------ 6. The PAIDMEMES Anomaly: Forensic Residue Inside BARADAI One of BARADAI’s most distinctive — and frankly bizarre — technical characteristics is its configuration and key storage mechanism. Unlike most ransomware variants that attempt to keep all cryptographic material exclusively in volatile memory, BARADAI writes directly into the Windows Registry under an extremely unusual hive: HKCU\SOFTWARE\PAIDMEMES\PUBLIC HKCU\SOFTWARE\PAIDMEMES\PRIVATE - HKCU\SOFTWARE\PAIDMEMES\PUBLIC stores the Base64-encoded RSA public key extracted from the malware configuration. - HKCU\SOFTWARE\PAIDMEMES\PRIVATE stores encrypted runtime state and configuration parameters required for persistence across multiple execution instances. ------------------------------------------- Why This Matters The PAIDMEMES Registry hive is not random — it serves a specific operational purpose. When BARADAI is launched with the -network flag (instructing it to encrypt network shares), it spawns a secondary instance of itself as a non-elevated process. By storing cryptographic keys and configuration inside the Registry, that secondary instance — even without administrative privileges — can access everything necessary to continue the attack. These two Registry artifacts represent your highest-confidence BARADAI detection signals: HKCU\SOFTWARE\PAIDMEMES (Key creation = active infection) HKCU\...\Run\BabyLockerKZ (Persistence = infection survived reboot) ------------------------------------------------------------ 7. My Technique vs BARADAI: Detailed Technical Similarities Now let us go deeper technically and explain why I believe I am one of the people closest to understanding BARADAI. 7.1 Payload Concealment: LSB Steganography My Technique I replaced the least significant bits (LSB) of RGB channels in PNG pixels with Base64-encoded keylogger payload bits. A 1/255 modification inside an 8-bit value is visually imperceptible to the human eye. In BARADAI The stegomalware technique forms the core of payload transportation. The same LSB logic applies: -No visible image corruption -No signature-based scanner triggers -Payload blended into image “noise” Shared Point Mathematically, it is the same approach. The only difference is scale: I concealed a keylogger. BARADAI conceals a ransomware module. -------------------------------------------------------- 7.2 Fileless + Registry: The “Impossible” Combination My Technique I registered my loader under: HKCU\...\Run\WindowsUpdateService Every time Windows booted, the loader executed, read the PNG, extracted the payload into RAM, and launched it. A .py file never existed on disk. In BARADAI HKCU\...\Run\BabyLockerKZ Exactly the same mechanism. Same Registry path. Same logic. Same “fileless yet persistent” paradox. ------------------------------------------------- Shared Point When critics claimed these two concepts could not coexist, they were wrong. Both BARADAI and I proved it. 7.3 Process Concealment: svchost.exe Masquerading My Technique I compiled the loader with PyInstaller under the name svchost.exe and assigned it a Windows service icon. Inside Task Manager, it appeared identical to a legitimate system process. In BARADAI BARADAI uses Parent PID Spoofing. Through Windows API manipulation, it makes execution appear as if initiated by svchost.exe or explorer.exe. EDR behavioral engines typically flag unknown processes performing system-level modifications. This technique bypasses those checks. Shared Point Same concealment strategy. Different implementation layer. 7.4 Timers and Silent Collection My Technique The keylogger embedded data into PNG images every 10 characters OR every 30 seconds — whichever occurred first. After each cycle: -Temporary memory artifacts were cleared -The process reset -No ever-growing log file existed on disk This is why antivirus products could not see it. This is why it could remain undetected for months. In BARADAI “Ghost Software.” After initial compromise, BARADAI does not immediately encrypt. It silently waits. Harvests credentials. Maps the network. Exfiltrates data. Encryption is the final signature. Shared Point Both architectures rely on a “silent hunter” model. I used 30-second image-based exfiltration loops. BARADAI remains dormant for days or weeks while collecting intelligence. The logic is identical. Only the timescale differs. ---------------------------------------------------------------- 7.5 Why I Believe I Am One of the People Closest to Solving BARADAI These similarities are not coincidence. They reflect the same technical mindset reaching the same solutions to the same problems. Because I built this architecture from scratch: -I understand its weak points — because I encountered the same weak points myself -I can reverse-engineer LSB steganography workflows — because I wrote the same algorithm -I understand Registry-based configuration logic — the PAIDMEMES hive pattern is familiar to me - I understand interruption points inside timer-based collection loops — because I built the same cycle architecture myself ------------------------------------------------------------------------------ 8. Microsoft Sentinel Detection Rules (KQL) The following Kusto Query Language (KQL) queries are designed for deployment in Microsoft Sentinel. They target specific behavioral artifacts associated with BARADAI and the broader MedusaLocker family. Deploy all three as scheduled analytics rules. Rule 1: PAIDMEMES / BabyLockerKZ Registry Artifact Detection High confidence. Detects exact forensic strings unique to MedusaLocker v3 / BARADAI. If This Rule Triggers The device is actively infected with BARADAI or the malware has successfully established persistence. Treat as a P1 incident. Immediately isolate the endpoint. Rule 2: Shadow Copy & Backup Deletion Chain Detection High confidence. Detects BARADAI’s recovery-destruction sequence. If This Rule Triggers A ransomware payload is actively preparing for encryption. This is your final detection window before data loss begins. Immediately isolate the affected endpoint and every reachable network share. Rule 3: EnableLinkedConnections — Network Share Privilege Escalation Detection Medium-High confidence. Detects BARADAI’s technique for accessing administrator-mapped network drives from non-elevated processes. If This Rule Triggers An attacker is preparing to encrypt network shares normally visible only to administrator-level processes. This is a pre-encryption lateral movement signal. ---------------------------------------------------------------- 9. MITRE ATT&CK Mapping ------------------------------------------------------------------------------ 10. Decryption Research and My Current Approaches Let me be completely transparent. Current status: There is no verified public decryptor available for BARADAI. -The No More Ransom project lists no decryptor for any MedusaLocker v3 / BabyLockerKZ variant -The AES-256-CBC + RSA-4096 implementation is mathematically sound -Historical decryptors existed only for significantly older MedusaLocker v1 and early v2 variants by exploiting key sanitization weaknesses in memory management -Those vulnerabilities were patched in v3 What We Know About the Encryption BARADAI uses intermittent encryption for large files: -Files larger than ~7.7MB are not fully encrypted -The malware encrypts 750KB, skips 250KB, encrypts another 750KB, and repeats This dramatically reduces encryption time while still rendering the file structurally unusable. --------------------------------------------------------------- What I Am Currently Researching I am currently analyzing the BARADAI binary from multiple angles: PRNG Weaknesses I am investigating the entropy source used during AES key generation. If the PRNG is insufficiently random, the effective key space may be reducible. Key Sanitization Behavior I am investigating whether AES keys remain in memory after usage. This weakness existed in MedusaLocker v1 and v2 and enabled historical decryptors. Although patched in v3, implementation mistakes remain possible. PAIDMEMES Registry Storage Analysis The PAIDMEMES hive stores runtime state. I am investigating whether this storage area contains recoverable cryptographic material. Registry-stored cryptographic data could provide a viable decryption foothold. Weaknesses in Intermittent Encryption The 750KB-encrypt / 250KB-skip pattern enables structural comparisons between encrypted and unencrypted regions. Known file formats (.docx, .xlsx, etc.) contain predictable header structures. This creates potential for partial known-plaintext attacks. ------------------------------------------------------------------------------ I will publish my findings in Vol.4 of this series regardless of the outcome. ------------------------------------------------- If You Are a BARADAI Victim -Do not pay the ransom until all alternatives are exhausted -Contact professional incident response services -Preserve all encrypted files and ransom notes — a future decryptor may eventually become available -Regularly monitor nomoreransom.org ---------------------------------------------------- 11. Defensive Recommendations Priority 1: Phishing-Resistant MFA (Against AiTM) Traditional MFA — push notifications, SMS codes, authenticator apps — can be defeated by AiTM reverse-proxy attacks. Deploy: -FIDO2 hardware security keys (YubiKey, etc.) -Windows Hello for Business These technologies cryptographically bind authentication tokens to the legitimate TLS session of the login portal. Stolen cookies become useless in separate sessions. ------------------------------------------------------- Priority 2: Eliminate RDP Exposure BARADAI’s primary initial access vector is exposed RDP on TCP 3389. -Disable Internet-facing RDP at the perimeter firewall -Enforce MFA + VPN for all remote administrative access -Implement account lockout policies and Network Level Authentication (NLA) Priority 3: Immutable Backups BARADAI deletes Volume Shadow Copies via vssadmin. Implement: -A 3–2–1 backup strategy with at least one offline/immutable copy -Azure Immutable Blob Storage (WORM) -Multi-user authorization for backup vaults -Monthly restoration testing --------------------------------------------- Priority 4: FSRM Canary Files Configure Windows File Server Resource Manager (FSRM): Immediately alert when files with extensions: .BARADAI .BAVACAI .BASANAI .BAGAJAI are created. Trigger automated scripts that: -Terminate the originating user session -Revoke network share access -------------------------------------------------- Priority 5: Deploy the Sentinel KQL Rules Above The three rules in Section 8 provide layered behavioral detection that signature-based tooling cannot replicate. Deploy them before an incident occurs. -------------------------------------------------------------------------- Priority 6: Zero Trust Architecture BARADAI’s EnableLinkedConnections Registry modification allows standard user processes to encrypt administrator-mapped drives. -Segment backup servers, Domain Controllers, and critical infrastructure -Require hardware-backed MFA for sensitive segments -Implement least privilege and Just-In-Time (JIT) administrative access with Azure PIM ------------------------------------------------------------------------ 📢 Call to Action: Collective Intelligence I started this research alone. But disrupting the impact of the B-Family requires collective effort. If your organization or threat-hunting operations have observed additional logs, unusual network traffic, or alternative steganographic payload samples associated with the B-Family (BARADAI, BAVACAI, BASANAI, etc.), do not remain silent. Data Sharing You may share anonymized IoCs or log artifacts with us. and Direct Contact If you have technically significant observations or findings related to BARADAI analysis, you can contact me directly through my Webex profile. Webex Contact - email address removed for privacy reasons Our collective security depends on the aggregation of these small signals. --------------------------------------------- Sources and References For technical verification and further investigation, refer to the following resources: Threat Intelligence & Ransomware Reports CYFIRMA: Weekly Threat Intelligence Report (2026–05–08) Ransomware.live: BAVACAI Group & DLS Infrastructure PCrisk: BAVACAI | BAGAJAI | BASANAI Analysis Technical Foundations & MITRE TTPs CISA: MedusaLocker Advisory (AA22–181A) Picus Security: MedusaLocker TTPs and Simulation Barracuda: GhostFrame Phishing Kit Spotlight (2025–12–04) Detection & Response Tools Microsoft Sentinel: Official Shadow Copy Deletion Analytics Rule GitHub (Bert-JanP): Hunting Queries and Detection Rules No More Ransom: Global Decryption Tools Repository Cassandra MARE Independent Research Deniz Tektek: Stegomalware & Fileless Persistence (2026–04–05) https://medium.com/@deniizz/stegomalware-steganografi-ve-windows-registry-ile-kalıcılık-sağlayan-png-01e50849a218 Cassandra Community: Initial BARADAI Analysis (2026–05–14) https://medium.com/@cassandracommunity/baradai-ransomware-hayalet-yazılım-ı-parçalarına-ayırıyoruz-0c04bb008f73 This article has been published strictly for defensive purposes. All described techniques have been analyzed within the context of threat detection and defense. This is my debut article on the Microsoft Tech Community. I am Deniz Tektek, a Red Team Operator, Cybersecurity Analyst, and Founder of the Cassandra community. My work focuses on the intersection of human psychology, IoT security, and the development of zero-trust local AI agents. This article, “The Fileless Paradox,” is the inaugural entry in my "We Saw It Coming" threat intelligence series, where I document technical overlaps between independent research and active real-world threats. What’s Next? Vol. 2: "Invisible Exfiltration" — Analyzing how BARADAI’s C2 hides in plain sight. Vol. 3: "The Human Gateway" — Why your MFA and AI-driven defenses are currently being bypassed. Vol. 4: "Cracking BARADAI" — My ongoing decryption research. Connect With Me If you want to discuss these findings, exchange logs, or collaborate on security research, please check my profile bio for contact information or connect with me via LinkedIn. I welcome all technical perspectives and peer reviews. My LinkedIn: https://www.linkedin.com/in/deniz-t-91166438a Deniz Tektek — May 2026 © Deniz Tektek & Cassandra — All Rights Reserved. Originally published on Microsoft Tech Community. Cross-posted on Medium.DenizTektekMay 29, 2026Copper Contributor261Views1like0CommentsPasskey Sign in Method (Entra Account) missing in Security
Hi Microsoft Support we enable FIDO2 passkey in entraId. However, when we try to register the FIDO2 passkey on myaccount.microsoft.com -> Security -> Add a Sign-in Method -> Passkey is missing. Attached screenshot. For a personal account, the Passkey method is available at the same location, even though interface is slightly different than an Entra Id account. Attached screenshot for the personal account as well. Kindly guide us on where to register the passkey or if we need to enable certain settings in EntraId for the passkey to show up in sign-in methods. We have Auth Strengths enabled in EntraId for the particular user in question and this reflects in the Device Lockscreen during login on Entra Registred Device. Thanks ChandraSolvedchandraOMay 25, 2026Copper Contributor365Views0likes3CommentsHas anyone else been experiencing frequent Chrome freezes lately?
I've noticed that Google Chrome occasionally becomes completely unresponsive on several Windows 11 devices that are Microsoft Entra ID joined. In some cases, the browser freezes to the point where users are unable to recover without performing a hard reboot of the device. Unfortunately, the issue tends to reoccur after some time, even after restarting the machine. Has anyone else encountered similar behaviour in a Windows 11 and Entra ID-joined environment? If so, were you able to identify the root cause or find a reliable fix?NyathiMay 24, 2026Copper Contributor73Views0likes0CommentsBlackHat Community Interest Survey
Hey all! We’re planning Microsoft Security community circles, meetups, and AMA sessions during Black Hat week and would love your input on the topics and conversations most valuable to you. Please help us by filling out this form with your opinions (NO PERSONAL DATA COLLECTED): https://forms.cloud.microsoft/Pages/ResponsePage.aspx?id=v4j5cvGGr0GRqy180BHbR11eh_DyBlNCr6Pu5FQsI9ZUN1VQWTRDOTRZUVpQNEFLR05HMkg2RkFRTi4u Thank you!Trevor_RusherMay 20, 2026Community Manager83Views0likes0Comments
Tags
- cloud security989 Topics
- security785 Topics
- microsoft information protection520 Topics
- azure499 Topics
- information protection and governance486 Topics
- microsoft 365421 Topics
- microsoft sentinel347 Topics
- azure active directory240 Topics
- data loss prevention218 Topics
- microsoft 365 defender174 Topics