From late April 2026 to mid-June 2026, Microsoft Defender Experts observed increased ACR Stealer activity across customer environments. These campaigns are successfully using ClickFix lures to steal browser credentials, authentication tokens, and sensitive documents from enterprise environments. Successful compromise can expose browser credentials, session tokens, authentication artifacts, and sensitive enterprise data, potentially enabling account compromise, unauthorized access to cloud resources, and follow-on intrusion activity. Security teams should prioritize monitoring for ClickFix lures, suspicious WebDAV activity, obfuscated PowerShell execution, and attempts to access browser credential stores.

ACR Stealer is an information-stealing malware family reportedly offered through a malware-as-a-service (MaaS) model and associated with the rebranding of Amatera Stealer. During this period, two campaigns stand out, together appearing frequently in reviewed recent intrusions. Both begin the same way, with a ClickFix social engineering technique that tricks targets into running the threat actor’s command, but the intrusion chains that follow diverge in how they deliver payloads, establish execution, and evade detection.

The first campaign relies on WebDAV-delivered payloads, staged PowerShell, Python-based loaders and persistence, and, in some intrusions, blockchain-backed dead-drop command-and-control (C2) resolution. The second campaign takes a more fileless route, using MSHTA, obfuscated PowerShell, and steganography-assisted in-memory execution. Despite these differences, both campaigns ultimately pursue the same goal: stealing browser-stored credentials and other sensitive data for exfiltration.

These two campaigns represent some of the most prevalent ACR Stealer delivery campaigns observed by Defender Experts; however, they do not represent the full range of delivery methods used by this malware family. Attribution to ACR Stealer is based on the observed behavior and post-exploitation tradecraft, corroborated by open-source intelligence on the infrastructure associated with this malware family. Additional campaigns, infrastructure patterns, and execution chains are likely active, and organizations should treat the indicators and techniques described here as representative.

Microsoft Defender for Endpoint can help surface both campaigns through behavioral coverage for living-off-the-land execution, suspicious WebDAV and MSHTA activity, obfuscated PowerShell, scheduled-task persistence, in-memory payload execution, and browser credential theft. In this blog, we analyze both campaigns in detail, including their delivery mechanisms, post-exploitation tradecraft, indicators of compromise, hunting opportunities, and guidance to help defenders detect and disrupt related activity in their environments.

Campaign 1: WebDAV-based ClickFix with Python loaders and blockchain C2

Initial access

In this campaign, a ClickFix prompt, likely delivered through malvertising or SEO-manipulated search results, instructs the target user to run a command that launches cmd.exe. The command subsequently invokes rundll32.exe to load a DLL from a remote WebDAV share accessed over HTTPS. The WebDAV path commonly uses a GUID-based directory structure and filenames designed to resemble legitimate resources (for example, google.ct), enabling the activity to blend with expected network traffic and evade casual inspection.

We observed three variants of the initial execution command:

Variant 1: Direct rundll32 invocation

Variant 2: pushd-Mounted WebDAV Share

Variant 3: Headless and obfuscated pushd execution

Variants 2 and 3 are notable for their use of pushd, which transparently maps the remote WebDAV share to a temporary local drive prior to execution. This technique allows threat actors to execute remotely hosted content through what appears to be a local path, simplifying payload execution while reducing user awareness. In the more advanced variant, threat actors further enhance stealth by launching commands through conhost.exe –headless, suppressing visible console windows, and employing environment variable obfuscation with delayed variable expansion to conceal critical execution components such as pushd, rundll32, and the remote host name. Combined with minimized or headless execution, these techniques reduce user visibility, complicate static analysis and detection, and enable the infection chain to execute with minimal indication to the victim.

Execution, persistence, and evasion through process masquerading

Once rundll32.exe loads the DLL retrieved from the remote server, the malware establishes communication with threat actor-controlled infrastructure and executes a heavily obfuscated PowerShell script. The script employs excessive arithmetic no-ops, dead loops, fake control flow, and randomized variable names to hinder static analysis and evade signature-based detection.

The PowerShell script subsequently deploys another stage that functions as both a malware installer and a persistence mechanism. It:

• Downloads a ZIP-packaged payload from a remote server and extracts it into a deceptive directory under %LocalAppData%\Temp (for example, LogiOptionsPlus).
• Launches a Python script using a bundled pythonw.exe instance to avoid displaying a console window.
• Removes previous deployments and terminates running instances before installation, effectively operating as an updater.
• Establishes persistence through a hidden scheduled task disguised as a legitimate software update, ensuring execution at user sign-in.
• Copies timestamps from a trusted Windows binary (notepad.exe) to the deployed files and clears PowerShell command history to reduce forensic visibility.

Python loader launching the stealer

The Python component serves as a heavily obfuscated loader designed to conceal its true functionality until runtime. It employs multiple layers of defense against static analysis, including dynamic API resolution, encoded string reconstruction, junk-data removal, character shifting, string reversal, Base64 decoding, and zlib decompression. These techniques ensure that the embedded payload remains unreadable in its static form and is reconstructed only during execution, significantly hindering signature-based detection and automated analysis.

Once decoded, the final-stage payload functions as an in-memory shellcode loader. It extracts an archive file masquerading as a legitimate application installer, reads a file from the archive, and injects the payload into a system process. The loader allocates executable memory using VirtualAlloc, copies the payload into the allocated memory region, and transfers execution through the Windows Fiber API (ConvertThreadToFiber, CreateFiber, and SwitchToFiber). This technique facilitates stealthy in-memory execution while minimizing artifacts written to disk.

Credential theft and data staging for exfiltration

The malware (injected code) aggressively harvests information from browser credential stores. It invokes Windows Data Protection API (DPAPI) routines to decrypt locally stored browser passwords, cookies, and authentication tokens. It also enumerates files across the system, targeting PDFs, Microsoft 365 documents, and data stored in enterprise-synchronized directories such as OneDrive and SharePoint. The collected data is subsequently archived, indicating preparation for exfiltration.

Blockchain dead-drop C2 resolution

A notable variation in this campaign is the use of blockchain services for C2 resolution, utilizing a technique known as EtherHiding. While most intrusions rely on more conventional C2 mechanisms, a subset deploys an additional secondary Python loader that leverages blockchain services as dead-drop resolvers. When this loader executes, it has been observed communicating with public blockchain RPC endpoints and third-party Web3 node infrastructure, likely querying data stored on a decentralized public ledger to retrieve follow-up payloads or a C2 address.

By externalizing C2 information to the blockchain, operators could dynamically update infrastructure without modifying or redeploying the malware, significantly complicating detection and takedown efforts. This behavior was observed across both variants of the campaign.

Campaign 2: MSHTA-initiated PowerShell chain with steganographic payload delivery

The second campaign takes a distinctly different approach to both delivery and execution. Where Campaign 1 relies on disk-based artifacts (Python runtime, scheduled tasks, and masquerading binaries), this campaign achieves its objectives almost entirely through fileless, in-memory execution, making it harder to detect through file-based scanning and forensic analysis.

Initial access through MSHTA and ClickFix

The execution chain begins when the victim, directed through malvertising or SEO-manipulated search results, encounters a ClickFix prompt that triggers a command spawning MSHTA to fetch and execute remote HTA content from an threat actor-controlled domain. The embedded VBScript loader abuses COM objects to decode and execute encoded PowerShell content.

PowerShell downloader and obfuscation

The decoded PowerShell stage employs obfuscation techniques similar to those seen in Campaign 1: randomized variable names, arithmetic no-op operations, dead loops, misleading control flow, and custom encryption routines. Prior to contacting its next-stage infrastructure, the malware generates a victim-specific identifier and disables certificate validation. The retrieved content is executed directly in memory.

Steganography-based payload delivery

A notable technique in this campaign is the use of steganography to conceal malicious content inside a publicly hosted image. Instead of downloading a secondary script (as in Campaign 1), the malware retrieves a JPEG image from an image-hosting service.

Analysis of the script revealed custom routines that extract an embedded payload from image pixels, decrypt and decompress it, and execute it entirely in memory. The payload dynamically resolves APIs such as LoadLibrary, GetProcAddress, VirtualAlloc, CreateThread, and WaitForSingleObject at runtime to perform reflective shellcode execution. By combining steganography with in-memory execution, the malware minimizes on-disk artifacts and complicates both detection and analysis.

Credential theft, data collection, and exfiltration

Following execution, the malware accesses credential stores belonging to Chromium-based browsers, including Google Chrome and Microsoft Edge, specifically the Login Data and Web Data databases, alongside Windows DPAPI decryption activity. This behavior indicates attempts to recover stored browser credentials, session cookies, authentication tokens, and other sensitive user information.

The malware also enumerates and accesses multiple high-value PDF documents across Desktop and Downloads locations, suggesting targeted collection of potentially sensitive files. The combination of browser credential harvesting and systematic document access points to an information-stealing objective focused on staging credentials and valuable user data for exfiltration.

Mitigation and protection guidance

Microsoft recommends the following mitigations to reduce the impact of ClickFix lures, script-based payload delivery, credential theft, and post-compromise activity.

• Educate users to recognize ClickFix-style prompts, fake verification checks, and paste-and-run instructions as malicious, especially when they invoke command interpreters or script hosts such as cmd.exe, PowerShell, rundll32.exe, or mshta.exe.
• Reduce exposure to malvertising, SEO poisoning, and other web-based delivery chains by enforcing web filtering, blocking low-reputation or newly observed domains, and limiting access to remote content sources that are not required for business operations.
• Use application control and attack surface reduction rules to restrict PowerShell, Python, mshta.exe, rundll32.exe, and similar tools from launching untrusted or internet-delivered content, particularly from user-writable directories such as Downloads, Temp, and %LocalAppData%.
• Monitor for suspicious persistence and defense-evasion behavior, including scheduled tasks masquerading as software updates, timestomping, PowerShell history clearing, and execution chains that progress from remote content retrieval into PowerShell, Python, or shellcode-loading behavior.
• Investigate abnormal access to Chromium-based browser databases, DPAPI-related decryption activity, staged collection of Microsoft 365 documents or PDFs, and compression activity that may indicate credential theft or data staging for exfiltration.
• If compromise is suspected, isolate affected devices, rotate exposed credentials, revoke potentially compromised tokens, review persistence mechanisms, and investigate outbound connections to remote shares, image-hosting services, or other infrastructure used to resolve or retrieve follow-on payloads.
• Harden endpoints against credential theft by reducing reliance on browser-stored credentials, enforcing multifactor authentication and conditional access, and reviewing how privileged accounts access sensitive applications and synchronized enterprise data.
• Turn on cloud-delivered protection and behavior-based detections to help identify rapidly evolving threats, suspicious script execution, in-memory payload delivery, abuse of browser credential stores, and unusual child-process activity.
• Run endpoint detection and response (EDR) in block mode and enable automated investigation and remediation so post-breach detections are contained, and malicious artifacts can be removed with minimal delay.
• Harden PowerShell by enforcing appropriate execution policies, turning on script block logging, module logging, and transcription, and monitoring this telemetry for signs of malicious script activity.
• Turn on tamper protection and prevent local administrators from weakening antivirus protection through local policy or exclusion changes.

Microsoft Defender XDR detections

Microsoft Defender XDR customers can refer to the list of applicable detections below. Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog. 

Microsoft Security Copilot

Microsoft Security Copilot is embedded in Microsoft Defender and provides security teams with AI-powered capabilities to summarize incidents, analyze files and scripts, summarize identities, use guided responses, and generate device summaries, hunting queries, and incident reports.

Customers can also deploy AI agents, including the following Microsoft Security Copilot agents, to perform security tasks efficiently:

• Threat Intelligence Briefing agent
• Phishing Triage agent
• Threat Hunting agent
• Dynamic Threat Detection agent

Security Copilot is also available as a standalone experience where customers can perform specific security-related tasks, such as incident investigation, user analysis, and vulnerability impact assessment. In addition, Security Copilot offers developer scenarios that allow customers to build, test, publish, and integrate AI agents and plugins to meet unique security needs.

Threat intelligence reports

Microsoft Defender XDR customers can use the following threat analytics reports in the Defender portal (requires license for at least one Defender XDR product) to get current information available in the Defender portal about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide the intelligence, protection information, and recommended actions to help prevent, mitigate, or respond to associated threats found in customer environments:

• Amatera Stealer: Rebranded ACR Stealer With Improved Evasion, Sophistication
• Double Trouble: Latrodectus and ACR Stealer observed spreading via Google Authenticator Phishing Site

Microsoft Security Copilot customers can also use the Microsoft Security Copilot integration in Microsoft Defender Threat Intelligence, either in the Security Copilot standalone portal or in the embedded experience in the Microsoft Defender portal to get more information about this threat actor.

Advanced hunting queries

Microsoft Defender XDR customers can run the following advance hunting queries to find related activity in their networks:

Run the query below to identify suspicious commands executed through ClickFix-based activity observed while delivering this stealer

DeviceRegistryEvents
| where RegistryKey has "RunMRU"
| where (RegistryValueData has_all ("rundll32", "@ssl", " /c ", " start ") and (RegistryValueData matches regex @"\\\\[^\\]+@ssl\\[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12}\\\w+\.\w+,#1" or
RegistryValueData matches regex @"(?i)pushd \\\\[^\\]+@ssl\\[0-9a-fA-F]{8}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{4}-[0-9a-fA-F]{12} ")) 
or RegistryValueData has_all ("@ssl", " /c ", "conhost --headless ") and RegistryValueData contains "rundll32"

Run the query below to identify scheduled task creation used for persistence by a malicious PowerShell script

DeviceProcessEvents
| where InitiatingProcessFileName =~ "powershell.exe"
| where InitiatingProcessCommandLine has_all ("-Command", "powershell")
| where ProcessCommandLine has_all ("schtasks", " /run /tn ", " Autoupdate ") and ProcessCommandLine matches regex "[0-9]{8}"

Run the query below to identify suspicious MSHTA launch through PowerShell

DeviceProcessEvents
| where InitiatingProcessParentFileName has "explorer.exe"
| where InitiatingProcessFileName =~ "powershell.exe" and InitiatingProcessCommandLine in~ ('"PowerShell.exe" ', '"PowerShell.exe"')
| where ProcessCommandLine has_all ('"mshta.exe" https://') and ProcessCommandLine matches regex "/[0-9]{7}"

MITRE ATT&CK techniques observed

The following mapping summarizes the primary tactics and techniques observed across the two ACR Stealer intrusion chains. The mapping is intended to help defenders align observed behaviors with existing detection coverage, response playbooks, and hunting priorities.

Indicators of compromise (IOC)

References

• Intelligence Insights: May 2026 | Red Canary
• Possible ACR Stealer From Page Impersonating Claude

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The post ACR Stealer: Two observed intrusion chains amid increased threat activity appeared first on Microsoft Security Blog.

On July 14, 2026, Microsoft Threat Intelligence identified a coordinated supply chain compromise of the @asyncapi npm organization, a widely used set of packages for the AsyncAPI specification and code generation. Five package versions across four package names were republished within roughly ninety minutes, each carrying the same maliciously injected loader: @asyncapi/specs (in both the 6.11.2-alpha.1 prerelease and 6.11.2 stable release), @asyncapi/generator@3.3.1, @asyncapi/generator-components@0.7.1, and @asyncapi/generator-helpers@1.1.1.

Because @asyncapi/specs is a transitive dependency of numerous AsyncAPI tooling packages, this attack affected developer workstations, CI/CD pipelines, container builds, or production services that resolved and imported the affected versions during the exposure window. Unlike the more common postinstall-hook supply-chain pattern, this campaign executes at module-load (import/require) time. When any consuming build or application imports a poisoned package, the injected block runs immediately. Because the trigger is an import rather than an install script, the common npm install –ignore-scripts mitigation does not neutralize it. The second stage decrypts and evaluates a Miasma modular runtime with active command and control (C2), persistence, and decentralized fallback channels. Although disabled in this instance, credential-harvesting, propagation, and additional high-risk modules could be enabled through persistence.

Microsoft Defender Antivirus detects and blocks malicious artifacts as Trojan:JS/MiasmStealer.SC  and Trojan:Script/Supychain.A. Microsoft Defender for Endpoint provides behavioral coverage for the suspicious detached Node.js process spawn, IPFS retrieval, and persistence activity. Organizations should immediately remove all five affected versions, purge npm and Yarn caches, hunt for sync.js under the NodeJS masquerade directories, block outbound connections to 85.137.53[.]71 on ports 8080, 8081, and 8091, and rotate all credentials accessible from any environment that imported the compromised packages. Detailed hunting queries, indicators of compromise, and mitigation guidance are provided in the succeeding sections.

Attack chain overview

The compromise originated from a pwn request against asyncapi/generator. A misconfigured GitHub Actions workflow (pull_request_target) executed attacker-controlled pull-request (PR) code, exposed the asyncapi-bot personal access token (PAT), and enabled unauthorized pushes to auto-publish branches. The legitimate GitHub Actions OpenID Connect (OIDC) release workflows then published the poisoned packages under the automated identity npm-oidc-no-reply@github[.]com, producing artifacts with valid provenance signatures built from unauthorized source commits.

The campaign progressed through six phases, shown in Figure 1:

1. Pipeline compromise. The attacker exploited a vulnerable GitHub Actions workflow to steal a privileged bot token.
2. Code injection. Heavily obfuscated loaders were inserted into one source file per package.
3. Staged release. An alpha prerelease was followed by a stable release 24 minutes later, with a byte-identical payload, expanding blast radius.
4. Delivery. Consumers pulled poisoned versions through normal npm and Yarn dependency resolution; –ignore-scripts was not effective.
5. Import-time execution. require() or import triggered the malicious main(), which spawned a hidden detached child process.
6. IPFS second-stage fetch. The child downloaded sync.js from IPFS and wrote it to an OS-specific “NodeJS” masquerade directory.

The Miasma runtime provided encrypted bootstrap, persistence, C2 communication, data return paths, and resilient discovery via Nostr, Ethereum, BitTorrent DHT, libp2p, and IPFS. Six additional capability modules (credential harvest, encrypted exfiltration, supply-chain propagation, metamorphic generation, AI-tool poisoning, and sandbox evasion) were implemented but disabled in this build.

How the attack started: GitHub Actions pwn request

The attack chain began with a malicious pull request targeting the asyncapi/generator repository’s docs-preview automation. Opened as PR #2155, it carried the attacker-controlled commit 47be388, timestamped 05:08:58 UTC on July 14. The associated Docs Preview (Netlify) workflow started at 05:11:05 UTC.. Although the PR and source fork were later removed, the workflow record remains available.

The pull request PR #2155 targeted manual-netlify-preview.yml, which combined two unsafe choices: it used pull_request_target, placing the job in the base repository’s security context, and it checked out the pull request’s untrusted head commit. The run had a broadly privileged GITHUB_TOKEN, checkout credentials persisted in the local Git configuration until post-job cleanup (the default behavior of actions/checkout), and steps that referenced repository secrets.

The submitted MDX contained code was designed to retrieve JavaScript from rentry[.]co/elzotebo999 and evaluate the response. The public log confirms that the malicious commit was processed by the privileged workflow, but it does not show whether the rentry[.]co web request succeeded or whether a credential was stolen. Later push records identify asyncapi-bot as the authenticated actor. Together, these records establish that the vulnerable workflow ran before the bot-authenticated pushes, but they do not establish how the credential was obtained.

The underlying workflow weakness had been identified before the compromise. On April 29, a proof-of-concept examined whether untrusted pull-request content could be executed in the privileged docs-preview workflow. A May 17 proposal then sought to separate untrusted build activity from steps that received repository secrets and was still under review when the incident occurred.

Trusted publishing became the delivery mechanism

Once the attacker could push commits as asyncapi-bot, there was no need to compromise npm or construct a separate publishing channel. The attacker could ride the project’s normal release path and let its trusted pipeline do the distribution. Commit 3eab3ec carries a timestamp of 06:58:42 UTC, while a surviving push-triggered workflow started at 07:05:42 UTC. Its message, “fix: test release workflow on next”, matched the release workflow’s commit-message condition. The legitimate release-with-changesets.yml workflow then published three poisoned packages at approximately 07:10 UTC.

A closely linked compromise subsequently affected asyncapi/spec-json-schemas. The malicious lineage first triggered workflows on alpha between 07:56 and 08:04 UTC. The same malicious commit was later pushed to master at approximately 08:14 UTC, followed by a child commit at 08:28 UTC. The legitimate if-nodejs-release.yml workflow published @asyncapi/specs@6.11.2-alpha.1 at 08:06 UTC and @asyncapi/specs@6.11.2 at 08:30 UTC.

All five malicious versions were published through npm trusted publishing using GitHub OIDC and carried valid provenance attestations. The attestations accurately identified the legitimate repositories, commits, and workflows that created the packages, even though the triggering commits were unauthorized.

The payload operates in multiple stages, each designed to increase evasion and ensure resilient execution. Stage 0 establishes stealth by declaring no npm lifecycle hooks. Stage 1 executes the loader at require-time and spawns a hidden child process. Stage 1b deobfuscates the IPFS fetch logic and downloads sync.js. Stage 2 decrypts the ~8.2 MB encrypted bundle through three cryptographic layers. Stage 3 initializes the full Miasma modular runtime with C2, persistence, and decentralized fallback channels.

Stage 0: No lifecycle hooks declared

The absence of lifecycle hooks is a deliberate evasion choice. Security tooling that focuses on preinstall/postinstall auditing will not flag these packages. All affected packages declared no preinstall, install, or postinstall hooks in package.json. This bypassed hook-focused scanners and left import-time execution as the real trigger path.

Stage 1: Import-time loader

The loader executes the moment any application imports the compromised module; no user action beyond dependency resolution is required. The attacker placed the same bootstrap pattern in each package’s exported entry path, so normal application startup would trigger execution automatically.

• @asyncapi/specs → index.js
• @asyncapi/generator → lib/templates/config/validator.js
• @asyncapi/generator-helpers → src/utils.js
• @asyncapi/generator-components → lib/utils/ErrorHandling.js

spawn('node', [payloadPath], {
   detached: true,
   stdio: 'ignore',
   windowsHide: true,
 }).unref();

Stage 1b: IPFS fetch

The inner payload reveals hard-coded IPFS content identifiers and OS-aware drop logic. This intermediate stage reconstructs the transport routine at runtime, so the larger second stage never appears in cleartext in the published package.

const FILE_URL = 'hxxps://ipfs[.]io/ipfs/';
 const FILE_NAME = 'sync.js';
 function getTargetDirectory() {
   if (process.platform === 'win32') return '%LOCALAPPDATA%\NodeJS';
   if (process.platform === 'darwin') return '~/Library/Application Support/NodeJS';
   if (process.platform === 'linux') return '~/.local/share/NodeJS';
   return '~/.config/NodeJS';
 }

Stage 2: Encrypted payload (sync.js)

Despite appearing cryptographically sophisticated, the entire decryption chain uses static embedded key material, meaning the runtime can be recovered offline without execution. The layered design primarily increases analyst effort; every secret required to unwrap the bundle ships inside the loader.

• sync.js is ~8.2 MB; all key material is static and embedded.
• HKDF-SHA256 uses master string rt-vault-master-key-32b-aaaaaaaa and info string rt-file-key.
• AES-256-GCM uses IV = first 12 bytes and auth tag = last 16 bytes of the blob.
• The decrypted string is ROT-94de-rotated and then executed with eval().

Stage 3: Miasma runtime

The  runtime is a command framework identified as M-RED-TEAM v6.4 with campaign configuration miasma-train-p1. In this build’s configuration, persistence and C2 are active, but data collection and propagation modules remain dormant. The runtime supports traditional remote access trojan (RAT) commands, including directory listing, file retrieval, file upload, remote shell execution, proxying, and data exfiltration. Persistence is installed through platform-specific mechanisms: a Windows HKCU Run key (miasma-monitor), a Linux systemd user unit (miasma-monitor.service), and macOS shell RC injection (.zshrc, .bashrc, or .bash_profile).

• Recovered identifiers: M-RED-TEAM v6.4, miasma-train-p1, and miasma-test-org.
• Persistence: Win HKCU Run value miasma-monitor, Linux miasma-monitor.service, and macOS user-space shell/launch persistence.
• Primary endpoints: 85.137.53[.]71:8080 (C2), 85.137.53[.]71:8081 (upload), 85.137.53[.]71:8091 (management).
• Fallback channels include Nostr, Ethereum, BitTorrent DHT, libp2p, and IPFS.
• Disabled in the analyzed build: recon, propagation, AI-poisoning, metamorphic generation, and evasion.

Credential harvesting (disabled in this build)

The framework contains broad credential-access code targeting secrets across major platforms that a developer or continuous integration and continuous delivery (CI/CD) system might access, including browser-saved passwords from multiple browsers.

The framework targets over 100 environment variable names across source control (GITHUB_TOKEN, GITLAB_TOKEN), npm (NPM_TOKEN, NODE_AUTH_TOKEN), AWS (AWS_ACCESS_KEY, AWS_SECRET_ACCESS_KEY), Azure (AZURE_CLIENT_SECRET), GCP (GCLOUD_SERVICE_KEY), container/Kubernetes (DOCKER_TOKEN, K8S_AUTH_TOKEN), secrets managers (DOPPLER_TOKEN, VAULT_TOKEN), and AI platforms (ANTHROPIC_API_KEY, OPENAI_API_KEY).

Credential files targeted from disk include .npmrc (npm tokens), .aws/credentials (AWS keys), kubeconfig (Kubernetes API), id_rsa/id_ed25519 (SSH keys), .vault-token (HashiCorp Vault), .netrc (Git/HTTPS auth), .docker/config.json (Docker registry), and google_credentials.json (GCP service accounts). When a GITHUB_TOKEN is available, the framework can enumerate accessible repositories and CI/CD context through GitHub APIs.

Mitigation and protection guidance

Review dependency trees, lockfiles, artifact repositories, and CI caches for the five compromised versions, including transitive references.

Pin known-good versions: @asyncapi/specs 6.11.1 or earlier, @asyncapi/generator 3.3.0, @asyncapi/generator-components 0.7.0, and @asyncapi/generator-helpers 1.1.0.

Do not rely on npm install –ignore-scripts as a mitigation; this campaign executes when the module is imported, not through a lifecycle hook.

Purge npm and yarn caches on affected developer endpoints and build hosts, especially if the compromised tarballs were written into shared CI caches.

Hunt for sync.js and the NodeJS masquerade directory on endpoints, and investigate any detached Node.js execution that references the IPFS CID or the sync.js file name.

Block or alert on retrieval of the specific IPFS CID and monitor for network connections to 85.137.53[.]71 on ports 8080, 8081, and 8091.

Rotate credentials and secrets from a clean host if a build system or workstation imported a compromised version, because second-stage execution can expose tokens and build integrity.

Ensure that Microsoft Defender Antivirus cloud-delivered protection, Microsoft Defender for Endpoint telemetry, and Microsoft Defender XDR investigation workflows are enabled across developer and CI assets.

Update to NPM CLI to npm CLI v11.10.0+ or Use the NPM CLI min-release-age feature.

Organizations that do not rely on IPFS for business operations can reduce their attack surface by blocking public IPFS gateways (ipfs.io, dweb.link, cloudflare-ipfs.com, and others) at the network perimeter. This proactive measure removes an increasingly common payload delivery channel used in supply chain campaigns without affecting standard development workflows.

Organizations that produce software artifacts should also review their own release hardening. Because this incident appears consistent with CI/CD pipeline abuse through GitHub Actions OIDC publishing, defenders should review token scopes, workflow approvals, protected environments, release provenance, and anomaly detection around automated package publication. Supply chain response cannot stop at host triage; it must also include verification that the release process itself has not been subverted.

After remediation, validate recovery deliberately. Rebuild affected projects from a known-good dependency baseline, confirm that compromised hashes are absent from package caches and artifact stores, and review endpoint telemetry for any lingering sync.js, NodeJS directory artifacts, or suspicious node child processes. For development organizations that share base images or golden build runners, rebuild those images as well so future jobs do not silently inherit poisoned caches or post-compromise persistence.

Indicators of compromise

Microsoft Defender XDR detections

Microsoft Defender XDR customers can refer to the list of applicable detections below. Microsoft Defender XDR coordinates detection, prevention, investigation, and response across endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.

Advanced hunting queries

Microsoft Defender XDR customers can run the following advanced hunting queries to find related activity in their networks:

Persistence drop and detached spawn

// Query 1: sync.js dropped under a NodeJS directory or related detached execution
 union isfuzzy=true
 (
 DeviceProcessEvents
 | where Timestamp > ago(30d)
 | where (ProcessCommandLine has "sync.js" and ProcessCommandLine contains_cs "NodeJS")
     or ProcessCommandLine has "Qmet4fhsAaWMBUxNDfREHwgiyDeSWy4YSYs9wiKUW5jGyf"
 | project Timestamp, DeviceName, Evidence = ProcessCommandLine, Initiator = InitiatingProcessCommandLine, EventType = "Process"
 ),
 (
 DeviceFileEvents
 | where Timestamp > ago(30d)
 | where FileName == "sync.js" and FolderPath contains_cs "NodeJS"
 | project Timestamp, DeviceName, Evidence = strcat(FolderPath, "\\", FileName), Initiator = InitiatingProcessFileName, EventType = "File"
 )

IPFS CID retrieval

// Query 2: outbound retrieval of the IPFS second stage
DeviceNetworkEvents
| where Timestamp > ago(30d)
| where RemoteUrl has "ipfs.io"
| where RemoteUrl has "Qmet4fhsAaWMBUxNDfREHwgiyDeSWy4YSYs9wiKUW5jGyf"
| project Timestamp, DeviceName, RemoteUrl, RemoteIP, InitiatingProcessFileName

Poisoned package artifacts in caches

// Query 3: presence of a poisoned tarball in caches
DeviceFileEvents
| where Timestamp > ago(30d)
| where SHA256 in (
    "d425e4583cc6185d41e95c45eda00550045a5d1919b9a012236a4520d009dbd7",
    "9b2e65db653ca8575c9b10eefb9a80c6006404812c2ec212bf5675e3c690233b",
    "bfaeb987faa6de2b5a5eb63b1233d055215b09b0349a9394f2175fd7cdf385e4",
    "082d733db0687dcd768104972b065d4b58cb1e6043688c6c20fa3702337f36ab",
    "34014776d3d3ff11bc4439b02fd7ac0f02a887eb3a052eeafff236e2f6db8ad1")
| project Timestamp, DeviceName, FolderPath, FileName, InitiatingProcessFileName

Suspicious Node.js execution

DeviceProcessEvents
| where Timestamp > ago(3d) 
| where FileName in~ ("node", "node.exe")
| where ProcessCommandLine has "node.exe -e \"const _0x5af5e1" or ProcessCommandLine has "node -e \"const _0x5af5e1"
| project Timestamp, DeviceName, FileName, FolderPath, ProcessCommandLine, InitiatingProcessFileName, InitiatingProcessFolderPath

Microsoft Security Copilot

Security Copilot customers can use the standalone experience to create their own prompts or run prebuilt promptbooks to automate investigation and response tasks related to this threat. Useful promptbooks for this activity include Incident investigation, Microsoft User analysis, Threat actor profile, Threat Intelligence 360 report based on MDTI intelligence, and Vulnerability impact assessment. Some promptbooks require access to Microsoft Defender XDR, Microsoft Sentinel, or related Microsoft security plugins.

For this campaign, Security Copilot can help analysts summarize affected devices, pivot from the package hashes to endpoint evidence, identify hosts that communicated with the IPFS path or C2 infrastructure, and build remediation actions such as cache purge, credential rotation, and containment sequencing for impacted developer systems and build runners.

Threat intelligence reports

Microsoft customers can use Microsoft Defender XDR Threat analytics and related Microsoft threat intelligence reporting to stay current on the malicious activity, indicators, detection coverage, and recommended response actions associated with this compromise. These reports provide investigation context, protection guidance, and updated intelligence that security teams can use to prevent, mitigate, or respond to related activity in customer environments.

As with other active supply-chain investigations, defenders should monitor for updated intelligence on package status, additional affected versions, infrastructure changes, and newly surfaced post-compromise tradecraft. Microsoft will continue to incorporate validated indicators and detections into Microsoft security products as the investigation evolves.

Learn more

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.   

• Microsoft 365 Copilot AI security documentation 
• How Microsoft discovers and mitigates evolving attacks against AI guardrails 
• Learn more about securing Copilot Studio agents with Microsoft Defender  
• Evaluate your AI readiness with our latest Zero Trust for AI workshop.

The post Unpacking the AsyncAPI npm supply chain compromise and import-time payload delivery appeared first on Microsoft Security Blog.

In a series of campaigns observed between mid-2025 and mid-2026, Microsoft identified threat actor activity with overlapping tradecraft commonly associated with ShinyHunters, including voice phishing (vishing) and supply chain compromise, to target customer SaaS-based applications such as Salesforce instances. The threat actors abused trusted OAuth relationships for unauthorized access, data exfiltration, and persistence.

Two primary intrusion paths were observed including vishing techniques targeting OAuth consent and supply chain compromise through trusted workflows and integrations such as Salesloft and Gainsight. Abuse of these access paths led to inherited user and application privileges, allowing successful enumeration and querying of customer relationship management (CRM) records while evading conventional authentication detections. These intrusion paths often led to persistent access and exfiltration of data at scale. This tradecraft highlights how a single entry point can rapidly expand to greater enterprise impacts.

Microsoft observed activity associated with these techniques in many tenants from various industries such as retail, education and manufacturing. These findings reinforce the importance of monitoring OAuth-connected applications, validating third-party integrations, reviewing configurations, and enabling Salesforce event monitoring. Leveraging this data, Microsoft consulted with Salesforce to improve granularity in telemetry for Defender for Cloud Apps with near-real-time detection, offering connected application attribution and expanded application permission insights. This activity was not the result of a vulnerability inherent to Salesforce. Rather, the threat actors abused trusted OAuth relationships for unauthorized access, data exfiltration, and persistence.

Attack chain overview

Threat actor campaigns targeting Salesforce customers and using tradecraft associated with ShinyHunters pose a high-impact risk to sensitive data and downstream SaaS ecosystems. These campaigns abuse OAuth trust relationships to operate within pre-existing, legitimate workflows.

Observed activity can be grouped into two primary intrusion paths:

Voice‑phishing-driven OAuth consent abuse

In campaigns beginning in mid-2025, the threat actors conducted vishing attacks impersonating IT support personnel. Threat actors socially engineered employees into authorizing attacker-controlled connected apps within their Salesforce tenant. In several confirmed cases, threat actors guided users through the OAuth consent workflow to grant access to a malicious application disguised as a legitimate Salesforce Data Loader tool. After users granted consent, these highly privileged OAuth applications enabled threat actors to perform API calls on behalf of the victim user, facilitating:

• Enumeration of Salesforce instances belonging to targeted organizations
• Persistent access to Salesforce CRM data
• Possible lateral movement into other SaaS platforms through discovered credentials

This intrusion path exploits the OAuth authorization flow of trusted SaaS services rather than relying on malware or credential replay. Threat actors exfiltrate data through sanctioned application access inherited from user privileges.

SaaS supply‑chain compromise targeting trusted integrations

Following initial access campaigns, threat actors  escalated into supply‑chain-driven attacks targeting third‑party SaaS vendors offering popular solutions that integrate with Salesforce, often using OAuth tokens. In August 2025, compromised Salesloft Drift credentials enabled attackers to obtain connection secrets used by downstream SaaS applications, enabling the use of OAuth tokens in multiple customer Salesforce instances.

A subsequent campaign in November 2025 targeted Gainsight-published applications integrated with Salesforce, allowing attackers to leverage trusted external connections to maintain persistent API access in multiple Salesforce customer instances. These activities often appeared indistinguishable from legitimate integration behavior. Threat actors performed discovery, bulk data queries, and mass exfiltration of sensitive CRM records, including accounts, contacts, and service case data, without generating traditional sign-in anomalies.
More recently, in June 2026, the market intelligence platform Klue experienced an incident where a threat actor, Storm-3138, gained access to its system.  Credentials used to access Salesforce customer instances were used in the same fashion, to discover, query, and exfiltrate data.

Improving visibility into Salesforce OAuth abuse

For customers using Salesforce Shield: Event Monitoring, the upgraded Microsoft Defender for Cloud Apps Salesforce connector onboards the Real-Time Event Monitoring (RTEM) framework, enabling faster detection and investigation of Salesforce-based attacks.

Investigations into these campaigns exposed a recurring challenge for security teams: malicious activity often appeared indistinguishable from legitimate Salesforce usage because threat actors operated through trusted identities, approved OAuth applications, and authorized integrations. Traditional authentication-focused detections frequently provided limited visibility into the resulting application activity.

To improve investigation and detection of these scenarios, Microsoft expanded Salesforce visibility in Defender for Cloud Apps through additional event telemetry, connected application attribution, and enhanced application permissions insights. These capabilities help security teams identify suspicious OAuth activity, investigate potentially compromised integrations, and better understand how access was obtained and used within customer Salesforce instances.

Key capabilities include:

• Near-real-time visibility into Salesforce security and activity events.
• Connected application attribution, including application identity and granted OAuth scopes.
• Expanded identity, session, and API activity context to support investigations.
• Improved correlation within Microsoft Defender to help identify suspicious activity spanning identities, applications, and SaaS environments.

Together with Salesforce Shield: Event Monitoring, these capabilities help security teams investigate suspicious OAuth activity, validate the legitimacy of connected applications, and better understand the potential impact of a compromise.

New posture and governance capabilities for connected OAuth apps

While improved detection is critical, recent incidents have also highlighted the need for stronger preventive controls and ongoing governance of OAuth-connected applications. To address this, Microsoft Defender introduces new posture capabilities for connected and external client apps in Salesforce. Security teams can gain visibility into each OAuth app and its non-human identity, prioritize risk, and reduce the attack surface.

Deep visibility into app permissions and access

Microsoft Defender provides comprehensive visibility into all Salesforce-integrated connected and external client apps, including granted OAuth scopes and privileges.

Highly privileged apps

Security teams often struggle to identify applications with powerful administrative or sensitive permissions. The highly privileged apps insight highlights applications that have been granted elevated scopes, enabling quick identification of apps that may pose significant risk.

Additionally, security teams can use permission-based filters to identify apps with specific high-risk scopes and validate whether such access is justified.

Unused apps

Organizations often create applications for temporary or one-time use, but those applications are rarely removed afterward. These unused apps continue to retain permissions, creating unnecessary exposure. With the recent changes, Defender now allows security teams to identify applications that have been inactive for extended periods (for example, 90 days or more), making it easy to review and revoke access where appropriate to reduce the attack surface.

Risk-based prioritization of connected apps

To further streamline investigation and response, Defender introduces a comprehensive risk scoring model for connected applications. Each application is assigned a numerical risk score [0-100] based on multiple risk indicators, such as usage patterns, permission sensitivity, and behavioral signals. This allows security teams to prioritize efforts effectively and focus on applications that require immediate attention. Security teams can create custom policies based on risk thresholds to trigger alerts, actions, and notifications.

Risk score investigation

To further investigate the specific Non-Human identity risk details, the factors contributing to the risk score are available in Non-Human Identities Risk score tab.

Mitigation and protection guidance

Microsoft recommends the following mitigations to reduce the impact of this threat. Check the recommendations card for the deployment status of monitored mitigations.  

• Microsoft Defender for Cloud Apps customers can connect their Salesforce instance to get improved visibility and threat detection capabilities.
• Implement best practices as recommended by Salesforce.
• Secure your Experience Cloud Guest User access
• Familiarize yourself with and proactively monitor Salesforce event logs.

Microsoft Defender detections

Microsoft Defender customers can refer to the list of applicable detections including new detections powered by the upgraded Microsoft Defender for Cloud Apps Salesforce connector. Microsoft Defender coordinates detection, prevention, investigation, and response for endpoints, identities, email, and apps to provide integrated protection against attacks like the threat discussed in this blog.

Customers with provisioned access can also use Microsoft Security Copilot in Microsoft Defender to investigate and respond to incidents, hunt for threats, and protect their organization with relevant threat intelligence.

Threat intelligence reports 

Microsoft customers can use the following reports in Microsoft products to get the most up-to-date information about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer Salesforce instances.

• OSINT Profile: ShinyHunters Calling: Financially Motivated Data Extortion Group Targeting Enterprise Cloud Applications

Advanced hunting

NOTE: The sample queries let you search one week of events. To inspect events and hunt for threat actor-related indicators over a longer period, go to the Advanced Hunting page > Query tab, and use the calendar dropdown to set the time range to Last 30 days (the maximum for raw data).

Hunt for Salesforce connected-app activity from suspicious infrastructure

CloudAppEvents
| where Application == "Salesforce"
| where ActionType in ("ApiTotalUsage", "API Event")
| extend ConnectedAppId = tostring(
    coalesce(
        RawEventData.CONNECTED_APP_ID, // from ApiTotalUsage 
        RawEventData.ConnectedAppId // from API Event
    )
)
| where isnotempty(ConnectedAppId)
| where array_length(UncommonForUser) > 0 // at least 1 attribute is flagged as uncommon

Hunt for API activity associated with connected apps and relevant user ids

CloudAppEvents
| where Application == "Salesforce"
| where ActionType in ("ApiTotalUsage", "API Event")
| extend SalesforceUserId=coalesce(tostring(RawEventData.USER_ID), tostring(RawEventData.UserId))
| extend ConnectedAppName=tostring(RawEventData.CONNECTED_APP_NAME)  // Connected App Name is not available on the ApiEvent event
| summarize count() by AccountObjectId, AccountId, AccountDisplayName, SalesforceUserId, IPAddress, UserAgent, ConnectedAppName

Hunt for anomalous report export / large data access

CloudAppEvents
| where Application == "Salesforce"
| where ActionType  == "ReportExport"
| extend SalesforceUserId = tostring(RawEventData.USER_ID)
| summarize Events=count() by AccountObjectId, AccountId, AccountName, SalesforceUserId, IPAddress, UserAgent

Pivot from a suspicious connected app (name/id) to impacted users and actions

CloudAppEvents
| where Application == "Salesforce"
| where RawEventData has ""
| project Timestamp, AccountId, AccountDisplayName, ActionType, IPAddress, UserAgent, RawEventData
| order by Timestamp desc

Audit queries to verify what objects users are accessing

CloudAppEvents
| where Application == "Salesforce"
| where ActionType == "UniqueQuery"
| extend 
    QueryText = tostring(RawEventData.QUERY_IDENTIFIER), // Full query text
    QueryObject = extract(@"(?i)\bfrom\s+([^\s]+)", 1, tostring(RawEventData.QUERY_IDENTIFIER)), // Extract just the target object
    SalesforceUserId = tostring(RawEventData.USER_ID)
| where QueryText != "SOQL"
| project Timestamp, AccountDisplayName, SalesforceUserId, QueryObject, QueryText

Hunt for users with very high Defender risk score signing into Salesforce

let VeryRiskyUsers = IdentityInfo
| where RiskScore >= 90
| distinct AccountObjectId
CloudAppEvents
| where Application == "Salesforce"
| where ActionType has "sso" or ActionType has "saml"
| where AccountObjectId in (VeryRiskyUsers)
| project Timestamp, AccountObjectId, AccountDisplayName, ActionType, UserAgent
| order by Timestamp desc

Indicators of compromise (IOC)

After further investigation, Microsoft has discovered that two of the reported IP addresses belong to a researcher conducting attack research. We have removed the IOCs from this report.

MITRE ATT&CK techniques observed

Initial Access

• T1566.004 Phishing: Voice Phishing: Impersonating IT support to get victims to grant access.
• T1528 Steal Application Access Token: Using stolen OAuth tokens from Salesloft and Gainsight.

Persistence

• T1671 Cloud Application Integration: Leveraging Connected Apps for access to a customer Salesforce environment.

Collection

• T1213.004 Data from Information Repositories: Customer Relationship Management Software: Stealing data from a customer Salesforce environment.

Exfiltration

• T1567 Exfiltration Over Web Service: Usage of the fake Data Loader application to steal data.

This research is provided by Microsoft Defender Security Research, Shruti Ranjit, Doug Cranston, Anand Deshpande, Ronen Rafaeli, and with contributions from members of Microsoft Threat Intelligence.

Learn more

For the latest security research from the Microsoft Threat Intelligence community, check out the Microsoft Threat Intelligence Blog.

To get notified about new publications and to join discussions on social media, follow us on LinkedIn, X (formerly Twitter), and Bluesky.

To hear stories and insights from the Microsoft Threat Intelligence community about the ever-evolving threat landscape, listen to the Microsoft Threat Intelligence podcast.

Review our documentation to learn more about our real-time protection capabilities and see how to enable them within your organization.   

• Microsoft 365 Copilot AI security documentation 
• How Microsoft discovers and mitigates evolving attacks against AI guardrails 
• Learn more about securing Copilot Studio agents with Microsoft Defender  
• Evaluate your AI readiness with our latest Zero Trust for AI workshop.

The post Defending SaaS-based applications against ShinyHunters OAuth abuse appeared first on Microsoft Security Blog.