As threat actors operationalize AI to accelerate attacks, they are also leveraging the wider global interest around AI itself as a social engineering lure. In recent months, Microsoft Threat Intelligence has observed a growing number of campaigns that impersonate the branding of popular AI platforms such as ChatGPT, Microsoft Copilot, DeepSeek, and Anthropic’s Claude as lures. These campaigns, which don’t represent compromise of services, span phishing, malvertising, and search engine optimization (SEO)-driven attacks that ultimately lead to credential theft, financial fraud, or malware infection.

Threat actors are quick to capitalize on highly anticipated launches or emerging trends, leveraging trusted branding and exploiting user curiosity to improve the success rates of their campaigns. Despite the AI-themed lures, however, these campaigns combine longstanding tactics, such as urgency-driven messaging, abuse of trusted services, and multi-stage redirection chains that require user interaction to evade detection.

While traditional lures like invoices, payment notifications, or delivery alerts remain effective and continue to be widely used, AI-themed lures reflect a shift in social engineering that is likely to persist as a long-term tactic used by threat actors, from cybercriminal groups to nation states. Notably, Microsoft Threat Intelligence has observed the initial access broker Storm-3075 employing AI-themed malvertising to deliver payloads, including malware signed by the malware-signing-as-a-service (MSaaS) offering attributed to the financially motivated threat actor Fox Tempest, on behalf of multiple downstream actors.

This blog details several of the campaigns observed by Microsoft Threat Intelligence in the past few months that used AI brands and references as lures, and provides guidance to help users and organizations detect, mitigate, and respond to these threats. Importantly, Microsoft believes that the activity noted in this blog is purely abuse of AI brand names as lures, not reflecting a compromise of any referenced vendor. As threat actors scale their operations with AI, organizations should leverage AI-powered security capabilities to enhance visibility, automate detection, and accelerate response across email, identity, and endpoint surfaces.

ChatGPT-themed lure leads to phishing kit collecting credit card data

On May 5, 2026, Microsoft detected a ChatGPT-themed phishing attack that delivered malicious URLs leading to phishing pages that collected credit card and personal information such as names and addresses. This phishing activity, which consisted of 4,500 emails sent to targets in South Africa (97%), was part of a broader campaign using similar themes and infrastructure. We also observed this campaign delivering as much as 100,000 emails on a single day to targets in Switzerland, Austria, and South Africa affecting a broad range of industries, including higher education and professional services.

The emails used the sender display name ChatGPT and the subject “To ensure your ChatGPT Plus continues to work – please update your payment method”. The emails posed as an urgent request to update the ChatGPT Plus subscription payment method. They warned the recipient that if a new payment method was not provided within seven days, the account would be downgraded to a free plan. A ChatGPT logo was prominently displayed at the top of the email body.

The phishing email contained a clickable Update payment method button, which did not directly send users to the attacker-controlled site. Instead, users were redirected through a series of legitimate and abused redirector hops. This is a common technique used by threat actors to exploit the reputation of trusted domains and bypass email filters, evade detection, and track victim engagement.

Targets were first directed to grupoconstat[.]bitrix24[.]com[.]br (a legitimate customer relationship management (CRM) service), which redirected to awstrack[.]me (an Amazon domain used for tracking email opens and clicks), which in turn redirected to a Rebrandly URL (a legitimate but often abused URL shortener service). Targets were finally sent to a likely legitimate but compromised domain legendarytrendsbay[.]shop where the threat actor had placed the phishing page in the /ChatGPT/ folder.

The landing page did not immediately display the phishing content. It first required visitors to pass a custom CAPTCHA, which was a simple Update payment button. If they clicked this button, users were sent to the next page where personal information, including first name, last name, and address was collected. The final page then collected the name, credit card number, expiration date, and card verification code.

Claude-themed phishing campaign collected credentials and access tokens

From April 20 to 22, 2026, Microsoft observed a phishing campaign impersonating Anthropic-branded services to target users with account-related lures tied to the Claude AI platform. The campaign sent phishing emails to targets across more than 2,000 organizations, primarily in the United States (62%), the United Kingdom (18%), and India (9%). While this campaign impacted a broad range of industries, it was most notably focused on information technology (56%), other business entities (21%), and financial services (8%).

The campaign used enforcement-themed messaging claiming that the recipient’s account was in violation of acceptable use policies and required immediate action. The emails impersonated Anthropic’s popular AI service Claude using the display names Anthropic Teams and Anthropic PBC, masquerading as legitimate account-related communications. Subject lines followed a consistent structure of “Claude Appeal Request” combined with date elements.

The email body was delivered as HTML and included Anthropic and Claude branding. The message informed recipients that their account was violating “AUP (Account Usage Policy)” and that Anthropic had “initiated an appeal procedure”. The message instructed recipients to review the attached material to access their appeal and indicated that Claude features would be limited pending review.

The email attachment was a PDF named Fill and Sign Claude Appeal Form.pdf, which was designed to resemble an official process tied to Claude account enforcement. The document presented an appeal workflow, prompting users to copy an appeal ID and click the “Claude Appeal” link, which initiated the credential harvesting process.

When clicked, the link embedded in the PDF directed users to an attacker-controlled domain, dash.awaydouble[.]org. The initial landing page displayed a Cloudflare verification prompt, presented as confirming the user was arriving from a “legitimate session”. This step likely served as a gating mechanism to impede automated analysis and sandbox detonation.

Users who completed the verification were redirected to another Claude-themed landing page hosted on servicing.pureplantcravings[.]com. This page was named “Account Appeal Notice” and contained “Account Security & Compliance” message informing users that their account had been flagged for repeated violations of usage policies. The page provided a reference date and a one-time access code, prompting users to copy the code and continue.

Clicking “Continue” redirected users to the final page, which was not available at the time of analysis. Source code revealed conditional redirect logic that routed users to one of two final landing pages, depending on whether the site was accessed through mobile device or a desktop system.

While the final redirect destination was no longer active at the time of analysis, infrastructure overlap, including shared intermediate domains and consistent redirect logic, strongly suggested that users were ultimately presented with a Microsoft sign-in experience. This final stage is consistent with adversary-in-the-middle (AiTM) tactics designed to intercept authentication tokens and facilitate account compromise.

“Awesome AI Windows Plugin” malvertising deploys Vidar stealer

Since at least early 2026, Microsoft Threat Intelligence has observed malvertising campaigns that use AI-themed terms such as “Awesome AI Windows Plugin” and “Flux Pro AI” in social engineering lures in malicious popups, in malware executable names, and GitHub repository and folder names throughout the attack chain. These campaigns are notable for their scale and velocity, moving from launch to mass impact within hours and infecting tens to hundreds of thousands of endpoints. The malware delivered in these campaigns is frequently code-signed, lending an additional layer of perceived trust to both the operating system and the user.

Microsoft attributes this malvertising activity to an initial access broker and malware distributor tracked as Storm-3075. We assess that Storm-3075 delivers final payloads on behalf of multiple downstream actors. While the example campaign described in this section delivered Vidar Stealer, we have also observed this campaign distributing Lumma Stealer, Hijack Loader, and Oyster.

On March 13, 2026, a single campaign run targeted over 66,000 devices. Microsoft has revoked the related signing certificate and GitHub has taken down the associated repository, helping to prevent tens of thousands of additional infections. Given the nature of the attack source, majority of impacted devices were likely consumer rather than enterprise endpoints. Telemetry showed global distribution, with the top affected countries being Japan, South Africa, the United States, and France.

Analysis of the redirection chain determined that the attack likely originated from free movie streaming sites. Infections on such sites typically begin when users interact with embedded movie players or click popups. Malvertising embedded in such sites can redirect users to a range of unwanted content, including malware. In this campaign, users were redirected to a page advertising a download for an “Awesome AI Windows plugin”, a fictitious product name. The plugin purported to help users watch free, high-quality videos, a lure aligned with the context of users already streaming free or pirated content.

Clicking the download button retrieved an executable named ProFluxeFlowAi-win-Setup.exe, which the user then had to manually launch. The file name mimicked a legitimate product with a similar name, Flux Pro AI, which supports text, image, and video creation. This lure reinforced the perceived legitimacy of the executable within the streaming of free movies context. The executable itself was hosted on GitHub in a repository named shippingtechnologymovie under a folder named AI-techVideos, both tailored to the AI video helper narrative.

The malware executable was signed with a fraudulently obtained Microsoft-issued code-signing certificate obtained through Artifact Signing (certificate thumbprint: 4f5c5b3ef45cfff7721754487a86aeff9a2e6e32). Microsoft attributes the signing service used by the threat actor to Fox Tempest, a financially motivated threat actor operating a malware-signing-as-a-service (MSaaS) offering used by other threat actors. Microsoft has revoked over one thousand code signing certificates attributed to Fox Tempest. In May 2026, Microsoft’s Digital Crimes Unit (DCU), in partnership with Resecurity, facilitated a disruption of Fox Tempest infrastructure and access model.   

Signing malware through such a service is expensive; however, for a threat actor targeting tens or hundreds of thousands of infections, the cost can be justified by the additional level of trust signed binaries imply to both the operating system and the user. Signed malware also tends to exhibit lower detection rates early in the infection lifecycle, extending the window of effective distribution.

Another notable feature of the malware is that, immediately after launch, it displays a window with a “Continue” checkmark and does not proceed until the box is clicked. This extra user interaction step is uncommon. We assess that this technique is intended to hide the malicious functionality from sandboxes and automated analysis environments that cannot dynamically perform the click. Until the user clicks “Continue,” the malware performs no suspicious activity on the operating system. This technique is functionally analogous to the CAPTCHAs frequently seen in phishing attacks.

Once the user clicks “Continue”, the executable drops and runs a malicious Python-based downloader. Both the Python interpreter and the downloader script are saved in the \AppData\Local\ folder as pythonw.exe and LICENSE.txt, respectively. The malicious script runs shellcode that loads the next-stage malware from the command-and-control (C2) domain brokeapt[.]com. The final payload observed in this campaign was Vidar infostealer.

Fake DeepSeek V4 installers on GitHub delivered Vidar Stealer

In April 2026, Microsoft identified a social engineering campaignsocial-engineering campaign that leveraged interest in the newly released DeepSeek V4 by impersonating it through a fraudulent GitHub repository and organization. The campaign abused GitHub’s release-asset infrastructure to deliver information-stealing malware such as Vidar stealer. Search engines increased the exposure of the malicious repository, exacerbated by the fact that DeepSeek did not publish an official V4 repository on GitHub.

Our investigation shows the DeepSeek lure is one identity in a broader rotating brand-abuse ecosystem that recycles whichever AI tool is trending into a fresh malware download experience. After discovering this activity, Microsoft shared the details with GitHub, and GitHub has since taken down the malicious organization, repository, and operator account.

On April 24, 2026, within hours of DeepSeek officially previewing its new V4 frontier model, a threat actor initiated the attack chain that can be summarized as:

1. Resource development on GitHub, all within roughly 45 minutes: A new GitHub organization (DeepSeek-V4), a single repository (deepseek-V4), and a release tag (deepseek-V4). The repository was decorated with stolen DeepSeek branding, real benchmark data, and SEO-optimized topics.
2. Search-driven discovery: Users found the repository through GitHub repository search, search engines, social sharing, and AI-assisted search results pointing to the lure page. The repository’s llms.txt and topic taxonomy were designed to be discovered by both classical search engines and large-language-model-powered search; observed top-rank results on search engines are consistent with that design, though we did not observe paid advertising and therefore do not assess this as malvertising.
3. Archive download from GitHub’s release-asset CDN: The release page hosted two archives, deepseek-v4-pro_x64.7z and deepseek-v4-flash_x64.7z.
4. User extraction: Users needed to extract the executable from the archive using common Windows archive tools.
5. Payload execution: The archives contained a heavyweight Win32 PE that masqueraded as the DeepSeek installer. At least one confirmed victim endpoint revealed the extracted payload landed at: C:\Users\<user>\Downloads\Programs\IA DeepSeek-V4\deepseek-v4-flash_x64.exe.
6. Active payload rotation: The threat actor actively rotated archive content while preserving file names and the release page. We observed at least three distinct archive hash generations in three days.

Microsoft Defender telemetry observed the first victim download approximately four hours later. The threat actor’s operational tempo on April 24, 2026, is consistent with a prepared, rehearsed workflow. The repository was designed to be convincing at a glance. It accumulated 91 stars and 27 forks within four days, though the proportion of organic versus inflated engagement is not independently confirmed. The attacker invested in several credibility-building elements:

• Stolen branding: The repository’s README and assets folder embedded the legitimate DeepSeek whale logo, copied from the real deepseek-ai/DeepSeek-V2 repository.
• Real benchmark data as lure: The release notes displayed authentic DeepSeek V4 benchmark scores against Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro, copied from the official release announcement.
• Action-oriented SEO topics: The repository was tagged with deepseek-v4, deepseek-v4-download, deepseek-v4-downloader, deepseek-v4-install, and deepseek-v4-installer, which are queries users are expected to use when intent-shopping for an installer.
• LLM-aware discoverability: A top-level llms.txt file repeated the same SEO copy in a format aimed at AI-assisted search engines.

On closer inspection, the staging gives the operation away: the repository contained only a README, LICENSE, llms.txt, and stub assets/ and inference/ directories with no real model code; all nine commits were made in a single burst on April 24, 2026 by a single author; the README claimed an MIT license while repository metadata specified Apache 2.0.

Once the lure was live, search engines increased the exposure of the malicious repository. We tested the queries an interested user would naturally try when looking for DeepSeek V4 on GitHub or the open web. In a snapshot captured on April 28, 2026, the results were as follows (search results are volatile and may differ at the time of reading):

The 7z archives hosted on GitHub contained a loader executable such as SHA-256: 5455341ed1bbe75a664fca2dd0794c508e1874f75360253a7ff5bc119bc92d80. The loader was observed downloading and installing Vidar stealer and potentially additional malware.

Lastly, Microsoft observed that the DeepSeek-themed payloads share infrastructure with a much larger rotating fake-AI / fake-tool ecosystem. The same shared loader hash (SHA-256 5455341…) appeared under file names impersonating GPT-5.5, Claude Code, Kimi, Seedance, Gemma, GrokCLI, Manus AI, FraudGPT, and others (see table below). Public research from Trend Micro, Zscaler ThreatLabz, and Huntress describe the same broader ecosystem, with TradeAI.exe, OpenClaw_x64.7z, WormGPT_x64.7z, and DeepSeekAI_agent_x64.7z appearing as sibling lures and the downstream payload set documented as Vidar plus GhostSocks.

Mitigation and protection guidance

To defend against social engineering campaigns that leverage AI brands as lures, Microsoft recommends the following mitigation measures:

• Configure automatic attack disruption in Microsoft Defender XDR. Automatic attack disruption is designed to contain attacks in progress, limit the impact on an organization’s assets, and provide more time for security teams to remediate the attack fully.
• Enforce multifactor authentication (MFA) on all accounts, remove users excluded from MFA, and strictly require MFA from all devices in all locations at all times.
• Use the Microsoft Authenticator app for passkeys and MFA, and complement MFA with conditional access policies, where sign-in requests are evaluated using additional identity-driven signals.
• Conditional access policies can also be scoped to strengthen privileged accounts with phishing resistant MFA.
• Enable Zero-hour auto purge (ZAP) in Office 365 to quarantine sent mail in response to newly acquired threat intelligence and retroactively neutralize malicious phishing, spam, or malware messages that have already been delivered to mailboxes.
• Configure Microsoft Defender for Office 365 Safe Links to recheck links on click. Safe Links provides URL scanning and rewriting of inbound email messages in mail flow and time-of-click verification of URLs and links in email messages, other Microsoft Office applications such as Teams, and other locations such as SharePoint Online. Safe Links scanning occurs in addition to the regular anti-spam and anti-malware protection in inbound email messages in Microsoft Exchange Online Protection (EOP). Safe Links scanning can help protect your organization from malicious links that are used in phishing and other attacks.
• Invest in advanced anti-phishing solutions that monitor and scan incoming emails and visited websites. For example, organizations can leverage web browsers like Microsoft Edge that automatically identify and block malicious websites, including those used in this phishing campaign, and solutions that detect and block malicious emails, links, and files.
• Encourage users to use Microsoft Edge and other web browsers that support Microsoft Defender SmartScreen, which identifies and blocks malicious websites, including phishing sites, scam sites, and sites that host malware.
• Enable network protection to prevent applications or users from accessing malicious domains and other malicious content on the internet.

Microsoft Defender detections

Microsoft Defender customers can refer to the list of applicable detections below. Microsoft Defender coordinates detection, prevention, investigation, and response across endpoints, identities, email, 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 the most up-to-date information about the threat actor, malicious activity, and techniques discussed in this blog. These reports provide the intelligence, protection information, and recommended actions to prevent, mitigate, or respond to associated threats found in customer environments.

• Threat overview profile: Evolving phishing threats

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.

Indicators of compromise

Learn more

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The post AI brands as bait: How threat actors are using the AI hype in social engineering appeared first on Microsoft Security Blog.

Microsoft Threat Intelligence identified a large-scale npm supply chain attack affecting 32 maliciously modified packages across more than 90 versions under the @redhat-cloud-services npm scope. The compromise originated from the upstream RedHatInsights/javascript-clients Continuous Integration and Continuous Delivery (CI/CD) pipeline, allowing attackers to publish trojanized packages through the legitimate GitHub Actions OpenID Connect (OIDC) publishing workflow. As a result, the malicious packages carried authentic provenance signatures while embedding the campaign marker “Miasma: The Spreading Blight.”

Once installed, the trojanized packages triggered an npm preinstall hook that executed a heavily obfuscated 4.29 MB dropper script. Through multiple layers of obfuscation and encryption, the malware downloaded the Bun JavaScript runtime and launched a secondary payload designed to harvest credentials from GitHub, npm, Amazon Web Service (AWS), Azure, Google Cloud Platform (GCP), HashiCorp Vault, Kubernetes, and developer systems. The malware also attempted to propagate by compromising additional maintainer packages and, in some scenarios, could destroy the maintainer’s home directory.

The payload operated across Linux, macOS, and Windows by dynamically downloading the correct Bun runtime for each platform, although Linux CI/CD runners appeared to be the primary target. On developer systems, the malware stole Secure Shell (SSH) keys, command-line interface (CLI) credentials, browser and wallet data, while in CI/CD environments it scraped GitHub Actions runner memory for secrets, escalated privileges using passwordless sudo, and republished poisoned packages with forged Supply-chain Levels for Software Artifacts (SLSA) provenance to continue downstream propagation. Microsoft shared its findings with the npm team, leading to the removal of affected repositories and the implementation of additional protections on the @redhat-cloud-services namespace to prevent unauthorized publishing.

Attack chain overview

At a high level, the malware payload progresses through 10 phases:

• Delivery and execution: The infection begins automatically during npm install, where the malicious preinstall hook executes node index.js without requiring user interaction.
• Staged unpacking: The payload is unpacked through multiple decoding layers, including several ROT (rotate)-based obfuscation variants followed by AES-128-GCM decryption. The malware then downloads the Bun runtime and detonates the final payload.
• Environment gating: The malware validates the execution environment before continuing. It terminates execution on systems configured with few regions in locale settings and can optionally restrict execution to CI/CD environments only.
• Defense evasion: The malware attempts to neutralize security controls
• Credential access: The malware harvests secrets and authentication tokens from GitHub, npm, major cloud providers, HashiCorp Vault, and Kubernetes environments, including scraping sensitive data directly from CI runner process memory.
• Privilege escalation: It installs a passwordless sudo rule to obtain elevated privileges and maintain deeper system control.
• Persistence: The malware continuously monitors stolen tokens and prepares secondary-stage payload deployment for long-term access.
• Exfiltration: Stolen data is transmitted using three separate command-and-control (C2) channels, including abuse of GitHub infrastructure as an exfiltration mechanism.
• Self-propagation: The malware republishes packages owned by the compromised maintainer using forged provenance metadata, effectively allowing the threat to spread like a worm across trusted package ecosystems.
• Destructive tripwire: If the malware detects interaction with a planted decoy token, it triggers a destructive fail-safe command (rm -rf ~/) intended to wipe the victim’s home directory.

The payload replaces the legitimate index.js with a single-line obfuscated script.

Obfuscation

Stage 0 – Malicious preinstall trigger: The attack begins in package.json, where a weaponized preinstall hook automatically executes during npm install, allowing the malware to run through both direct and transitive dependency installation. The modified packages also replaced the original index.js while leaving source-map metadata unchanged, indicating probable release-pipeline tampering.

Stage 1 – Multi-layer JavaScript obfuscation: The 4.29 MB index.js dropper uses layered obfuscation, beginning with a large character-code array reconstructed at runtime, decoded through a ROT-XX (Caesar cipher) transformation, and dynamically executed via eval().

Stage 2 – AES-encrypted payloads and Bun runtime abuse: The next layer decrypts two AES-128-GCM encrypted blobs: one downloads the Bun runtime from official Bun infrastructure, while the second contains the primary payload. The malware then executes the payload via Bun, creating an unusual process chain (node → shell → bun → payload) designed to evade Node-focused monitoring and detections.

Stage 3 – Obfuscator.io string-array protection: The Bun-executed payload is additionally protected using Obfuscator.io techniques, including rotated string arrays, decoder functions, and hundreds of alias wrappers that conceal nearly every string and identifier from static analysis.

Stage 4 – Custom cryptographic string cipher: Sensitive strings remain protected behind a bespoke encryption routine that derives keys using PBKDF2-HMAC-SHA-256 with 200,000 iterations, followed by multiple SHA-256-seeded permutation and XOR stages, significantly complicating reverse engineering and static extraction.

Credential theft

The payload targets secrets across multiple providers:

• GitHub: Validates token/scopes, enumerates repos, reads Actions/org secrets, uses GraphQL for branch/history, and steals ACTIONS_RUNTIME_TOKEN + ACTIONS_ID_TOKEN_REQUEST_TOKEN.
• npm: Validates via /-/whoami, exchanges OIDC token for publish rights, and searches maintainer-owned packages for poisoning targets.
• AWS: Pulls Identity and Access Management (IAM) credentials via Instance Metadata Service (IMDS) and Elastic Container Service (ECS) metadata, plus Secrets Manager access.
• Azure: Collects IMDS OAuth2 tokens for management.azure.com, graph.microsoft.com, and Key Vault (*.vault.azure.net).
• GCP: Harvests metadata.google.internal service-account tokens, Secret Manager, and Resource Manager access.
• Vault/K8s: Probes Vault (127.0.0.1:8200) across many token paths; reads Kubernetes Service Account (SA) token and namespace secrets.
• CI & Local : Steals CIRCLE_TOKEN; exfiltrates secrets from SSH/AWS/npm/PyPI/git/env/gcloud/kube/docker, browser data, and wallet files (*.wallet, wallet.dat).

Runner memory scraping

The payload locates the GitHub Actions Runner.Worker PID using /proc scanning, then extracts runtime secrets using the following:

// Locates Runner.Worker PID via /proc
'findRunnerWorkerPIDLinux'
// Scans /proc//cmdline for "Runner.Worker"
 
// Extracts secrets from process memory
tr -d '\0' | grep -aoE '"[^"]+":{"value":"[^"]*","isSecret":true}' | sort -u

This activity bypasses normal secret masking by reading secrets directly from runner process memory.

Privilege escalation

The payload performs the following actions to escalate its privileges:

• Injects sudoers rule through bind mount: echo ‘runner ALL=(ALL) NOPASSWD:ALL’ > /mnt/runner
• Modifies /etc/hosts for DNS redirection

// Injects passwordless sudo via /etc/sudoers.d bind mount at /mnt
echo 'runner ALL=(ALL) NOPASSWD:ALL' > 
 && chmod 0440 /mnt/runner
 
// Neutralize Security product monitoring 
sudo sh -c "echo '127.0.0.1 ' >> /etc/hosts"
 
// Validates sudo access before operations
sudo -n true

Exfiltration

The malware abuses GitHub and victim-owned assets instead of a single easy-to-block C2 endpoint:

Channel A (victim-owned repo drop): Creates a public repo in the victim’s GitHub account (“Miasma: The Spreading Blight”) and commits stolen credential JSON to results/<timestamp>-<counter>.json. Repo names are randomized (adjective-creature-<0–99999>), spreading indicators.

Channel B (code propagation): Injects its own source as .github/setup.js into non-protected branches across victim-owned repos via Git Data API (blob → tree → commit → ref update). Skips protected/default branches and common bot/release branches; uses chore: update dependencies [skip ci] with spoofed github-actions@github.com.

Channel C (dormant HTTPS sender): Includes a disabled POST path to api.anthropic.com:443/v1/api (noop: true in this sample). The same domain is used to validate stolen Anthropic keys (for example, ~/.claude.json), indicating a swappable live exfiltration path.

C2 is not tied to one account; it rotates across a pool of 16 attacker-controlled GitHub accounts per session. Stolen tokens are double-Base64 encoded in transit, and traffic is masked with python-requests/2.31.0 user-agent spoofing

Propagation and persistence

The malware spreads across repositories while maintaining access through credential theft, supply-chain forgery, and destructive safeguards:

• Enumerates /user/repos and /user/orgs to spread into additional repositories
• Installs Bun runtime, executes second-stage payload using bun run .claude/
• Deploys token monitor for ongoing credential capture
• Forges SLSA provenance attestations through Sigstore (Fulcio or Rekor) to appear legitimate
• Plants a decoy honeytoken (IfYouInvalidateThisTokenItWillNukeTheComputerOfTheOwner); triggering/revoking it can invoke a wiper routine (rm -rf ~/ and ~/Documents)

Impact and blast radius

This attack has a wide blast radius, affecting packages, credentials, and downstream systems.

• Direct compromise of @ redhat-cloud-services packages with broad ecosystem adoption
• Amplification through downstream dependencies into thousands of projects
• Cascading risk: stolen npm tokens enable further package poisoning, stolen GitHub tokens enable repo manipulation, and stolen AWS credentials enable cloud access
• SLSA provenance forgery erodes trust in supply chain attestation frameworks

Campaign scope

Our investigation uncovered the following affected packages and versions.

Mitigation and protection guidance

Microsoft recommends the following mitigations to reduce the impact of this threat:

• Review dependency trees for direct or transitive usage of affected @ redhat-cloud-services / packages.
• Identify systems that installed or built affected package versions during the suspected exposure window.
• Pin known-good package versions where possible and avoid automatic dependency upgrades until validation is complete.
• Disable pre- and post-installation script execution by ensuring you run npm install with –ignore-scripts.
• While GitHub team has already invalidated all the npm tokens that had write access and 2FA bypass, Microsoft Defender still recommends rotating credentials, tokens, npm access tokens, CI/CD secrets, and cloud credentials that might have been exposed in affected build or developer environments.
• Audit organization and personal GitHub account for public repositories with the description “Miasma: The Spreading Blight” or other unexpected repositories created during the exposure window, and revoke any GitHub tokens that might have been implicated.
• Audit CI/CD logs for unexpected outbound network connections, script execution, or suspicious package lifecycle activity.
• Review npm package lockfiles, build logs, and artifact provenance for evidence of compromised package versions.
• Enable cloud-delivered protection in Microsoft Defender Antivirus or equivalent antivirus protection.
• Use Microsoft Defender XDR to investigate suspicious activity across endpoints, identities, cloud apps, and developer environments. Use Microsoft Defender Vulnerability Management to search for redhat-cloud-services packages across your estate.

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.

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.

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 Defender XDR Threat analytics

Microsoft Defender XDR customers can reference the Threat analytics report for this campaign in the Microsoft Defender portal at https://security.microsoft.com/threatanalytics3 for the latest indicators, recommended actions, and mitigation status across their estate. 

Advanced hunting

The following KQL queries can be used in Microsoft Defender XDR Advanced Hunting to identify potential exposure to this supply-chain compromise.

Bun execution from temporary directories

DeviceProcessEvents
| where FileName == "bun" or ProcessCommandLine has "bun run"
| where FolderPath startswith "/tmp/" or FolderPath startswith @"C:\Users\*\AppData\Local\Temp"
| project Timestamp, DeviceName, InitiatingProcessFileName, 
    ProcessCommandLine, FolderPath, AccountName
| sort by Timestamp desc

Bun execution from temporary directory (CloudProcessEvents)

CloudProcessEvents
| where Timestamp > ago(7d)
| where ProcessName =~ "bun"
   or ProcessCommandLine has "bun run"
| where FolderPath startswith "/tmp/"
   or ProcessCommandLine matches regex @"/tmp/[^ ]*bun"
| project Timestamp, TenantId, AzureResourceId,
          KubernetesNamespace, KubernetesPodName,
          ContainerName, ContainerImageName, ContainerId,
          AccountName,
          ProcessName, FolderPath, ParentProcessName, ProcessCommandLine,
          UpperLayer  = tostring(AdditionalFields.UpperLayer),
          DriftAction = tostring(AdditionalFields.DriftAction),
          Memfd       = tostring(AdditionalFields.Memfd)
| sort by Timestamp desc

Bun download activity

CloudProcessEvents
| where Timestamp > ago(7d)
| where ProcessName in~ ("curl","wget")
| where ProcessCommandLine matches regex
        @"https?://[^\s""']*?(github\.com/oven-sh/bun/releases|release-assets\.githubusercontent\.com/[^\s""']*?bun-(linux|darwin|windows)|/bun-(linux|darwin|windows)-(x64|aarch64|arm64)\.zip)"
| extend BunUrl = extract(
        @"(https?://[^\s""']*?(?:github\.com/oven-sh/bun/releases|release-assets\.githubusercontent\.com/[^\s""']*?bun-(?:linux|darwin|windows)|/bun-(?:linux|darwin|windows)-(?:x64|aarch64|arm64)\.zip)[^\s""']*)",
        1, ProcessCommandLine),
         OutputPath = extract(@"-[oO]\s+[""']?(\S+?)[""']?(\s|$)", 1, ProcessCommandLine)
| project Timestamp, TenantId, AzureResourceId,
          KubernetesNamespace, KubernetesPodName,
          ContainerImageName, ContainerId,
          ProcessName, ParentProcessName, ParentProcessId,
          BunUrl, OutputPath, ProcessCommandLine,
          UpperLayer = tostring(AdditionalFields.UpperLayer)
| sort by Timestamp desc

npm → Node → Bun process chain

DeviceProcessEvents
| where InitiatingProcessFileName in ("node", "node.exe")
| where FileName == "bun" or FileName == "bun.exe"
| join kind=inner (
    DeviceProcessEvents
    | where InitiatingProcessFileName in ("npm", "npm.cmd")
    | where FileName in ("node", "node.exe")
) on DeviceId, $left.InitiatingProcessId == $right.ProcessId
| project Timestamp, DeviceName, AccountName,
    NpmCommandLine = ProcessCommandLine1,
    BunCommandLine = ProcessCommandLine

Cloud metadata endpoint access from build processes

DeviceNetworkEvents
| where RemoteIP in ("169.254.169.254", "169.254.170.2")
| where InitiatingProcessFileName in ("node", "node.exe", "bun", "bun.exe")
| project Timestamp, DeviceName, RemoteIP, RemoteUrl,
    InitiatingProcessFileName, InitiatingProcessCommandLine

GitHub repository creation activity

CloudAppEvents
| where ActionType == "CreateRepository" or RawEventName == "repo.create"
| where Application == "GitHub"
| where AccountType == "ServiceAccount" or ActorType has "Integration"
| project Timestamp, AccountDisplayName, ActionType, RawEventName,
    IPAddress, City, CountryCode

Process memory access (runner scraping)

DeviceProcessEvents
| where FileName == "grep"
| where ProcessCommandLine has_all ("value", "isSecret\":true")

npm token enumeration

DeviceNetworkEvents
| where RemoteUrl has "registry.npmjs.org/-/npm/v1/tokens"
    or RemoteUrl has "registry.npmjs.org/-/whoami"
| project Timestamp, DeviceName, RemoteUrl,
    InitiatingProcessFileName, InitiatingProcessCommandLine

Linux CI runner detection (process tree)

# For Linux runners not managed by Defender, use these shell commands:
# Detect: npm preinstall spawning bun from /tmp
ps aux | grep -E '/tmp/b-[a-z0-9]+/bun'
# Detect: payload writes to /tmp/p*.js
inotifywait -m /tmp -e create | grep '^/tmp/p.*\.js$'

Indicators of compromise (IOC)

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The post Preinstall to persistence: Inside the Red Hat npm Miasma credential-stealing campaign appeared first on Microsoft Security Blog.