Malicious code in anuaja (npm)
The npm package 'anuaja' versions 1.0.0, 1.1.0, and 1.1.1 is part of a large family of malicious forks of the Baileys WhatsApp Web library. These forks inject code that silently subscribes the victim's WhatsApp account to attacker-controlled channels without user consent by issuing authenticated FOLLOW queries using the victim's own session. The target channels are obfuscated or dynamically fetched to evade detection. The malicious code does not exfiltrate credentials, establish persistence, or execute arbitrary remote code.
AI Analysis
Technical Summary
The 'anuaja' npm package (versions 1.0.0, 1.1.0, 1.1.1) belongs to a family of over 100 near-identical malicious forks of the Baileys WhatsApp Web library. These forks inject a covert subscription action into the WhatsApp socket layer that, upon connection, issues an authenticated 'w:mex' FOLLOW query to attacker-chosen WhatsApp Channel/Newsletter JIDs. This action silently subscribes the victim's WhatsApp account to these channels using the victim's authenticated session. The target JIDs are obfuscated via multiple techniques or dynamically fetched from attacker-controlled remote sources, allowing runtime changes without republishing the npm package. The malicious behavior is consistent across the family, involving a patched socket-connect routine that triggers the FOLLOW query shortly after connection. The attack does not involve credential theft, persistence mechanisms, or arbitrary code execution.
Potential Impact
Victims unknowingly subscribe their WhatsApp accounts to attacker-controlled channels or newsletters, potentially exposing them to unwanted content or spam. The attack leverages the victim's authenticated WhatsApp session to perform these subscriptions silently. There is no direct credential compromise, persistence, or remote code execution involved.
Mitigation Recommendations
No official patch or remediation is currently documented. Users should avoid installing or using the affected 'anuaja' package versions 1.0.0, 1.1.0, and 1.1.1. Review and audit dependencies for forks of the Baileys WhatsApp Web library, especially those with obfuscated or dynamic code fetching. Monitor for suspicious subscriptions in WhatsApp accounts and remove unauthorized channel subscriptions manually. Patch status is not yet confirmed — check the vendor advisory or npm security advisories for updates.
Malicious code in anuaja (npm)
Description
The npm package 'anuaja' versions 1.0.0, 1.1.0, and 1.1.1 is part of a large family of malicious forks of the Baileys WhatsApp Web library. These forks inject code that silently subscribes the victim's WhatsApp account to attacker-controlled channels without user consent by issuing authenticated FOLLOW queries using the victim's own session. The target channels are obfuscated or dynamically fetched to evade detection. The malicious code does not exfiltrate credentials, establish persistence, or execute arbitrary remote code.
Affected software
Run on your own infrastructure? Check whether these packages are installed with threat-finder — our free open-source scanner.
AI-Powered Analysis
Machine-generated threat intelligence
Technical Analysis
The 'anuaja' npm package (versions 1.0.0, 1.1.0, 1.1.1) belongs to a family of over 100 near-identical malicious forks of the Baileys WhatsApp Web library. These forks inject a covert subscription action into the WhatsApp socket layer that, upon connection, issues an authenticated 'w:mex' FOLLOW query to attacker-chosen WhatsApp Channel/Newsletter JIDs. This action silently subscribes the victim's WhatsApp account to these channels using the victim's authenticated session. The target JIDs are obfuscated via multiple techniques or dynamically fetched from attacker-controlled remote sources, allowing runtime changes without republishing the npm package. The malicious behavior is consistent across the family, involving a patched socket-connect routine that triggers the FOLLOW query shortly after connection. The attack does not involve credential theft, persistence mechanisms, or arbitrary code execution.
Potential Impact
Victims unknowingly subscribe their WhatsApp accounts to attacker-controlled channels or newsletters, potentially exposing them to unwanted content or spam. The attack leverages the victim's authenticated WhatsApp session to perform these subscriptions silently. There is no direct credential compromise, persistence, or remote code execution involved.
Mitigation Recommendations
No official patch or remediation is currently documented. Users should avoid installing or using the affected 'anuaja' package versions 1.0.0, 1.1.0, and 1.1.1. Review and audit dependencies for forks of the Baileys WhatsApp Web library, especially those with obfuscated or dynamic code fetching. Monitor for suspicious subscriptions in WhatsApp accounts and remove unauthorized channel subscriptions manually. Patch status is not yet confirmed — check the vendor advisory or npm security advisories for updates.
Technical Details
- Gcve Source
- db.gcve.eu
- Osv Id
- MAL-2026-17386
- Osv Schema Version
- 1.7.4
- Ecosystems
- ["npm"]
Threat ID: 6abd30852a4e24523d34012c
Added to database: 09/30/2026, 15:53:41 UTC
Last enriched: 09/30/2026, 16:02:59 UTC
Last updated: 09/30/2026, 22:43:14 UTC
Views: 2
Community Reviews
0 reviewsCrowdsource mitigation strategies, share intel context, and vote on the most helpful responses. Sign in to add your voice and help keep defenders ahead.
Want to contribute mitigation steps or threat intel context? Sign in or create an account to join the community discussion.
Actions
Updates to AI analysis require Pro Console access. Upgrade inside Console → Billing.
External Links
Need more coverage?
Upgrade to Pro Console for AI refresh and higher limits.
For incident response and remediation, OffSeq services can help resolve threats faster.
Latest Threats
Check if your credentials are on the dark web
Instant breach scanning across billions of leaked records. Free tier available.