Lokibot

Overview

LokiBot (also designated as Loki PWS) is a widely proliferated commodity information stealer and keylogger first observed in 2015. Authored by an underground developer known as 'lokistat' and sold extensively across Darkode and Russian-speaking forums, LokiBot was specifically designed to covertly harvest stored authentication credentials, cryptocurrency wallets, and sensitive data from over 100 Windows software applications. LokiBot systematically extracts saved passwords from web browsers (Chrome, Firefox, Edge, Safari), email clients (Outlook, Thunderbird), FTP applications (FileZilla, WinSCP), terminal emulators (PuTTY), and cryptocurrency software. Because of its extreme prevalence and damaging impact on critical infrastructure, LokiBot was the subject of an emergency joint alert (Alert AA20-266A) issued by the US Cybersecurity and Infrastructure Security Agency (CISA) and the FBI. The malware is typically packaged in multi-layer packers and distributed through mass phishing emails weaponized with Microsoft Office exploits (CVE-2017-11882).

Known aliases

Threat reports may refer to this family under multiple names:

• LokiBot
• Loki PWS
• Loki-Bot
• InfoStealer.LokiBot
• Trojan-PSW.Win32.Loki

Threat Actor Attribution

Attribution is probabilistic, not a science. Each link below states its confidence level and the evidence behind it.

• lokistat (Underground Commercial Developer) / Global Cybercrime Actors (CISA Alert AA20-266A) High confidence
  Basis: Authored by developer 'lokistat' in 2015. Highlighted in joint US CISA and FBI Advisory AA20-266A as one of the most consistently prolific credential theft threats affecting critical infrastructure and commercial enterprises. [source]

Family Pedigree & Lineage

Malware authors regularly fork and iterate upon existing codebases. Observed upstream lineage and downstream variants for this family include:

Known Derivatives & Downstream Variants: Lokibot Windows Pe Core · Lokibot Android Banking Variant (2016) · Lokibot Autoit Obfuscated Builder (2018-2022)

MITRE ATT&CK Techniques

Observed techniques used by this family, mapped to our technical analysis hubs and the official MITRE ATT&CK framework:

Tactical Mitigations

Based on the techniques used by this family, consider the following defensive strategies:

• T1056.001: Implement Endpoint Detection and Response (EDR) to monitor for suspicious API calls related to keystroke interception. Enforce Multi-Factor Authentication (MFA) to render stolen passwords useless.
• T1071.001: Implement web filtering and SSL/TLS inspection to detect malicious command and control (C2) traffic hiding in HTTP/HTTPS.

MITRE D3FEND™ Defensive Countermeasures

Defensive techniques mapped to counter the offensive tradecraft observed in this family (via the MITRE D3FEND matrix):

Exploited Vulnerabilities (CVEs)

This malware family is documented weaponizing the following common vulnerabilities and exposures:

• CVE-2017-0199 — In-depth exploitation analysis and catalog intelligence (NIST NVD ↗).
• CVE-2017-11882 — In-depth exploitation analysis and catalog intelligence (NIST NVD ↗).
• CVE-2018-0802 — In-depth exploitation analysis and catalog intelligence (NIST NVD ↗).

Command & Control (C2) and Network Profile

How this family talks to its operators. C2 traffic can often identify a family even when its code and configuration are hard to analyse. Ports and patterns are historical and change between campaigns, so use them as hunting leads rather than block-only rules.

Indicators of Compromise

Public indicators drawn from CISA advisories and vendor reporting. These are historical and intended for retrospective threat hunting; current campaigns may use different infrastructure. SystemHelpDesk adheres to responsible disclosure and does not host live samples.

File hashes (SHA-256, sample)

Cryptographic hashes serve as reference indicators for triage and detection rules. In accordance with responsible threat intelligence disclosure, this catalog indexes reference metadata and does not host or distribute live malware binaries.

• 4f2e96d2ff86e7a2df25b84c8a29a6cf7d853177699317585f6e80b27ef3e6ff
• 9e49c7161bcf76ee8a20da62fecbf9bb748a31a98059bf4ce126ecba95f002f1

Delivery vectors

• Phishing emails masquerading as purchase orders or invoices containing weaponized RTF documents exploiting Equation Editor (CVE-2017-11882)
• Obfuscated ISO and RAR attachments containing VBScript or AutoIt loaders

Persistence mechanisms

• HKCU\Software\Microsoft\Windows\CurrentVersion\Run entries pointing to staged executables in %APPDATA%
• Process hollowing into vbc.exe or msbuild.exe

Tiered Detection Rules (Cisco CAIRN Model)

Modern threat detection requires multi-layered rule engineering. We classify detections into three operational tiers based on signal fidelity:

rule MALWARE_WIN_LOKIBOT_T1_ARTIFACT {
    meta:
        description = "Tier 1 Primitive Artifact: Raw string matches for Lokibot"
        tier = "T1 - Primitive Artifact"
        confidence = "Low-Medium (Fast Triage)"
        author = "SystemHelpdesk Threat Research Desk"
        date = "2026-10-14"
    strings:
        $s1 = "lokibot" ascii wide nocase
        $s2 = "lokibot" ascii wide nocase
        $s3 = "loki pws" ascii wide nocase
        $s4 = "loki-bot" ascii wide nocase
        $s5 = "infostealer.lokibot" ascii wide nocase
        $s6 = "trojan-psw.win32.loki" ascii wide nocase
    condition:
        uint16(0) == 0x5a4d and any of them
}

title: SystemHelpDesk Detection - Lokibot (Infostealer) Execution & Triage
id: 79974481-37e2-58f5-b20f-23cb301bf6ef
status: production
description: Detects execution, defense evasion, and persistence behaviors associated
  with the Lokibot malware family. Detects browser credential database harvesting,
  master key queries, and DPAPI extraction routines typical of modern infostealers.
references:
- https://malware-families-catalog.systemhelpdesk.com/families/lokibot
- https://malware-families-catalog.systemhelpdesk.com/defensive/infostealer-protection
- https://malware-families-catalog.systemhelpdesk.com/
author: Ricky Jordan, Lead Threat Intelligence Analyst, SystemHelpDesk MSP
date: '2026-10-14'
modified: '2026-10-14'
tags:
- attack.infostealer
- author.ricky-jordan
- source.systemhelpdesk-mfc
- attack.t1555_003
- attack.t1056_001
- attack.t1071_001
- attack.t1547_001
- attack.t1055_012
logsource:
  category: process_creation
  product: windows
detection:
  selection_process:
    Image|endswith:
    - \cmd.exe
    - \powershell.exe
    - \rundll32.exe
    CommandLine|contains:
    - \Google\Chrome\User Data\Default\Login Data
    - \AppData\Roaming\Mozilla\Firefox\Profiles
    - \AppData\Local\Microsoft\Edge\User Data
    - vaultcmd.exe /list
    - dpapi
  condition: selection_process
falsepositives:
- Legitimate enterprise administrative scripts during approved change windows
- Authorized IT software deployment utilities and backup management agents
level: high

rule MALWARE_WIN_LOKIBOT_T3_ATTRIBUTION {
    meta:
        description = "Tier 3 Attribution Fingerprint: Heuristic detection for Lokibot tradecraft"
        tier = "T3 - Attribution Fingerprint"
        confidence = "High (Confirmed Family Signature)"
        author = "SystemHelpdesk Threat Research Desk"
        date = "2026-10-14"
    strings:
        // Core family identifier & heuristic payload signatures
        $attrib_str = "lokibot" ascii wide nocase
        $pe_magic = "MZ" ascii
        $net_marker = "http" ascii nocase
    condition:
        uint16(0) == 0x5a4d and ($attrib_str and $net_marker) and filesize < 15MB
}

Containment & Response Steps

Ordered checklist for responders. Adapt to your environment and engage professional support for active incidents.

1. Isolate the affected workstation from the corporate network immediately to prevent active exfiltration of harvested credentials, cookies, and tokens by Lokibot.
2. Force a global session invalidation and mandatory password reset for all cloud applications, corporate email, VPN accounts, and single sign-on (SSO) portals accessed from the machine.
3. Inspect and clean local browser credential databases (e.g. SQLite storage in Google Chrome, Microsoft Edge, Mozilla Firefox) and cryptocurrency wallet directories targeted by Lokibot.
4. Capture endpoint memory to identify process injection and terminate rogue background processes executing credential scraping routines.
5. Revoke and re-issue all multi-factor authentication (MFA) session cookies and client TLS certificates present on the compromised workstation.

What to Avoid

Common mistakes during response to this family that can destroy evidence, spread the infection, or worsen recovery.

1. Do not allow users to continue typing passwords or accessing sensitive corporate services on an endpoint actively flagged for Lokibot.
2. Avoid assuming credential compromise is limited to local files; all browser-stored cookies and passwords must be treated as exposed.

References & External Analysis

• CISA Alert AA20-266A - LokiBot Information Stealer
• MITRE ATT&CK - LokiBot (S0447)
• NIST NVD - CVE-2017-11882
• Search "lokibot" on VirusTotal (External Analysis)
• Search "lokibot" on Any.Run (Sandbox Sandbox)
• Search "lokibot" on MalwareBazaar (Malware Samples)

Frequently Asked Questions

Related Threats (Category: Infostealer)

Explore other documented threats in the same operational category:

• Ardamax
• Dimnie
• Barys
• Mintluks
• Allinonekeylogger
• Azorult
• Usteal
• Zapchast

Machine-readable

Get this profile as JSON: https://malware-families-catalog.systemhelpdesk.com/api/lokibot.json

Ecosystem & Interactive Environments

This profile is part of the Malware Families Catalog, a public dataset of 2,899 malware families. The catalog is also published across our ecosystem: Hugging Face, Kaggle, Zenodo, Replit, StackBlitz, CodeSandbox, and CodePen.