T1637.001 Domain Generation AlgorithmsCAUTION
Open-source AI-powered offensive security harness for automated penetration testing.
Overview
Open-source AI-powered offensive security harness for automated penetration testing.
eaf7d03223fcOBSERVED · 2026-10-18What it tells the agent
The instruction file, verbatim from the audited commit — this is the text the model reads, and the surface the audit's instruction layer examines. Quoted here so you can judge it without cloning anything.
--- name: "T1637.001_domain-generation-algorithms" description: "Adversaries may use Domain Generation Algorithms (DGAs) to procedurally generate domain names for uses such as command and control communication or malicious application distribution." category: "configuration" version: "18.1" author: "cyberstrike-official" tags: - mitre-attack - mobile - t1637.001 - command-and-control - android - ios - sub-technique technique_id: "T1637.001" tactic: "command-and-control" all_tactics: - command-and-control platforms: - Android - iOS mitre_url: "https://attack.mitre.org/techniques/T1637/001" tech_stack: - android - ios cwe_ids: - CWE-300 chains_with: - T1637 prerequisites: - T1637 severity_boost: T1637: "Chain with T1637 for deeper attack path" --- # T1637.001 Domain Generation Algorithms > **Sub-technique of:** T1637 ## High-Level Description Adversaries may use Domain Generation Algorithms (DGAs) to procedurally generate domain names for uses such as command and control communication or malicious application distribution. DGAs increase the difficulty for defenders to block, track, or take over the command and control channel, as there could potentially be thousands of domains that malware can check for instructions. ## Kill Chain Phase - Command and Control (TA0037) **Platforms:** Android, iOS ## What to Check - [ ] Identify if Domain Generation Algorithms technique is applicable to target mobile environment - [ ] Check Android devices for indicators of Domain Generation Algorithms - [ ] Check iOS devices for indicators of Domain Generation Algorithms - [ ] Assess detection coverage (1 detection strategies) ## How to Test ### Identify Attack Surface Determine if the target mobile environment is susceptible to Domain Generation Algorithms by examining the target platforms (Android, iOS). ### Assess Existing Defenses Review whether mitigations for T1637.001 are in place. If defenses are absent or misconfigured, this technique may be exploitable. ## Remediation Guide No specific mitigations documented for this technique. ## Detection ### Detection of Domain Generation Algorithms ## Risk Assessment | Finding | Severity | Impact | | ------------------------------------------------- | -------- | ------------------- | | Domain Generation Algorithms technique applicable | Low | Command And Control | ## CWE Categories | CWE ID | Title | | ------- | ---------------------------------- | | CWE-300 | Channel Accessible by Non-Endpoint | ## References - [Data Driven Security DGA](https://datadrivensecurity.info/blog/posts/2014/Oct/dga-part2/) - [securelist rotexy 2018](https://securelist.com/the-rotexy-mobile-trojan-banker-and-ransomware/88893/) - [MITRE ATT&CK Mobile - T1637.001](https://attack.mitre.org/techniques/T1637/001)
Trust audit
CAUTIONgrade B · trust 89/100 Install with care. The audit found things worth knowing before you trust its output.
| Layer | What it checks | Result |
|---|---|---|
| L0 | Provenance & inventory | WARN |
| L1 | Static analysis of the code | NA |
| L2 | Instruction surface (what it tells the agent) | PASS |
| L3 | Class-specific surface | PASS |
| L4 | Behavioural (sandbox) | SKIPPED |
What the source does
- Filesystem
- none-observed
- Network
- none-observed
- Shell
- none-observed
- Dependencies
- pinned
- Secrets in source
- none-found
Findings (5)
packages/app/public/apple-touch-icon-v3.png
packages/app/public/apple-touch-icon.png
packages/app/public/favicon-96x96-v3.png
packages/app/public/favicon-96x96.png
packages/app/public/favicon-v3.ico
Gates applied: no_behavioural_pass.
eaf7d03223fcfull audit observations/trust-audit/skill/cyberstrikeus__t1637.001_domain-generation-algorithms.json · Report an issue / request a re-scanAudit history
Every audit this skill has had.
| Date | Source | Verdict | Grade | Score | Change |
|---|---|---|---|---|---|
| 2026-10-18 | eaf7d03223fc | CAUTION | B | 89 | first audit |
Questions
What does the T1637.001 Domain Generation Algorithms skill do?
Open-source AI-powered offensive security harness for automated penetration testing.
Is T1637.001 Domain Generation Algorithms safe to install?
With care. The audit graded it B (89/100) and found 5 things worth knowing before you trust this skill, listed below with the exact line each was found on.
What can T1637.001 Domain Generation Algorithms access on my machine?
The audit observed no filesystem, network or shell use at all in its source.
How current is this page?
The grade is for one exact copy of the source (eaf7d03223fc), read on 2026-10-18. The repository is watched, and a new audit runs when it changes — this is the first audit.
Add this badge to your README
Shows this page's audit result and links back to it. It updates when the audit does.
[](https://modelsatlas.com/skills/cyberstrikeus/t1637-001-domain-generation-algorithms/)
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