Recon Skills is a pack of security-testing skills covering reconnaissance, web applications, APIs, authentication, vulnerability validation, cloud infrastructure, and reporting. Security professionals use it for authorized assessments of systems they own or have written permission to test. The catalogue entries are individual skills from the pack.
Getting it into your agent
It runs from inside its repository, so the clone comes first — what it calls does not travel with the file alone.
git clone --depth 1 https://github.com/uphiago/recon-skillsnpx agentmods add skills/uphiago/recon-skills/zimbra-attackWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/uphiago/recon-skills/zimbra-attack)<a href="https://agentmods.dev/skills/uphiago/recon-skills/zimbra-attack"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/zimbra-attack/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/uphiago/recon-skills/zimbra-attack"><img src="https://agentmods.dev/badge/skills/uphiago/recon-skills/zimbra-attack.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 6 findings, up to high
These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →
- high Server-Side Request Forgery · line 172 Code accesses a cloud instance metadata endpoint (e.g. 169.254.169.254). A single request can return temporary IAM credentials, making this a high-value SSRF target for credential theft.Fix: Remove access to cloud metadata endpoints unless strictly required. If metadata is needed, restrict it (e.g. IMDSv2 with hop limit) and never expose returned credentials.
- medium Data Exfiltration · line 42 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 90 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 113 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 137 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
- medium Data Exfiltration · line 146 Data is being sent to an external URL. This could be legitimate telemetry or data exfiltration. Manual review is recommended.Fix: Verify the destination URL is trusted and necessary. Remove or replace with documented APIs. Ensure no secrets, tokens, or PII are transmitted.
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00026 | $0.02592 |
| Opus 5 | $0.00013 | $0.01296 |
| Sonnet 5 | $0.00005 | $0.00518 |
| Haiku 4.5 | $0.00003 | $0.00259 |
Grade C, and why
zimbra-attack scanned grade C with 2 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 10d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Cloud metadata endpointhighServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
"http://metadata.google.internal/" Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
compatibility: Requires curl, nmap, python3, masscan, subfinder, httpx, nuclei How it starts
The opening of the file, as written. The whole thing — 220 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Zimbra Attack Skill
Zimbra Collaboration Suite attack surface — SOAP API user enumeration without authentication, version fingerprinting, UploadServlet path traversal (CVE-2022-37042), /service/proxy internal SSRF, and Admin console access. Confirmed on IGN Argentina (Zimbra 8.8.11, admin user confirmed, UploadServlet active), gov-finance-portal (Zimbra webmail, SOAP auth functional), and ITERJ (Zimbra webmail active).
When to Use
- Target has
webmail.,mail., orzimbra.subdomains. - Redirect to
/zimbra/path on mail server. - Server header or page title contains "Zimbra".
- After
subdomain-enumerationdiscovers webmail hosts. - Government, university, or enterprise targets (Zimbra is common in these sectors).
Prerequisites
terminalwith curl, python3.- Target Zimbra URL (typically
https://webmail.target.com). - For CVE exploitation: knowledge of target Zimbra version.
How to Run
# Quick Zimbra detection
curl --max-time 30 --connect-timeout 10 -skI "https://TARGET/" | grep -iE "zimbra|zmail"
# SOAP user enumeration
curl --max-time 30 --connect-timeout 10 -sk -X POST "https://TARGET/service/soap/" \
-H "Content-Type: application/xml" \
-d '<soap:Envelope xmlns:soap="http://www.w3.org/2003/05/soap-envelope"><soap:Header><context xmlns="urn:zimbra"/></soap:Header><soap:Body><AuthRequest xmlns="urn:zimbraAccount"><account by="name">admin@TARGET</account><password>test</password></AuthRequest></soap:Body></soap:Envelope>'
Quick Reference
| Endpoint | What It Reveals | Risk |
|---|---|---|
/service/soap/ |
SOAP API — user enum, auth testing | High |
/service/soap/AuthRequest |
Differentiates valid user vs bad password | High |
/zimbraAdmin/ |
Admin console (if exposed) | Critical |
/service/upload?fmt=ext |
UploadServlet (CVE-2022-37042) | Critical |
/service/proxy?target= |
Internal SSRF | Critical |
/service/extension/ |
Extension listing | Medium |
/zimbra/downloads/index.html |
Version disclosure | Medium |
/zimbra/skins/_base/logos/LoginBanner.png |
Zimbra branding confirmation | Info |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 10d ago First seen · 220 lines · 26 tokens per session scan C 706b8863f1c0
zimbra-attack is a skill published in the GitHub repository uphiago/recon-skills (1,251 stars, last pushed 8d ago), licensed MIT. It adds 26 tokens to every session and 2,592 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it C with 2 findings (cloud metadata endpoint, makes network calls). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
performing-soap-web-service-security-testing
Perform security testing of SOAP web services by analyzing WSDL definitions and testing for XML injection, XXE, WS-Security bypass, and SOAPAction spoofing.
implementing-cloud-dlp-for-data-protection
Implementing Cloud Data Loss Prevention (DLP) using Amazon Macie, Azure Information Protection, and Google Cloud DLP API to discover, classify, and protect sensitive data across cloud storage, databases, and data pipelines.
auditing-gcp-iam-permissions
Auditing Google Cloud Platform IAM permissions to identify overly permissive bindings, primitive role usage, service account key proliferation, and cross-project access risks using gcloud CLI, Policy Analyzer, and IAM Recommender.
auditing-terraform-infrastructure-for-security
Auditing Terraform infrastructure-as-code for security misconfigurations using Checkov, tfsec, Terrascan, and OPA/Rego policies to detect overly permissive IAM policies, public resource exposure, missing encryption, and insecure defaults before cloud deployment.
detecting-compromised-cloud-credentials
Detecting compromised cloud credentials across AWS, Azure, and GCP by analyzing anomalous API activity, impossible travel patterns, unauthorized resource provisioning, and credential abuse indicators using GuardDuty, Defender for Identity, and SCC Event Threat Detection.
detecting-misconfigured-azure-storage
Detecting misconfigured Azure Storage accounts including publicly accessible blob containers, missing encryption settings, overly permissive SAS tokens, disabled logging, and network access violations using Azure CLI, PowerShell, and Microsoft Defender for Storage.