Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add MingyiSecLab/Mingyi-Atlas --skill ssrf-analysisgit clone --depth 1 https://github.com/MingyiSecLab/Mingyi-AtlasWrote 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/mingyiseclab/mingyi-atlas/ssrf-analysis)<a href="https://agentmods.dev/skills/mingyiseclab/mingyi-atlas/ssrf-analysis"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/ssrf-analysis/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/mingyiseclab/mingyi-atlas/ssrf-analysis"><img src="https://agentmods.dev/badge/skills/mingyiseclab/mingyi-atlas/ssrf-analysis.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00064 | $0.01402 |
| Opus 5 | $0.00032 | $0.00701 |
| Sonnet 5 | $0.00013 | $0.00280 |
| Haiku 4.5 | $0.00006 | $0.00140 |
Grade C, and why
ssrf-analysis 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://169.254.169.254/` (AWS IMDS) | IAM role creds → full AWS takeover | Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
- Python: `requests.get(user_url)`, `httpx.get`, `urllib.request.urlopen`, `aiohttp.get` This is a copy
94% identical to ssrf — 5 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 106 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SSRF Hunting Playbook
SSRF is one of the highest-yield vuln classes for 0-day work because (a) it's often medium severity in isolation but chains to critical when it reaches cloud metadata, internal admin panels, or unprotected Redis/Elasticsearch, and (b) modern frameworks have dozens of bypass classes that scanners miss.
1. Sources (user-controlled input that becomes a URL)
Look for any parameter that gets fed into an HTTP client:
- Python:
requests.get(user_url),httpx.get,urllib.request.urlopen,aiohttp.get - Node:
fetch(userUrl),axios.get,http.request,got,node-fetch - Java:
HttpURLConnection,HttpClient.send,URL.openConnection,OkHttpClient - Go:
http.Get(u),http.NewRequest,net.Dial("tcp", u) - Ruby:
Net::HTTP.get,URI.open,Faraday.get - PHP:
file_get_contents($url),curl_exec,fsockopen
Grep patterns to run via bash:
semgrep --config p/ssrf /workspace/src --sarif -o /workspace/sem-ssrf.sarif
grep -rE 'requests\.get\(|httpx\.|urllib.*urlopen|fetch\(|axios\.|http\.Get\(|URL\(' /workspace/src
2. Sinks that make SSRF worth reporting
| Sink | Impact |
|---|---|
http://169.254.169.254/ (AWS IMDS) |
IAM role creds → full AWS takeover |
http://metadata.google.internal/ |
GCP service-account token |
http://169.254.169.254/metadata/v1/ |
Azure IMDS |
http://127.0.0.1:6379/ (Redis) |
Unauthenticated RCE via Lua eval / cron |
http://127.0.0.1:9200/ |
Elasticsearch data leak + RCE |
http://127.0.0.1:2375/ |
Docker daemon — container escape |
gopher://, dict://, file:// |
Protocol smuggling (SMTP, memcached) |
Internal *.svc.cluster.local |
Kubernetes service mesh lateral |
3. Bypass classes (why scanners miss real bugs)
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 · 106 lines · 64 tokens per session scan C ba66df580566
ssrf-analysis is a skill published in the GitHub repository MingyiSecLab/Mingyi-Atlas (11 stars, last pushed 2mo ago), licensed Apache-2.0. It adds 64 tokens to every session and 1,402 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it C with 2 findings (cloud metadata endpoint, makes network calls). It is 94% identical to ssrf, differing in 5 lines, and is treated as a copy.
Other skills, from other repositories
cis-aws-foundations-4.3
Ensure AWS Config is enabled in all regions.
cis-aws-foundations-6.5
Ensure the default security group of every VPC restricts all traffic.
cis-aws-foundations-2.1.3
Ensure Organizations management account is not used for workloads.
cis-aws-foundations-2.5
Ensure MFA is enabled for the 'root' user account.
cis-aws-foundations-2.7
Eliminate use of the 'root' user for administrative and daily tasks.
cis-aws-foundations-4.4
Ensure that server access logging is enabled on the CloudTrail S3 bucket.