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AWS IAM Key Leakage: The AKIA Nightmare

The $10,000 Mistake​

It is a story every DevOps engineer dreads. A Data Scientist wants to test a script locally. They hardcode their AWS credentials into a Jupyter Notebook cell:

aws_access_key_id = "AKIAIOSFODNN7EXAMPLE"
aws_secret_access_key = "wJalrXUtnFEMI/K7MDENG/bPxRfiCYEXAMPLEKEY"

They run the cell, debug the code, and then delete the lines. They push the notebook to GitHub.

Three hours later, the company gets an alert: "Unusual EC2 instance activity." Someone has spun up 50 p3.16xlarge instances for crypto mining. The bill is already at $10,000.

Why Deleting the Code Didn't Help​

Jupyter Notebooks (.ipynb) are JSON files. When you run a cell, the output is often cached in the file structure. Even if you delete the input code, the variables might persist in the runtime state or be recoverable from git history if not properly scrubbed.

Furthermore, many developers forget that AKIA keys are Long-Term Credentials. Unlike ASIA keys (which are temporary), AKIA keys never expire until manually rotated.

The "AKIA" Signal​

AWS Access Key IDs have a distinct signature: they always start with AKIA (for users) or ASIA (for temporary roles), followed by 16 alphanumeric characters.

Hackers use automated scrapers that monitor public repositories (GitHub, GitLab, Hugging Face) in real-time. They look specifically for this AKIA regex pattern. The time from "git push" to "compromise" is often less than 60 seconds.

Preventing Leaks​

  1. Use IAM Roles: If running on EC2 or Lambda, never use keys. Use Instance Profiles.
  2. Use .env files: Never hardcode secrets. Use python-dotenv.
  3. Scan Before Commit:

You must audit your notebooks before they leave your local machine. Veritensor scans both the code cells and the execution outputs of notebooks for high-entropy strings and specific AWS patterns.

veritensor scan ./notebooks
# Output: CRITICAL: AWS Access Key detected in cell 4

Detecting the leak locally is the only way to beat the scrapers.