
Detecting Encoded Payloads Hidden in CSV Fields Fast
A short Python scanner that flags base64 blobs, percent-encoded text, script tags, formula starts and oversized fields in a CSV before you import it.
The format everyone exports and nobody specifies. Delimiters, quoting, encodings and loading it into a database intact.

A short Python scanner that flags base64 blobs, percent-encoded text, script tags, formula starts and oversized fields in a CSV before you import it.

Email is a poor way to move a CSV of real data. Ranked alternatives, from not sending it to expiring links, plus how to encrypt a file you must attach.

The GDPR duties that land on a CSV of customer data, from minimization and retention to access requests and deletion, turned into practical engineering steps.

Pick CSV upload limits from measured parse and load speed, enforce each where it is cheapest, and reject bad shapes with a script that names the line.

A plain SHA-256 of an email is reversible with a guess list. Use a keyed HMAC for tokens and partial masking for display, with a script that does both.

Remove, generalize and pseudonymize the personal columns of a CSV with DuckDB before it leaves your team, then check that no one can still be singled out.

A CSV upload needs no exploit to take a server down: compressed bombs, one huge field, an unclosed quote and wide files, each with the limit that stops it.

Convert JSON to YAML with a YAML library so strings like yes, 3.10 and 02134 keep their quotes, and see what string formatting does to the same data.

A small function that neutralizes formula triggers in CSV text cells, why negative numbers must be exempt, and a test table to keep the fix from regressing.

How a name starting with an equals sign turns your CSV export into an attack on whoever opens it, what payloads do, and how to test your own exports.

How a CSV file hurts a team: formula injection, hostile uploads, leaked personal data, hidden payloads and risky loaders, with a fix and a check for each.

Convert YAML to JSON with PyYAML and json, and handle the cases that break it: dates, sets, integer keys, NaN, anchors, several documents and complex keys.
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