Hexlock – Replace PII in text with fake data that has the same format
PII redaction that preserves data format, tokenized values stay usable in LLM pipelines - ttarvis/hexlock
Hexlock – Replace PII in text with fake data that has the same format launched on April 30, 2026 with 5 votes, #1,846 of 21,248 launches that month and more than 75% of that year's launches.
What it does
hexlock transforms sensitive data before it reaches your LLM. Phone numbers, emails, and card numbers become realistic tokens that your prompt still understands, and can be restored after. hexlock is a tool for preventing sensitive data from being used with LLMs. It replaces sensitive data but preserves the format so the LLM understands it still. Then it rehydrates the response with the original data. The sensitive data never gets sent to the LLM. Data types protected include email, phone, SSNs, driver's license identifiers, passport IDs, credit card, GitHub tokens, Anthropic tokens, AWS keys, and more. See CONFIG for more types. pip install hexlock Usage import hexlock # ephemeral — no key…from github.com
Does a similar job
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TokenDagger – A tokenizer faster than OpenAI's Tiktoken2025 · github.com · ▲281TokenDagger is a drop-in replacement for OpenAI’s Tiktoken (the tokenizer behind Llama 3, Mistral, GPT-3.*, etc.). It’s written in C++ 17 with thin Python bindings, keeps the exact same BPE vocab/special-token rules, and focuses on raw speed. I’m teaching myself LLM internals by re-implementing the stack from first principles. Profiling TikToken’s Python/Rust implementation showed a lot of time was spent doing regex matching. Most of my perf gains come from a) using a faster jit-compiled regex engine; and b) simplifying the algorithm to forego regex matching special tokens at all.…

Stun LLMs with thousands of invisible Unicode charactersNov 2025 · gibberifier.com · ▲206I made a free tool that stuns LLMs with invisible Unicode characters. *Use cases:* Anti-plagiarism, text obfuscation against LLM scrapers, or just for fun! Even just one word's worth of “gibberified” text is enough to block most LLMs from responding coherently.
- ALA local-first, reversible PII scrubber for AI workflowsDec 2025 · medium.com · ▲38
Hi HN, I’m one of the maintainers of Bridge Anonymization. We built this because the existing solutions for translating sensitive user content are insufficient for many of our privacy-concious clients (Governments, Banks, Healthcare, etc.). We couldn't send PII to third-party APIs, but standard redaction destroyed the translation quality. If you scrub "John" to "[PERSON]", the translation engine loses gender context (often defaulting to masculine), which breaks grammatical agreement in languages like French or German. So we built a reversible, local-first pipeline for Node.js/Bun. Here…


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