Entropy, a CLI that scans files to find high entropy lines (might be secrets)
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Re: Entropy, a CLI that scans files to find high entropy lines (might be secrets)
#2Re: Entropy, a CLI that scans files to find high entropy lines (might be secrets)
#3Re: Entropy, a CLI that scans files to find high entropy lines (might be secrets)
#4This looks like a very handy CLI tool. Nice Go code also. Thanks.
Re: Entropy, a CLI that scans files to find high entropy lines (might be secrets)
#5Re: Entropy, a CLI that scans files to find high entropy lines (might be secrets)
#6Re: Entropy, a CLI that scans files to find high entropy lines (might be secrets)
#7If you properly encode your secret it will have the entropy of its surroundings.
For example you can hide a string of entropy (presumably something encrypted) in text as a biased output of an LLM. To recover it you would use the same LLM and measure deviations from next-token probabilities. This will also fool humans examining it as the sentence will be coherent.
Re: Entropy, a CLI that scans files to find high entropy lines (might be secrets)
#8- trufflehog: https://github.com/trufflesecurity/trufflehog
- detect-secrets: https://github.com/Yelp/detect-secrets
- semgrep secrets: https://semgrep.dev/products/semgrep-secrets -- (Paid, but may be included in existing licenses in some cases
Re: Entropy, a CLI that scans files to find high entropy lines (might be secrets)
#9This is very cool, but I have a thought - I see this as a last line of defense, and I am concerned that this would this give a false sense of security leading people to be more reckless with secrets.
I hope that more secrets adopt a GitHub like convention where they are prefaced with an identifier string so that you do not require heuristics to detect them.
Re: Entropy, a CLI that scans files to find high entropy lines (might be secrets)
#10Note that in an adversarial setting this will only be effective against careless opponents. If you properly encode your secret it will have the entropy of its surroundings. For example you can hide a string of entropy (presumably something encrypted) in text as a biased output of an LLM. To recover it you would use the same LLM and measure deviations from next-token probabilities. This will also fool humans examining…