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This is an excellent excellent resource regardless of whether you agree/disagree with the author's conclusions, simply by virtue of being a great list of broken down problems, well described, & accompanied by good technical descriptions of proposed fixes (again independent of your opinion on those fixes).

Just an excellent example of how to approach & elucidate a problem domain.

eazy

secrets.forEach(secret => logMessage = logMessage.replaceAll(secret, '**'))

Nice article; very comprehensive.

As far as run-time exposure prevention goes, I feel like in-band signaling might work better than out-of-band for this problem. Along the lines of the taint checking technique mentioned, you can insert some magic string (say, some recognizable prefix + a randomly generated UUID) into your sensitive strings at the source, that you then strip out at the sink. (Or wrap your secrets in a pair of such magic strings.) Then block or mask any strings containing that magic string from making it into any persisted data, including logs. And it will be easy to identify the points of exposure, since they will be wherever you call your respective seal()/unseal() function or such.

> If you shift from “any string can be a secret” to “secrets are secrets”, it makes things a lot easier to reason about and protect.

> const secret = new Secret("...")

one of those things that's obvious in retrospect. That's a cute trick I'll definitely be stealing.

This is an excellent write-up of the problem. New hires out of college/bootcamps often have no awareness of the risks here at all. Sometimes even engineers with years of experience but no operational mentorship in their career.

The kitchen sink example in particular is one that trips up people. Without knowing the specifics of how a library may deal with failure edge cases, it can catch you off guard (e.g., axios errors including API key headers).

A lot of these problems come from architectures where secrets go over the wire instead of just using signatures/ids. But in cases where you have to use some third party platform, there's often no choice.

It feels like it would be better to make strings as safe, and only log ones that have been marked as such.

I certainly agree with the desire to keep secrets out of logs, but isn’t the entire log itself also considered to be secret? Even a perfectly sanitized log probably contains lots of data about your production environment that you wouldn’t want to share with adversaries (e.g. peak usage hours).

With java theres a GuardedString implementation https://docs.oracle.com/en/middleware/idm/identity-governanc...

One particular thing to be careful of are core dumps.

What I did at a previous shop was remove the passwords as part of a smart gdb script that runs when the core is dumped, before it gets written to a readable location.

Writing the script also helped to demonstrate how to extract the passwords in the first place.

I think secrets ending up in the log is an issue but who should have access to view logs of what log should also be an important that is often ignored. This is also scope down the surface area of leakage.

Great read.

> And while people will write the code that accidentally introduces sensitive data into logs, they’re also the ones that will report, respond, and fix them.

This should probably be the first point and not the last.

oh god - I had that come up in an issue at work just about a month ago. A development system used really simple usernames and passwords since it was just for testing but all the lines with one of those got gobbled up because they had "secrets" in them.

I have very strong opinions on this issue that boils down to. _why are you logging everything you lazy asses_ and _adding all the secrets into another tool just to scan for them in logs just adds another point for them to leak_...

Especially since the ability of lines getting censored even when the secrets were just part of words showed that probably no hashing was involved.

But its a security tool so it stays. I kinda feel like Cassandra but I think I can already predict a major security issue with it or others with the same functionality in the future. its like some goddamn blind spot that software that is to prevent X cannot be vulnerable to X but somehow often is vulnerable because prevention of X and not being vulnerable to X are two separate things somehow.

Loved this “lead bullets” framing, especially the parts on taint checking, scanners, and pre-processing/sampling logs. One practical add-on to the "Sensitive data scanners" section is verification: can you tell which candidates are actually live creds?

We’ve been working on an open source tool, Kingfisher, that pairs fast detection (Hyperscan + Tree-Sitter) with live validation for a bunch of providers (cloud + common SaaS) so you can down-rank false positives and focus on the secrets that really matter. It plugs in at the chokepoints this post suggests: CI, repo/org sweeps, and sampled log archives (stdin/S3) after a Vector/rsyslog hop.

Examples:

  kingfisher scan /path/to/app.log --only-valid
  kingfisher scan --s3-bucket my-logs --s3-prefix prod/2025/09/

Baselines help keep noise down over time.

Repo: https://github.com/mongodb/kingfisher (Apache-2.0)

Disclosure: I help maintain Kingfisher.

I think the big problem is when secrets can be anywhere in a string and you don't control the input (e.g, library stacktraces, HTTP responses, JSON that was stringified). You need to pass the secrets to the logger so it can be redacted, it's heavily dependent on the dev and easy to forget during review.

And an exact match is just part of the problem; if a dev redacts the end and another dev redacts the start, you can still reassemble the secret with enough logs.

Just excellent. Lots of (common from my experience) examples, potential fixes and self-contained explanations. Nice.

Great article! I will definitely reference it in my upcoming discussions. I had some hard time defending having an EU based o11y stack for our EU based infra. I found it hard to articulate on the spot that there are myriads of places where sensitive/personal data can get in the logs and cause leaks, or make GDPR angry.