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Sebsqu/HRM

https://github.com/Sebsqu/HRM.git · scanned 2026-05-16 20:30 UTC (1 day ago) · 10 languages

27 findings (11 legacy + 16 scanner) Scanner says 97 (lower by 29)

UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 1 day ago · v2 · 19 findings from 2 sources. Findings combine the legacy security pipeline AND the multi-layer engine (atlas, wiring, flows, ranked) AND verified AI agent contributions.

JSON
{# ── 2026-05-17 R27 #5: score breakdown panel ────────────────────── Surfaces the score_breakdown JSON that's been silently stored on Repository for months. Turns hidden math into a trust signal. #}
Severity distribution — click a segment to filter
Active filters: excluding tests × Reset all
Severity: Critical 0 High 0 Medium 8 Low 9 Source: Legacy 11 9-layer 8 Crowd 0 Layer: Quality 9 Security 4 Software 3 Api 1 Frontend 1 Cicd 1

Bug-class explainers. Each card groups findings of the same shape — these are the patterns most likely to ship to prod and reappear in future scans unless you systematically fix the cause, not just the instance.

Fragile runtime 6 findings
What it is: Code that runs but breaks under predictable input — division by zero, missing keys, unbounded loops, off-by-one slicing.
Why it matters: Reaches production undetected because happy-path tests pass. First user with a weird input crashes the request.
How AI causes it: AI loves writing the happy path; doesn't probe edge cases unless explicitly asked.
Fix approach: Add property-based tests. Wrap external inputs with explicit validators. Use the framework's typed deserializer (Pydantic, attrs).
6 matching findings on this repo
  • medium Public web service has no security.txt .well-known/security.txt
  • medium Public web app has no Content Security Policy index.html
  • low robots.txt does not advertise a sitemap public/robots.txt
  • low Public web app has no humans.txt humans.txt
  • low Public docs site has no llms.txt llms.txt
  • low Public web app has no sitemap sitemap.xml
View all fragile runtime findings →
Config drift 2 findings
What it is: Settings duplicated across env files, Docker compose, K8s, and code defaults, all with slightly different values.
Why it matters: Production behaviour depends on whichever copy your loader reads first. Subtle bugs in staging that don't reproduce in dev.
How AI causes it: AI writes new config from memory rather than reading the existing source.
Fix approach: Pick one source of truth (env vars + a settings module). Have every other place import from there. Lint for duplicates in CI.
2 matching findings on this repo
  • medium No CI/CD configuration found
  • low File has no detected symbols: vite.config.js
View all config drift findings →
{# ── 2026-05-17 Round 14: AI-agent bridge footer ────────────────────── Discoverability: the /agents/voting/ guide + MCP manifest exist but aren't linked from anywhere users actually land. Small, opt-in footer. #}
For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.
For AI agents + API integrations
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