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sgl-project/sglang

https://github.com/sgl-project/sglang · scanned 2026-06-16 00:11 UTC (2 months, 2 weeks ago) · 10 languages

3566 raw signals (206 security + 3360 graph) 0th percentile · Python · huge (>500K LoC)

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Complete repo analysis

Last scanned 2 months, 2 weeks ago · v2 · last Δ -7.8 (diff) · 1417 actionable findings from 2 signal sources. 1068 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

JSON
Combined
42.0
/100
Security checks
37
42 findings
System graph
47
100.0% cov · 1375 findings
Critical
6
click to filter
Agents
10
0 votes
Crowd
0
reported
Score breakdown â 2026-05-17-v4 calibration-aware
Component Sub-score Weight Contribution
structure_score 40.0 0.15 6.00
security_score 22.9 0.25 5.72
testing_score 100.0 0.20 20.00
documentation_score 66.0 0.15 9.90
practices_score 65.0 0.15 9.75
code_quality 46.9 0.10 4.69
Overall 1.00 56.1
Calibrated penalty buckets (security_score): agent: 3.2 · authz: 1.2 · docker: 50.6 · threat: 45.4 · journey: 2.5
Severity distribution — click a segment to filter
Active filters: search: "legacy-markers" × excluding tests × Reset all
Severity: Critical 6 High 127 Medium 127 Low 660 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 42 System graph 1375 Crowd 0 Layer: Quality 1175 Security 132 Cicd 17 Software 25 Frontend 3 Network 2 Hardware 7 Api 56
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Scan summary Repository scanned at 47.3/100 with 100.0% coverage. It contains 56039 nodes across 30 cross-layer flows, written primarily in mixed languages. Engine surfaced 1837 findings — concentrated in quality (1177), cicd (447), security (119). Risk profile is high: 2 critical, 117 high, 214 medium. Recommended next step: open the quality layer findings first — that's where the highest-impact wins live.

Showing 0 of 1417 actionable findings. 2485 raw detector signals were grouped into reader-sized issues. Click TP / FP to vote on a finding's accuracy — votes adjust the confidence weighting and improve detection across the platform.

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