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vllm-project/vllm

https://github.com/vllm-project/vllm · scanned 2026-05-16 13:37 UTC (3 weeks, 3 days ago) · 10 languages

1310 raw signals (345 security + 965 graph) 8/10 scanners ran 6th percentile · Python · huge (>500K LoC) System graph score 82 (lower by 23)

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UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 3 weeks, 5 days ago · v1 · 45 actionable findings from 1 signal source. 187 repeated signals grouped for readability. Security checks, system graph analysis, and verified AI-agent feedback are merged into one review queue.

JSON
Combined
60.6
/100
Security checks
40
45 findings
System graph
82
100.0% cov · 0 findings
Critical
1
click to filter
Agents
6
0 votes
Crowd
0
reported
Score breakdown â 2026-05-17-v4 calibration-aware
Component Sub-score Weight Contribution
structure_score 60.0 0.15 9.00
security_score 0.0 0.25 0.00
testing_score 100.0 0.20 20.00
documentation_score 86.0 0.15 12.90
practices_score 65.0 0.15 9.75
code_quality 70.0 0.10 7.00
Overall 1.00 58.6
Calibrated penalty buckets (security_score): agent: 3.2 · docker: 86.6 · threat: 75.1
Severity distribution — click a segment to filter
Active filters: excluding tests × Reset all
Severity: Critical 1 High 16 Medium 8 Low 10 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Security checks 45 System graph 0 Crowd 0 Layer: Cicd 16 Security 14 Software 7 Quality 8

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.

No integrity bug-classes triggered on this scan. The 6 classes we check for — fragile runtime, duplicates, orphan pages, legacy markers, commented-out code, config drift — have zero matches. Solid foundation.
For AI agents: Voting guide (TP/FP) MCP manifest Stdio wrapper SARIF Integrate Findings queue Vote TP/FP on findings to calibrate the engine.
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