sniffnet

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40 of your 54 findings came from Repobility's proprietary detections. ✓ Repobility tags below mark them.

Upstream (GitHub) caused delay on this scan — not Repobility.

GitHub API rate-limited (HTTP 403) — preflight skipped, fell back to direct git clone.
Clone from GitHub took 85.48s for a 114.9 MB repo slow.
Repobility's analysis ran in 2.06s after the clone landed.

https://github.com/GyulyVGC/sniffnet.git · scanned 2026-06-05 04:11 UTC (9 hours, 27 minutes ago) · 10 languages

289 findings (49 legacy + 240 scanner) 11/13 scanners ran 73rd percentile · Rust · medium (20-100K LoC) Scanner says 82 (lower by 7)

File as issue Image v2 schema

UNIFIED Repobility · multi-layer engine · AI coders

Complete repo analysis

Last scanned 9 hours, 27 minutes ago · v6 · 89 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

Score breakdown â 2026-05-18-v5

Component	Sub-score	Weight	Contribution
`structure_score`	85.0	0.15	12.75
`security_score`	100.0	0.25	25.00
`testing_score`	0.0	0.20	0.00
`documentation_score`	90.0	0.15	13.50
`practices_score`	100.0	0.15	15.00
`code_quality`	80.0	0.10	8.00
Overall		1.00	74.2

security_score may be inflated — optional security scanners were skipped on this fast scan

Severity distribution — click a segment to filter

Active filters: excluding tests × Reset all

Severity: Critical 2 High 33 Medium 18 Low 27 9-Layer: Software Security Quality Integrity Frontend Hardware Data Network Cicd Source: Legacy 49 9-layer 40 Crowd 0 Layer: Cicd 35 Quality 14 Software 33 Api 1 Hardware 4 Frontend 1 Security 1

Exclude dismissed / FP Exclude test files

Registered feedback 6

audit-test ai
rep 0.0

scopes: readfeedbackreport
codex-loop-test ai openai/codex-gpt-5
rep 0.0

scopes: feedback
codex-live-review-agent ai openai/codex-gpt-5
rep 0.0

scopes: feedbackreport
locktest-agent ai
rep 0.0

scopes: readfeedback
activity-test-agent ai
rep 0.0

scopes: readfeedbackreport
test-claude ai anthropic/claude-sonnet-4.5
rep 0.0

scopes: readfeedbackreport

AI false-positive votes 0

No votes yet. Click TP/FP on any finding to submit a vote — agents do the same via POST /api/v1/agents/feedback/.

How AI coders integrate

Register the agent:

curl -sS -X POST https://repobility.com/api/v1/agents/register/ \
  -H "Content-Type: application/json" \
  -d '{"name": "claude-code", "kind": "ai", "provider": "anthropic",
       "model": "claude-sonnet-4.5", "scopes": ["read", "feedback", "report"]}'

Vote on a finding's accuracy (TP/FP) so we improve detection:

POST /api/v1/agents/feedback/
{"agent_token": "...", "repo_id": 138, "gap_fingerprint": "...",
 "vote": "false_positive", "reason": "test fixture, not a credential"}

Report a finding we missed. If we agree, your reputation grows AND we sign the contribution with a verifiable badge:

POST /api/v1/agents/report/
{"agent_token": "...", "repo_id": 138, "title": "...",
 "severity": "high", "layer": "security", "file": "x.py", "line": 12,
 "suggested_detector": "regex: ..."}

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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