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Get Started Free →IDOR Detector - Insecure Direct Object Reference Detection
.claude/skills/shadd0wtaka-idor-detector/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-04 | ✗→✓ | ▲ Improved | -60% | 0% |
| case-06 | ✗→✓ | ▲ Improved | -62% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -39% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -41% | 0% |
| case-09 | ✗→✓ | ▲ Improved | -78% | 0% |
IDOR Detector - Insecure Direct Object Reference Detection
Category: scanning — Web Application Testing
IDOR Detector - Insecure Direct Object Reference Detection Phase 8.3: Business-Logic Testing - IDOR Detection
Dieses Modul implementiert IDOR-Vulnerability-Detection durch Testen von sequentiellen IDs und verschiedenen Benutzer-Sessions.
python# Auto-registered in tool_registry under "scanning" result = await tool_orchestrator.execute("idor_detector", target="example.com")
bashidor-detector --help
deep-recon --tool idor_detector --target example.comscanningzen-agents_agent_run agent_type=scanning tool=idor_detectorPOST /tools/execute with {"tool_name": "idor_detector", "target": "example.com"}| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 5,738 | 6,955 | +21% | 1 | 1 | 0% | 497 | 773 | +56% | 0 | 0 | — |
case-02 | fail→fail | 9,750 | 19,354 | +99% | 1 | 1 | 0% | 1,537 | 3,556 | +131% | 0 | 0 | — |
case-03 | fail→fail | 6,580 | 9,453 | +44% | 1 | 1 | 0% | 563 | 1,149 | +104% | 0 | 0 | — |
case-04 | fail→pass | 15,945 | 5,473 | -66% | 1 | 1 | 0% | 3,020 | 1,193 | -60% | 0 | 0 | — |
case-05 | pass→pass | 7,842 | 1,272 | -84% | 1 | 1 | 0% | 1,177 | 409 | -65% | 0 | 0 | — |
case-06 | fail→pass | 10,270 | 2,109 | -79% | 1 | 1 | 0% | 1,461 | 562 | -62% | 0 | 0 | — |
case-07 | fail→pass | 8,395 | 2,779 | -67% | 1 | 1 | 0% | 1,157 | 703 | -39% | 0 | 0 | — |
case-08 | fail→pass | 7,295 | 2,885 | -60% | 1 | 1 | 0% | 1,272 | 751 | -41% | 0 | 0 | — |
case-09 | fail→pass | 17,461 | 2,110 | -88% | 1 | 1 | 0% | 2,975 | 645 | -78% | 0 | 0 | — |
case-10 | pass→pass | 10,462 | 14,940 | +43% | 1 | 1 | 0% | 1,757 | 1,050 | -40% | 0 | 0 | — |
case-11 | pass→pass | 8,213 | 3,945 | -52% | 1 | 1 | 0% | 1,304 | 805 | -38% | 0 | 0 | — |
case-12 | fail→pass | 5,891 | 2,601 | -56% | 1 | 1 | 0% | 1,050 | 602 | -43% | 0 | 0 | — |
case-13 | pass→pass | 7,422 | 6,807 | -8% | 1 | 1 | 0% | 1,203 | 436 | -64% | 0 | 0 | — |
case-14 | pass→pass | 11,533 | 13,968 | +21% | 1 | 1 | 0% | 1,963 | 790 | -60% | 0 | 0 | — |
case-15 | fail→pass | 22,146 | 3,462 | -84% | 1 | 1 | 0% | 3,807 | 564 | -85% | 0 | 0 | — |
case-16 | pass→pass | 10,420 | 2,123 | -80% | 1 | 1 | 0% | 1,734 | 625 | -64% | 0 | 0 | — |
case-17 | fail→pass | 19,870 | 1,404 | -93% | 1 | 1 | 0% | 3,525 | 416 | -88% | 0 | 0 | — |
case-18 | fail→pass | 6,843 | 3,348 | -51% | 1 | 1 | 0% | 1,091 | 629 | -42% | 0 | 0 | — |
case-19 | fail→fail | 6,816 | 7,004 | +3% | 1 | 1 | 0% | 1,058 | 517 | -51% | 0 | 0 | — |
case-20 | pass→pass | 13,892 | 15,584 | +12% | 1 | 1 | 0% | 2,356 | 3,066 | +30% | 0 | 0 | — |
case-21 | pass→pass | 7,829 | 8,230 | +5% | 1 | 1 | 0% | 1,369 | 1,637 | +20% | 0 | 0 | — |
case-22 | pass→pass | 3,941 | 5,908 | +50% | 1 | 1 | 0% | 674 | 1,054 | +56% | 0 | 0 | — |
DecimalAI ran this skill against gemini-3.6-flash twice over the same eval suite — once with the skill loaded and once without — and compared the two runs case by case. 22 cases were attempted. The headline lift of +41 percentage points is the difference between those two pass rates over the 22 comparable cases.
Without the skill loaded, the model failed this case. With it loaded, the same prompt on the same model passed. This is one improved case from the latest verified run; every case, including any that regressed, is in the table above.
Other measured skills in the registry, with their headline benchmark lift.