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Get Started Free →Adversaries may manipulate hardware components in products prior to receipt by a final consumer for the purpose of data or system compromise.
.claude/skills/cyberstrikeus-t1474-002-compromise-hardware-supply-chain/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | -1% | 0% |
| case-03 | ✗→✓ | ▲ Improved | -23% | 0% |
| case-06 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -55% | 0% |
| case-08 | ✗→✓ | ▲ Improved | -78% | 0% |
> Sub-technique of: T1474
Adversaries may manipulate hardware components in products prior to receipt by a final consumer for the purpose of data or system compromise. By modifying hardware or firmware in the supply chain, adversaries can insert a backdoor into consumer networks that may be difficult to detect and give the adversary a high degree of control over the system.
Platforms: Android, iOS
Determine if the target mobile environment is susceptible to Compromise Hardware Supply Chain by examining the target platforms (Android, iOS).
Review whether mitigations for T1474.002 are in place. If defenses are absent or misconfigured, this technique may be exploitable.
Security updates may contain patches to integrity checking mechanisms that can detect unauthorized hardware modifications.
| Finding | Severity | Impact | | ----------------------------------------------------- | -------- | -------------- | | Compromise Hardware Supply Chain technique applicable | High | Initial Access |
| CWE ID | Title | | ------ | ------------------------- | | CWE-20 | Improper Input Validation |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-02 | fail→fail | 30,022 | 43,518 | +45% | 1 | 1 | 0% | 3,938 | 6,678 | +70% | 0 | 0 | — |
case-01 | fail→pass | 31,852 | 25,021 | -21% | 1 | 1 | 0% | 4,158 | 4,096 | -1% | 0 | 0 | — |
case-03 | fail→pass | 43,221 | 32,831 | -24% | 1 | 1 | 0% | 6,028 | 4,631 | -23% | 0 | 0 | — |
case-04 | pass→pass | 8,655 | 10,086 | +17% | 1 | 1 | 0% | 1,374 | 1,619 | +18% | 0 | 0 | — |
case-05 | pass→pass | 15,174 | 5,090 | -66% | 1 | 1 | 0% | 1,572 | 1,797 | +14% | 0 | 0 | — |
case-06 | fail→pass | 21,458 | 18,050 | -16% | 1 | 1 | 0% | 2,632 | 3,028 | +15% | 0 | 0 | — |
case-07 | fail→pass | 18,632 | 8,771 | -53% | 1 | 1 | 0% | 2,891 | 1,313 | -55% | 0 | 0 | — |
case-08 | fail→pass | 33,751 | 8,172 | -76% | 1 | 1 | 0% | 6,192 | 1,350 | -78% | 0 | 0 | — |
case-09 | fail→pass | 18,978 | 10,888 | -43% | 1 | 1 | 0% | 2,112 | 1,528 | -28% | 0 | 0 | — |
case-10 | fail→pass | 20,892 | 23,747 | +14% | 1 | 1 | 0% | 2,725 | 3,277 | +20% | 0 | 0 | — |
case-20 | fail→pass | 23,246 | 46,961 | +102% | 1 | 1 | 0% | 2,593 | 6,571 | +153% | 0 | 0 | — |
case-11 | pass→pass | 11,350 | 8,800 | -22% | 1 | 1 | 0% | 1,670 | 1,428 | -14% | 0 | 0 | — |
case-12 | pass→pass | 25,532 | 28,637 | +12% | 1 | 1 | 0% | 3,099 | 3,848 | +24% | 0 | 0 | — |
case-13 | pass→pass | 15,947 | 15,810 | -1% | 1 | 1 | 0% | 2,006 | 2,508 | +25% | 0 | 0 | — |
case-14 | pass→pass | 16,699 | 21,984 | +32% | 1 | 1 | 0% | 2,134 | 2,938 | +38% | 0 | 0 | — |
case-15 | pass→pass | 13,270 | 3,695 | -72% | 1 | 1 | 0% | 1,671 | 1,280 | -23% | 0 | 0 | — |
case-16 | pass→pass | 8,554 | 1,920 | -78% | 1 | 1 | 0% | 574 | 1,084 | +89% | 0 | 0 | — |
case-17 | fail→pass | 18,221 | 9,120 | -50% | 1 | 1 | 0% | 2,027 | 1,187 | -41% | 0 | 0 | — |
case-18 | fail→pass | 13,098 | 8,127 | -38% | 1 | 1 | 0% | 1,341 | 1,303 | -3% | 0 | 0 | — |
case-19 | fail→fail | 16,346 | 17,572 | +8% | 1 | 1 | 0% | 2,385 | 2,428 | +2% | 0 | 0 | — |
case-21 | pass→fail | 28,929 | 33,193 | +15% | 1 | 1 | 0% | 3,734 | 5,255 | +41% | 0 | 0 | — |
case-22 | pass→pass | 27,018 | 41,793 | +55% | 1 | 1 | 0% | 3,333 | 6,105 | +83% | 0 | 0 | — |
case-23 | fail→pass | 23,510 | 24,312 | +3% | 1 | 1 | 0% | 2,879 | 4,150 | +44% | 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. 23 cases were attempted. The headline lift of +43 percentage points is the difference between those two pass rates over the 23 comparable cases. 1 case got worse with the skill loaded, and it is included in that figure.
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.