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Get Started Free →Adversaries may exploit software vulnerabilities that can cause an application or system to crash and deny availability to users.
.claude/skills/cyberstrikeus-t1499-004-application-or-system-exploitation/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-01 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-04 | ✗→✓ | ▲ Improved | -49% | 0% |
| case-05 | ✗→✓ | ▲ Improved | -64% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 15% | 0% |
| case-10 | ✗→✓ | ▲ Improved | -27% | 0% |
> Sub-technique of: T1499
Adversaries may exploit software vulnerabilities that can cause an application or system to crash and deny availability to users. Some systems may automatically restart critical applications and services when crashes occur, but they can likely be re-exploited to cause a persistent denial of service (DoS) condition.
Adversaries may exploit known or zero-day vulnerabilities to crash applications and/or systems, which may also lead to dependent applications and/or systems to be in a DoS condition. Crashed or restarted applications or systems may also have other effects such as Data Destruction, Firmware Corruption, Service Stop etc. which may further cause a DoS condition and deny availability to critical information, applications and/or systems.
Platforms: Windows, IaaS, Linux, macOS
> Note: No Atomic Red Team tests available for this technique. See Atomic Red Team GitHub for updates.
Leverage services provided by Content Delivery Networks (CDN) or providers specializing in DoS mitigations to filter traffic upstream from services. Filter boundary traffic by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport.
| Finding | Severity | Impact | | ------------------------------------------------------- | -------- | ------ | | Application or System Exploitation technique applicable | High | Impact |
| CWE ID | Title | | ------- | --------------------------------- | | CWE-400 | Uncontrolled Resource Consumption |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-25 | pass→pass | 20,851 | 15,989 | -23% | 1 | 1 | 0% | 2,763 | 3,578 | +29% | 0 | 0 | — |
case-01 | fail→pass | 40,014 | 46,833 | +17% | 1 | 1 | 0% | 5,462 | 6,766 | +24% | 0 | 0 | — |
case-02 | pass→pass | 11,495 | 10,028 | -13% | 1 | 1 | 0% | 1,232 | 1,213 | -2% | 0 | 0 | — |
case-03 | pass→pass | 10,845 | 8,687 | -20% | 1 | 1 | 0% | 1,073 | 1,278 | +19% | 0 | 0 | — |
case-04 | fail→pass | 17,765 | 8,724 | -51% | 1 | 1 | 0% | 2,169 | 1,115 | -49% | 0 | 0 | — |
case-05 | fail→pass | 27,732 | 8,496 | -69% | 1 | 1 | 0% | 3,601 | 1,279 | -64% | 0 | 0 | — |
case-06 | pass→pass | 32,080 | 3,002 | -91% | 1 | 1 | 0% | 5,163 | 1,228 | -76% | 0 | 0 | — |
case-07 | fail→fail | 13,752 | 22,020 | +60% | 1 | 1 | 0% | 2,073 | 3,309 | +60% | 0 | 0 | — |
case-08 | fail→pass | 10,172 | 6,465 | -36% | 1 | 1 | 0% | 1,537 | 1,768 | +15% | 0 | 0 | — |
case-09 | pass→pass | 17,212 | 9,073 | -47% | 1 | 1 | 0% | 1,954 | 1,356 | -31% | 0 | 0 | — |
case-10 | fail→pass | 16,473 | 8,610 | -48% | 1 | 1 | 0% | 1,556 | 1,129 | -27% | 0 | 0 | — |
case-11 | fail→pass | 26,615 | 3,398 | -87% | 1 | 1 | 0% | 3,934 | 1,141 | -71% | 0 | 0 | — |
case-12 | fail→pass | 16,813 | 8,362 | -50% | 1 | 1 | 0% | 2,641 | 1,144 | -57% | 0 | 0 | — |
case-13 | pass→pass | 16,859 | 8,223 | -51% | 1 | 1 | 0% | 1,771 | 1,193 | -33% | 0 | 0 | — |
case-14 | fail→pass | 8,194 | 4,023 | -51% | 1 | 1 | 0% | 1,224 | 1,374 | +12% | 0 | 0 | — |
case-15 | fail→pass | 10,629 | 2,933 | -72% | 1 | 1 | 0% | 864 | 1,230 | +42% | 0 | 0 | — |
case-16 | pass→pass | 4,836 | 8,151 | +69% | 1 | 1 | 0% | 905 | 1,304 | +44% | 0 | 0 | — |
case-17 | pass→pass | 12,123 | 8,444 | -30% | 1 | 1 | 0% | 1,338 | 1,402 | +5% | 0 | 0 | — |
case-18 | pass→pass | 17,420 | 20,522 | +18% | 1 | 1 | 0% | 2,498 | 3,065 | +23% | 0 | 0 | — |
case-19 | pass→pass | 23,414 | 3,712 | -84% | 1 | 1 | 0% | 2,843 | 1,350 | -53% | 0 | 0 | — |
case-20 | pass→pass | 7,628 | 8,289 | +9% | 1 | 1 | 0% | 385 | 1,083 | +181% | 0 | 0 | — |
case-21 | fail→pass | 12,095 | 8,890 | -26% | 1 | 1 | 0% | 988 | 1,388 | +40% | 0 | 0 | — |
case-22 | pass→pass | 8,081 | 8,141 | +1% | 1 | 1 | 0% | 424 | 1,294 | +205% | 0 | 0 | — |
case-23 | pass→pass | 31,075 | 19,308 | -38% | 1 | 1 | 0% | 4,969 | 4,311 | -13% | 0 | 0 | — |
case-24 | pass→pass | 21,674 | 22,228 | +3% | 1 | 1 | 0% | 2,972 | 3,924 | +32% | 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. 25 cases were attempted. The headline lift of +40 percentage points is the difference between those two pass rates over the 25 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.