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Get Started Free →Adversaries may dynamically establish connections to command and control infrastructure to evade common detections and remediations.
.claude/skills/cyberstrikeus-t1568-dynamic-resolution/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-10 | ✗→✓ | ▲ Improved | -69% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-12 | ✗→✓ | ▲ Improved | 47% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -35% | 0% |
| case-19 | ✗→✓ | ▲ Improved | -64% | 0% |
Adversaries may dynamically establish connections to command and control infrastructure to evade common detections and remediations. This may be achieved by using malware that shares a common algorithm with the infrastructure the adversary uses to receive the malware's communications. These calculations can be used to dynamically adjust parameters such as the domain name, IP address, or port number the malware uses for command and control.
Adversaries may use dynamic resolution for the purpose of Fallback Channels. When contact is lost with the primary command and control server malware may employ dynamic resolution as a means to reestablishing command and control.
Platforms: Linux, macOS, Windows, ESXi
> Note: No Atomic Red Team tests available for this technique. See Atomic Red Team GitHub for updates.
Network intrusion detection and prevention systems that use network signatures to identify traffic for specific adversary malware can be used to mitigate activity at the network level. Malware researchers can reverse engineer malware variants that use dynamic resolution and determine future C2 infrastructure that the malware will attempt to contact, but this is a time and resource intensive effort.
In some cases a local DNS sinkhole may be used to help prevent behaviors associated with dynamic resolution.
| Finding | Severity | Impact | | --------------------------------------- | -------- | ------------------- | | Dynamic Resolution technique applicable | Low | Command And Control |
| CWE ID | Title | | ------- | ---------------------------------- | | CWE-300 | Channel Accessible by Non-Endpoint |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-01 | fail→fail | 33,893 | 41,015 | +21% | 1 | 1 | 0% | 5,422 | 6,657 | +23% | 0 | 0 | — |
case-02 | fail→fail | 28,544 | 50,750 | +78% | 1 | 1 | 0% | 3,969 | 8,076 | +103% | 0 | 0 | — |
case-03 | fail→fail | 31,966 | 50,554 | +58% | 1 | 1 | 0% | 4,496 | 7,544 | +68% | 0 | 0 | — |
case-04 | pass→pass | 32,892 | 27,185 | -17% | 1 | 1 | 0% | 3,969 | 4,582 | +15% | 0 | 0 | — |
case-05 | pass→pass | 27,578 | 29,979 | +9% | 1 | 1 | 0% | 5,218 | 5,278 | +1% | 0 | 0 | — |
case-06 | pass→pass | 23,968 | 32,462 | +35% | 1 | 1 | 0% | 3,278 | 5,718 | +74% | 0 | 0 | — |
case-07 | fail→fail | 24,826 | 2,362 | -90% | 1 | 1 | 0% | 3,457 | 1,342 | -61% | 0 | 0 | — |
case-08 | pass→pass | 8,083 | 7,410 | -8% | 1 | 1 | 0% | 1,679 | 1,191 | -29% | 0 | 0 | — |
case-09 | pass→pass | 15,705 | 6,961 | -56% | 1 | 1 | 0% | 2,165 | 1,141 | -47% | 0 | 0 | — |
case-10 | fail→pass | 17,123 | 7,057 | -59% | 1 | 1 | 0% | 3,537 | 1,111 | -69% | 0 | 0 | — |
case-11 | fail→pass | 15,915 | 7,272 | -54% | 1 | 1 | 0% | 1,767 | 1,212 | -31% | 0 | 0 | — |
case-12 | fail→pass | 14,808 | 9,199 | -38% | 1 | 1 | 0% | 1,655 | 2,434 | +47% | 0 | 0 | — |
case-13 | pass→pass | 14,837 | 16,469 | +11% | 1 | 1 | 0% | 2,341 | 2,612 | +12% | 0 | 0 | — |
case-14 | pass→pass | 20,011 | 8,805 | -56% | 1 | 1 | 0% | 2,363 | 1,131 | -52% | 0 | 0 | — |
case-15 | pass→pass | 31,339 | 17,794 | -43% | 1 | 1 | 0% | 2,444 | 2,727 | +12% | 0 | 0 | — |
case-16 | pass→pass | 11,012 | 7,053 | -36% | 1 | 1 | 0% | 873 | 1,088 | +25% | 0 | 0 | — |
case-17 | fail→pass | 15,714 | 6,668 | -58% | 1 | 1 | 0% | 1,685 | 1,099 | -35% | 0 | 0 | — |
case-18 | pass→fail | 16,825 | 10,199 | -39% | 1 | 1 | 0% | 1,623 | 1,736 | +7% | 0 | 0 | — |
case-19 | fail→pass | 30,037 | 9,247 | -69% | 1 | 1 | 0% | 4,036 | 1,433 | -64% | 0 | 0 | — |
case-20 | fail→pass | 15,405 | 7,653 | -50% | 1 | 1 | 0% | 1,765 | 1,325 | -25% | 0 | 0 | — |
case-21 | fail→pass | 22,745 | 7,978 | -65% | 1 | 1 | 0% | 3,427 | 1,311 | -62% | 0 | 0 | — |
case-22 | pass→pass | 20,606 | 2,260 | -89% | 1 | 1 | 0% | 2,850 | 1,274 | -55% | 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 +27 percentage points is the difference between those two pass rates over the 22 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.