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Get Started Free →Adversaries may employ a known symmetric encryption algorithm to conceal command and control traffic rather than relying on any inherent protections provided by a communication protocol.
.claude/skills/cyberstrikeus-t1573-001-symmetric-cryptography/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-05 | ✗→✓ | ▲ Improved | 9% | 0% |
| case-07 | ✗→✓ | ▲ Improved | 109% | 0% |
| case-11 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-13 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-16 | ✗→✓ | ▲ Improved | -4% | 0% |
> Sub-technique of: T1573
Adversaries may employ a known symmetric encryption algorithm to conceal command and control traffic rather than relying on any inherent protections provided by a communication protocol. Symmetric encryption algorithms use the same key for plaintext encryption and ciphertext decryption. Common symmetric encryption algorithms include AES, DES, 3DES, Blowfish, and RC4.
Platforms: ESXi, Linux, macOS, Network Devices, Windows
> 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.
| Finding | Severity | Impact | | ------------------------------------------- | -------- | ------------------- | | Symmetric Cryptography 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-09 | pass→pass | 20,566 | 8,609 | -58% | 1 | 1 | 0% | 2,254 | 1,912 | -15% | 0 | 0 | — |
case-14 | pass→pass | 32,465 | 30,241 | -7% | 1 | 1 | 0% | 3,721 | 4,324 | +16% | 0 | 0 | — |
case-04 | fail→fail | 15,968 | 20,465 | +28% | 1 | 1 | 0% | 2,423 | 3,095 | +28% | 0 | 0 | — |
case-01 | fail→fail | 34,869 | 35,636 | +2% | 1 | 1 | 0% | 4,696 | 5,665 | +21% | 0 | 0 | — |
case-02 | fail→fail | 30,169 | 31,173 | +3% | 1 | 1 | 0% | 3,583 | 5,005 | +40% | 0 | 0 | — |
case-03 | fail→fail | 26,400 | 37,283 | +41% | 1 | 1 | 0% | 3,277 | 5,678 | +73% | 0 | 0 | — |
case-05 | fail→pass | 14,581 | 21,435 | +47% | 1 | 1 | 0% | 2,703 | 2,933 | +9% | 0 | 0 | — |
case-06 | pass→pass | 18,459 | 18,457 | -0% | 1 | 1 | 0% | 3,055 | 2,809 | -8% | 0 | 0 | — |
case-07 | fail→pass | 15,236 | 21,906 | +44% | 1 | 1 | 0% | 1,958 | 4,093 | +109% | 0 | 0 | — |
case-08 | pass→pass | 21,478 | 14,395 | -33% | 1 | 1 | 0% | 2,545 | 1,878 | -26% | 0 | 0 | — |
case-10 | fail→fail | 14,109 | 29,819 | +111% | 1 | 1 | 0% | 1,795 | 2,664 | +48% | 0 | 0 | — |
case-11 | fail→pass | 16,814 | 8,331 | -50% | 1 | 1 | 0% | 1,852 | 1,190 | -36% | 0 | 0 | — |
case-12 | pass→pass | 20,445 | 23,248 | +14% | 1 | 1 | 0% | 3,069 | 3,887 | +27% | 0 | 0 | — |
case-13 | fail→pass | 24,464 | 16,244 | -34% | 1 | 1 | 0% | 3,381 | 2,169 | -36% | 0 | 0 | — |
case-15 | pass→pass | 27,323 | 22,480 | -18% | 1 | 1 | 0% | 3,911 | 3,873 | -1% | 0 | 0 | — |
case-16 | fail→pass | 14,491 | 8,300 | -43% | 1 | 1 | 0% | 1,324 | 1,267 | -4% | 0 | 0 | — |
case-17 | fail→pass | 19,718 | 6,137 | -69% | 1 | 1 | 0% | 2,133 | 1,781 | -17% | 0 | 0 | — |
case-18 | fail→pass | 17,714 | 8,848 | -50% | 1 | 1 | 0% | 2,196 | 1,264 | -42% | 0 | 0 | — |
case-19 | pass→pass | 8,517 | 9,244 | +9% | 1 | 1 | 0% | 558 | 829 | +49% | 0 | 0 | — |
case-20 | pass→pass | 31,541 | 38,055 | +21% | 1 | 1 | 0% | 4,111 | 6,139 | +49% | 0 | 0 | — |
case-21 | pass→pass | 23,700 | 31,701 | +34% | 1 | 1 | 0% | 3,363 | 5,026 | +49% | 0 | 0 | — |
case-22 | pass→pass | 37,530 | 50,674 | +35% | 1 | 1 | 0% | 4,369 | 8,889 | +103% | 0 | 0 | — |
case-23 | pass→pass | 33,547 | 41,119 | +23% | 1 | 1 | 0% | 4,273 | 7,066 | +65% | 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 +30 percentage points is the difference between those two pass rates over the 23 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.