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Get Started Free →Adversaries may search within public scan databases for information about victims that can be used during targeting.
.claude/skills/cyberstrikeus-t1596-005-scan-databases/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-09 | ✗→✓ | ▲ Improved | 24% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 19% | 0% |
| case-21 | ✗→✓ | ▲ Improved | -4% | 0% |
| case-23 | ✗→✓ | ▲ Improved | -32% | 0% |
| case-06 | ✓→✗ | ▼ Worse | 65% | 0% |
> Sub-technique of: T1596
Adversaries may search within public scan databases for information about victims that can be used during targeting. Various online services continuously publish the results of Internet scans/surveys, often harvesting information such as active IP addresses, hostnames, open ports, certificates, and even server banners.
Adversaries may search scan databases to gather actionable information. Threat actors can use online resources and lookup tools to harvest information from these services. Adversaries may seek information about their already identified targets, or use these datasets to discover opportunities for successful breaches. Information from these sources may reveal opportunities for other forms of reconnaissance (ex: Active Scanning or Search Open Websites/Domains), establishing operational resources (ex: Develop Capabilities or Obtain Capabilities), and/or initial access (ex: External Remote Services or Exploit Public-Facing Application).
Platforms: PRE
> Note: No Atomic Red Team tests available for this technique. See Atomic Red Team GitHub for updates.
This technique cannot be easily mitigated with preventive controls since it is based on behaviors performed outside of the scope of enterprise defenses and controls. Efforts should focus on minimizing the amount and sensitivity of data available to external parties.
| Finding | Severity | Impact | | ----------------------------------- | -------- | -------------- | | Scan Databases technique applicable | High | Reconnaissance |
| CWE ID | Title | | ------- | --------------------------------- | | CWE-200 | Exposure of Sensitive Information |
| Case | Status | Duration (ms) | Turns | Tokens | Tool calls | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Without | With | Δ | Without | With | Δ | Without | With | Δ | Without | With | Δ | ||
case-04 | pass→pass | 17,385 | 26,180 | +51% | 1 | 1 | 0% | 1,899 | 4,090 | +115% | 0 | 0 | — |
case-01 | fail→fail | 27,867 | 29,423 | +6% | 1 | 1 | 0% | 3,888 | 4,782 | +23% | 0 | 0 | — |
case-02 | fail→fail | 31,724 | 39,086 | +23% | 1 | 1 | 0% | 3,444 | 5,326 | +55% | 0 | 0 | — |
case-03 | pass→pass | 35,638 | 27,168 | -24% | 1 | 1 | 0% | 4,762 | 3,626 | -24% | 0 | 0 | — |
case-05 | fail→fail | 23,515 | 17,322 | -26% | 1 | 1 | 0% | 2,439 | 2,886 | +18% | 0 | 0 | — |
case-06 | pass→fail | 22,257 | 29,823 | +34% | 1 | 1 | 0% | 2,350 | 3,885 | +65% | 0 | 0 | — |
case-07 | fail→fail | 24,828 | 22,794 | -8% | 1 | 1 | 0% | 3,136 | 4,288 | +37% | 0 | 0 | — |
case-08 | pass→pass | 10,977 | 2,466 | -78% | 1 | 1 | 0% | 1,012 | 1,056 | +4% | 0 | 0 | — |
case-09 | fail→pass | 18,656 | 23,039 | +23% | 1 | 1 | 0% | 2,368 | 2,937 | +24% | 0 | 0 | — |
case-10 | fail→fail | 20,085 | 25,041 | +25% | 1 | 1 | 0% | 2,575 | 2,321 | -10% | 0 | 0 | — |
case-11 | fail→fail | 27,968 | 23,298 | -17% | 1 | 1 | 0% | 3,858 | 4,012 | +4% | 0 | 0 | — |
case-12 | pass→pass | 19,377 | 15,760 | -19% | 1 | 1 | 0% | 2,270 | 2,347 | +3% | 0 | 0 | — |
case-13 | pass→pass | 4,761 | 9,661 | +103% | 1 | 1 | 0% | 679 | 1,017 | +50% | 0 | 0 | — |
case-14 | pass→pass | 21,283 | 10,244 | -52% | 1 | 1 | 0% | 2,579 | 2,315 | -10% | 0 | 0 | — |
case-15 | pass→pass | 24,699 | 9,192 | -63% | 1 | 1 | 0% | 3,297 | 2,008 | -39% | 0 | 0 | — |
case-16 | pass→pass | 17,960 | 29,522 | +64% | 1 | 1 | 0% | 2,971 | 3,993 | +34% | 0 | 0 | — |
case-17 | pass→pass | 9,016 | 7,803 | -13% | 1 | 1 | 0% | 634 | 1,019 | +61% | 0 | 0 | — |
case-18 | pass→fail | 29,299 | 34,261 | +17% | 1 | 1 | 0% | 3,886 | 4,698 | +21% | 0 | 0 | — |
case-19 | fail→pass | 25,877 | 21,479 | -17% | 1 | 1 | 0% | 2,797 | 3,328 | +19% | 0 | 0 | — |
case-20 | pass→pass | 26,030 | 26,513 | +2% | 1 | 1 | 0% | 3,254 | 3,549 | +9% | 0 | 0 | — |
case-21 | fail→pass | 19,161 | 11,640 | -39% | 1 | 1 | 0% | 1,782 | 1,716 | -4% | 0 | 0 | — |
case-22 | pass→pass | 25,607 | 19,145 | -25% | 1 | 1 | 0% | 2,519 | 2,843 | +13% | 0 | 0 | — |
case-23 | fail→pass | 16,382 | 5,903 | -64% | 1 | 1 | 0% | 2,194 | 1,483 | -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. 23 cases were attempted. The headline lift of +9 percentage points is the difference between those two pass rates over the 23 comparable cases. 2 cases got worse with the skill loaded, and they are 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.