Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →Adversaries may target the different network services provided by systems to conduct a denial of service (DoS).
.claude/skills/cyberstrikeus-t1499-002-service-exhaustion-flood/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-11 | ✗→✓ | ▲ Improved | -36% | 0% |
| case-07 | ✗→✓ | ▲ Improved | -17% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 37% | 0% |
| case-17 | ✗→✓ | ▲ Improved | -31% | 0% |
| case-21 | ✗→✓ | ▲ Improved | -44% | 0% |
> Sub-technique of: T1499
Adversaries may target the different network services provided by systems to conduct a denial of service (DoS). Adversaries often target the availability of DNS and web services, however others have been targeted as well. Web server software can be attacked through a variety of means, some of which apply generally while others are specific to the software being used to provide the service.
One example of this type of attack is known as a simple HTTP flood, where an adversary sends a large number of HTTP requests to a web server to overwhelm it and/or an application that runs on top of it. This flood relies on raw volume to accomplish the objective, exhausting any of the various resources required by the victim software to provide the service.
Another variation, known as a SSL renegotiation attack, takes advantage of a protocol feature in SSL/TLS. The SSL/TLS protocol suite includes mechanisms for the client and server to agree on an encryption algorithm to use for subsequent secure connections. If SSL renegotiation is enabled, a request can be made for renegotiation of the crypto algorithm. In a renegotiation attack, the adversary establishes a SSL/TLS connection and then proceeds to make a series of renegotiation requests. Because the cryptographic renegotiation has a meaningful cost in computation cycles, this can cause an impact to the availability of the service when done in volume.
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 | | --------------------------------------------- | -------- | ------ | | Service Exhaustion Flood technique applicable | Low | 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-02 | fail→fail | 28,048 | 35,137 | +25% | 1 | 1 | 0% | 3,734 | 5,858 | +57% | 0 | 0 | — |
case-01 | fail→fail | 31,345 | 36,189 | +15% | 1 | 1 | 0% | 3,697 | 5,961 | +61% | 0 | 0 | — |
case-03 | fail→fail | 28,750 | 30,162 | +5% | 1 | 1 | 0% | 4,098 | 5,414 | +32% | 0 | 0 | — |
case-04 | pass→pass | 8,557 | 8,500 | -1% | 1 | 1 | 0% | 596 | 1,538 | +158% | 0 | 0 | — |
case-05 | pass→pass | 30,349 | 3,925 | -87% | 1 | 1 | 0% | 1,806 | 1,567 | -13% | 0 | 0 | — |
case-06 | pass→pass | 17,842 | 14,547 | -18% | 1 | 1 | 0% | 1,991 | 2,585 | +30% | 0 | 0 | — |
case-11 | fail→pass | 20,740 | 3,201 | -85% | 1 | 1 | 0% | 2,197 | 1,410 | -36% | 0 | 0 | — |
case-07 | fail→pass | 20,219 | 11,253 | -44% | 1 | 1 | 0% | 2,509 | 2,075 | -17% | 0 | 0 | — |
case-08 | fail→pass | 11,000 | 1,878 | -83% | 1 | 1 | 0% | 967 | 1,328 | +37% | 0 | 0 | — |
case-09 | pass→pass | 21,177 | 20,949 | -1% | 1 | 1 | 0% | 2,222 | 3,613 | +63% | 0 | 0 | — |
case-10 | pass→pass | 21,221 | 22,918 | +8% | 1 | 1 | 0% | 2,340 | 3,417 | +46% | 0 | 0 | — |
case-12 | pass→fail | 15,863 | 16,203 | +2% | 1 | 1 | 0% | 1,669 | 2,494 | +49% | 0 | 0 | — |
case-13 | pass→pass | 20,056 | 23,710 | +18% | 1 | 1 | 0% | 3,002 | 3,663 | +22% | 0 | 0 | — |
case-14 | pass→pass | 20,640 | 20,685 | +0% | 1 | 1 | 0% | 2,325 | 3,307 | +42% | 0 | 0 | — |
case-15 | pass→pass | 13,408 | 9,158 | -32% | 1 | 1 | 0% | 2,013 | 1,558 | -23% | 0 | 0 | — |
case-16 | pass→pass | 10,654 | 1,739 | -84% | 1 | 1 | 0% | 1,043 | 1,289 | +24% | 0 | 0 | — |
case-17 | fail→pass | 19,870 | 8,272 | -58% | 1 | 1 | 0% | 2,285 | 1,566 | -31% | 0 | 0 | — |
case-18 | pass→pass | 9,926 | 8,433 | -15% | 1 | 1 | 0% | 1,238 | 1,661 | +34% | 0 | 0 | — |
case-19 | pass→pass | 20,253 | 26,217 | +29% | 1 | 1 | 0% | 2,969 | 4,309 | +45% | 0 | 0 | — |
case-20 | pass→pass | 34,681 | 22,769 | -34% | 1 | 1 | 0% | 2,851 | 4,356 | +53% | 0 | 0 | — |
case-21 | fail→pass | 32,231 | 7,336 | -77% | 1 | 1 | 0% | 2,498 | 1,397 | -44% | 0 | 0 | — |
case-22 | pass→pass | 24,509 | 25,590 | +4% | 1 | 1 | 0% | 3,065 | 3,877 | +26% | 0 | 0 | — |
case-23 | pass→pass | 21,183 | 21,596 | +2% | 1 | 1 | 0% | 2,371 | 3,855 | +63% | 0 | 0 | — |
case-24 | pass→pass | 24,992 | 14,060 | -44% | 1 | 1 | 0% | 2,467 | 2,228 | -10% | 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. 24 cases were attempted. The headline lift of +17 percentage points is the difference between those two pass rates over the 24 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.