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Get Started Free →Adversaries may perform Network Denial of Service (DoS) attacks to degrade or block the availability of targeted resources to users.
.claude/skills/cyberstrikeus-t1498-network-denial-of-service/SKILL.md| Test case | Without → With | Effect | Δ tokens | Δ turns |
|---|---|---|---|---|
| case-02 | ✗→✓ | ▲ Improved | 13% | 0% |
| case-08 | ✗→✓ | ▲ Improved | 41% | 0% |
| case-15 | ✗→✓ | ▲ Improved | -14% | 0% |
| case-18 | ✗→✓ | ▲ Improved | 54% | 0% |
| case-19 | ✗→✓ | ▲ Improved | 9% | 0% |
Adversaries may perform Network Denial of Service (DoS) attacks to degrade or block the availability of targeted resources to users. Network DoS can be performed by exhausting the network bandwidth services rely on. Example resources include specific websites, email services, DNS, and web-based applications. Adversaries have been observed conducting network DoS attacks for political purposes and to support other malicious activities, including distraction, hacktivism, and extortion.
A Network DoS will occur when the bandwidth capacity of the network connection to a system is exhausted due to the volume of malicious traffic directed at the resource or the network connections and network devices the resource relies on. For example, an adversary may send 10Gbps of traffic to a server that is hosted by a network with a 1Gbps connection to the internet. This traffic can be generated by a single system or multiple systems spread across the internet, which is commonly referred to as a distributed DoS (DDoS).
To perform Network DoS attacks several aspects apply to multiple methods, including IP address spoofing, and botnets.
Adversaries may use the original IP address of an attacking system, or spoof the source IP address to make the attack traffic more difficult to trace back to the attacking system or to enable reflection. This can increase the difficulty defenders have in defending against the attack by reducing or eliminating the effectiveness of filtering by the source address on network defense devices.
For DoS attacks targeting the hosting system directly, see Endpoint Denial of Service.
Platforms: Windows, IaaS, Linux, macOS, Containers
> Note: No Atomic Red Team tests available for this technique. See Atomic Red Team GitHub for updates.
When flood volumes exceed the capacity of the network connection being targeted, it is typically necessary to intercept the incoming traffic upstream to filter out the attack traffic from the legitimate traffic. Such defenses can be provided by the hosting Internet Service Provider (ISP) or by a 3rd party such as a Content Delivery Network (CDN) or providers specializing in DoS mitigations.
Depending on flood volume, on-premises filtering may be possible by blocking source addresses sourcing the attack, blocking ports that are being targeted, or blocking protocols being used for transport.
As immediate response may require rapid engagement of 3rd parties, analyze the risk associated to critical resources being affected by Network DoS attacks and create a disaster recovery plan/business continuity plan to respond to incidents.
| Finding | Severity | Impact | | ---------------------------------------------- | -------- | ------ | | Network Denial of Service 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→pass | 33,547 | 34,433 | +3% | 1 | 1 | 0% | 5,341 | 6,016 | +13% | 0 | 0 | — |
case-01 | fail→fail | 23,925 | 20,958 | -12% | 1 | 1 | 0% | 2,643 | 3,605 | +36% | 0 | 0 | — |
case-03 | pass→pass | 20,138 | 21,759 | +8% | 1 | 1 | 0% | 2,160 | 3,438 | +59% | 0 | 0 | — |
case-04 | pass→pass | 17,521 | 18,932 | +8% | 1 | 1 | 0% | 1,894 | 3,094 | +63% | 0 | 0 | — |
case-05 | pass→pass | 23,686 | 19,241 | -19% | 1 | 1 | 0% | 2,837 | 3,516 | +24% | 0 | 0 | — |
case-06 | pass→pass | 10,426 | 8,898 | -15% | 1 | 1 | 0% | 858 | 1,701 | +98% | 0 | 0 | — |
case-07 | pass→pass | 9,784 | 1,646 | -83% | 1 | 1 | 0% | 793 | 1,474 | +86% | 0 | 0 | — |
case-08 | fail→pass | 12,696 | 7,447 | -41% | 1 | 1 | 0% | 1,133 | 1,598 | +41% | 0 | 0 | — |
case-09 | pass→pass | 12,352 | 6,858 | -44% | 1 | 1 | 0% | 1,178 | 1,400 | +19% | 0 | 0 | — |
case-10 | pass→pass | 16,558 | 9,763 | -41% | 1 | 1 | 0% | 1,524 | 1,946 | +28% | 0 | 0 | — |
case-11 | pass→pass | 13,373 | 8,029 | -40% | 1 | 1 | 0% | 1,238 | 2,500 | +102% | 0 | 0 | — |
case-17 | pass→pass | 25,986 | 11,548 | -56% | 1 | 1 | 0% | 2,336 | 2,270 | -3% | 0 | 0 | — |
case-12 | pass→pass | 14,504 | 2,912 | -80% | 1 | 1 | 0% | 1,430 | 1,640 | +15% | 0 | 0 | — |
case-13 | pass→pass | 14,469 | 12,054 | -17% | 1 | 1 | 0% | 1,976 | 2,249 | +14% | 0 | 0 | — |
case-14 | pass→pass | 14,374 | 7,060 | -51% | 1 | 1 | 0% | 2,357 | 1,479 | -37% | 0 | 0 | — |
case-15 | fail→pass | 14,973 | 2,123 | -86% | 1 | 1 | 0% | 1,769 | 1,515 | -14% | 0 | 0 | — |
case-16 | pass→pass | 13,259 | 7,473 | -44% | 1 | 1 | 0% | 1,229 | 1,570 | +28% | 0 | 0 | — |
case-18 | fail→pass | 5,676 | 6,981 | +23% | 1 | 1 | 0% | 946 | 1,459 | +54% | 0 | 0 | — |
case-19 | fail→pass | 15,291 | 6,977 | -54% | 1 | 1 | 0% | 1,404 | 1,527 | +9% | 0 | 0 | — |
case-20 | pass→pass | 11,623 | 8,699 | -25% | 1 | 1 | 0% | 1,914 | 1,714 | -10% | 0 | 0 | — |
case-21 | pass→pass | 21,014 | 2,481 | -88% | 1 | 1 | 0% | 2,373 | 1,538 | -35% | 0 | 0 | — |
case-22 | fail→pass | 7,398 | 9,319 | +26% | 1 | 1 | 0% | 1,095 | 1,481 | +35% | 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.
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.