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Why AWS keeps appearing on abuse lists
Large public clouds offer millions of virtual machines, automated APIs and inexpensive short-lived instances. Attackers can obtain computing capacity quickly, rotate addresses and abandon infrastructure before a provider or an outside list updates its records. WAYSCloud identifies low-cost virtual machines, API-driven provisioning and initially clean IP reputation as reasons cloud infrastructure attracts abuse.
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Scale then amplifies what observers see. A provider operating a very large address space and serving more customers has more opportunities for a compromised account, a fraudulent payment, a vulnerable application or a deliberately malicious tenant. A raw total answers “how many bad addresses were observed?” It does not answer “what share of this provider’s available addresses were bad?”
That distinction is why an AWS appearance on a list should be treated as an observation of abuse exposure, not as proof that AWS customers are inherently worse. To judge provider quality, you need both the numerator (reports, malicious IPs or active C&Cs) and a comparable denominator.
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What the Spamhaus 2024 baseline actually shows
Spamhaus’s active botnet C&C table for January through June 2024 lists 13 networks. The figures below are counts of active C&C servers observed during that six-month period, not a rate per customer, IP block or address.
| Provider or network | Active botnet C&Cs | What the number means |
|---|---|---|
| Tencent | 253 | Observed active C&C servers, Jan–Jun 2024 |
| Alibaba | 232 | Observed active C&C servers, Jan–Jun 2024 |
| DigitalOcean | 90 | Observed active C&C servers, Jan–Jun 2024 |
| Hetzner | 53 | Observed active C&C servers, Jan–Jun 2024 |
| 46 | Observed active C&C servers, Jan–Jun 2024 | |
| OVH | 43 | Observed active C&C servers, Jan–Jun 2024 |
| Colocrossing | 42 | Observed active C&C servers, Jan–Jun 2024 |
| Contabo | 39 | Observed active C&C servers, Jan–Jun 2024 |
| Huawei | 38 | Observed active C&C servers, Jan–Jun 2024 |
| Amazon | 34 | Observed active C&C servers, Jan–Jun 2024 |
| Constant | 23 | Observed active C&C servers, Jan–Jun 2024 |
| Microsoft | 23 | Observed active C&C servers, Jan–Jun 2024 |
| ITLDC | 22 | Observed active C&C servers, Jan–Jun 2024 |
Spamhaus says providers in this ranking either have an abuse problem, do not take appropriate action on reports, or fail to notify Spamhaus after remediation. That wording describes why a network is included; it is not a statement that every customer is malicious or that the list is a normalized quality league table.
The direction of change also matters. Amazon’s active-C&C count was down 42% compared with the previous six-month comparison. OVH fell 36% and Microsoft 26%. A provider can still appear in a high-profile list while materially improving its response or reducing observed infrastructure.
Volume and density answer different questions
Raw volume
Volume is a count: malicious IPs, reports, domains, spammed hosts or active C&Cs observed in a defined window. It is easy to communicate and useful for estimating the absolute amount of abuse an ecosystem may expose to the internet. It is also heavily influenced by provider size, customer mix, address allocation and how aggressively a feed discovers infrastructure.
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Abuse density
Density normalizes the count by a denominator. The Cybercrime Information Center describes a spam score that divides reported spammed-content or spambot hosts by an autonomous system’s routable IPv4 addresses. In generic form:
abuse density = observed abusive assets ÷ routable IPv4 addresses
Multiplying the result by 100 or by a fixed number such as 10,000 can make it easier to read, but the multiplier must be identical for every provider. A density value is meaningful only when the numerator and denominator cover the same observation window, the same address families and the same provider boundaries.
IPv4-only denominators are not a universal measure of cloud capacity. They are, however, the disclosed basis for the cited methodology. If a provider has substantial IPv6 use, omitting IPv6 can make its apparent rate look higher or lower depending on where abuse is observed. Any published ranking must state that limitation instead of implying that the result covers all infrastructure.
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The current malicious-IP snapshot is a volume list, not a density ranking
WAYSCloud’s current table, accessed in 2026, reports these raw malicious-IP counts:
| Provider and ASN | Malicious IPs | Interpretation |
|---|---|---|
| Amazon, AS16509 | 724,893 | Largest raw count in this snapshot; no denominator supplied here |
| DigitalOcean | 643,188 | Second-largest raw count in this snapshot; no denominator supplied here |
| Google, AS396982 | 185,203 | Raw count; no denominator supplied here |
| Microsoft, AS8075 | 141,692 | Raw count; no denominator supplied here |
| Oracle, AS31898 | 59,927 | Raw count; no denominator supplied here |
These values can answer “which listed provider had the most malicious IPs in that snapshot?”—Amazon, with 724,893. They cannot establish which provider had the highest abuse rate. To turn them into rates, you would need the routable IPv4 address count for each ASN, measured for the same date or interval, plus the table’s feed composition, ASN attribution and deduplication rules.
How to build a defensible 13-provider density ranking
A reproducible ranking is possible, but only after joining equivalent datasets. Use this workflow rather than sorting raw totals.
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- Fix the observation window. Use identical start and end dates for all 13 networks. Date-stamp live dashboards because Spamhaus notes that daily figures are indicators and fluctuate.
- Document the feeds. List every blocklist, sensor or report stream, its geographic coverage, confidence threshold and update cadence. A provider monitored by more feeds can appear worse simply because it is observed more intensively.
- Map ASNs consistently. Decide whether a provider is represented by one ASN, a published set of ASNs or a legal organization. Keep hosting subsidiaries and regional networks in or out by rule, not case by case. Record the mapping version so another analyst can reproduce it.
- Obtain the denominator. Count routable IPv4 addresses assigned to each included ASN during the same period. State whether announced, allocated or actually routed space is used; those are not interchangeable.
- Deduplicate addresses. Collapse repeated sightings according to a published rule. If an IP changes hands during the window, define whether it is counted once, by time in service or by provider ownership at detection.
- Calculate and rank. Apply the same formula to every provider, preserve the unrounded value for audit, then sort by density. Publish the raw numerator beside the rate so readers can see the scale trade-off.
- Show uncertainty. Include the window, feed limits and any confidence intervals or sensitivity checks. A small denominator or sparse feed can make a rate swing sharply after only a few observations.
Without those steps, a supposed “density ranking” is only a volume ranking with a new label.
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Six axes that belong in a fair comparison
| Axis | Question to answer | Why it changes the verdict |
|---|---|---|
| Raw volume | How many unique malicious IPs or active C&Cs were observed? | Shows absolute exposure but favors large providers in the wrong direction. |
| Density per routable IPv4 | What share of the disclosed address space was observed as abusive? | Controls, partially, for network size. |
| Report-to-IP ratio | How many independent reports or incidents map to each address? | Separates one-off sightings from persistent, repeatedly reported infrastructure. |
| Response and remediation | How quickly does the provider investigate, suspend and notify the reporting party? | Two providers with the same rate can create very different ongoing harm. |
| ASN and geography coverage | Which regions, subsidiaries and address families are included? | Uneven scope can create artificial differences. |
| Measurement transparency | Are dates, feeds, thresholds and deduplication rules disclosed? | Readers can judge whether the result is reproducible. |
What provider response tells you that a count cannot
An abuse count is a lagging signal. A provider may have already disabled an account while a blocklist still shows the address, or a malicious tenant may evade detection until after the measurement window. Conversely, a high count with slow remediation can indicate an active operational problem rather than merely a large customer base.
Spamhaus recommends robust customer verification, payment verification, blocklists of abusive customers, strong acceptable-use policies, blocklist checks and traffic monitoring for abnormal patterns. These controls reduce fraudulent sign-ups, identify repeat offenders and shorten the time between a report and containment. When comparing providers, ask for evidence of those processes—published reporting channels, response targets, suspension practices and notification after remediation—rather than inferring behavior from a single list position.
How to read live dashboards without overclaiming
Spamhaus’s 2026 dashboard states that it analyzes 7.5 million IPs every 24 hours and protects 4.5 billion user mailboxes. Those scale figures describe the monitoring operation, not the number of abusive customers at any one provider. Daily indicators naturally move as addresses are reassigned, campaigns start or stop and feeds refresh.
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- Capture the definition of the indicator, not just its headline number.
- Compare like-for-like intervals; do not place a one-day count beside a six-month total.
- Keep historical snapshots so a temporary spike is not mistaken for a long-term trend.
- Report provider improvements, such as Amazon’s 42% six-month decline in active C&Cs, alongside the remaining count.
Direct answers to the common AWS questions
Why is AWS always on abuse lists?
Its enormous, automated infrastructure creates more opportunities for abuse to be observed in absolute terms. Visibility and customer scale raise the numerator; neither alone proves a higher per-address rate.
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Is AWS worse than smaller cloud providers?
The cited data do not support that blanket conclusion. Amazon ranked tenth in the 13-network Spamhaus active-C&C table with 34, while smaller networks appeared above and below it. The current WAYSCloud table puts Amazon first by raw malicious-IP count, but supplies no matching denominators.
Which provider has the most malicious IPs?
In WAYSCloud’s current snapshot accessed in 2026, Amazon (AS16509) has 724,893, ahead of DigitalOcean’s 643,188. That is a volume result, not a density or provider-quality result.
What is abuse density per IP block?
It is the number of observed abusive assets divided by a defined address-space denominator, such as routable IPv4 addresses. The denominator, dates and counting rules must be disclosed for the rate to be comparable.
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Not from the cited tables alone. A valid order requires the same-period routable-address dataset, consistent ASN scope, feed definitions and deduplication rules. Publishing an order without those inputs would disguise a volume comparison as a rate.
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