Anti-DDoS Firm Weaponized: When the Shield Becomes the Sword

Solid points. As an MSP, we've started treating DDoS scrubbers like any other critical vendor. We require a copy of their latest SOC 2 Type II report before onboarding. It's not foolproof, but it forces them to have some documented controls over internal access. If they can't prove they segment their management plane from their scrubbing nodes, we walk away.

The CEO blaming a 'competitor' is a classic PR move, but technically plausible. We saw similar tactics in the gaming industry where booter services would DDoS each other to steal customers. What's scary here is the scale. If a specialized anti-DDoS firm can't keep their own control plane secure, it casts doubt on the whole industry's maturity.

I'd add monitoring for 'carpet bombing' patterns in your NetFlow data. Sometimes when a mitigation center is hijacked, they don't just attack one target; they spray traffic everywhere. We use this Python snippet in our SOC to visualize potential carpet bombing anomalies:

import pandas as pd
# Assuming 'df' is a dataframe of flow logs
distinct_targets = df.groupby('SrcIP')['DstIP'].nunique()
print(distinct_targets[distinct_targets > 1000])


If you see a single source hitting thousands of distinct destinations in a minute, your 'protector' might be the attacker.

To expand on the monitoring aspect, don't just look for the attack; look for the source. If your scrubber is hijacked, you'll see 'clean' IPs initiating massive outbound connections. We cross-reference our BGP feed with internal logs to catch this. Here’s a KQL query to isolate high-volume traffic originating from your provider's supposed 'clean' IP range:

CommonSecurityLog
| where SourceIP in ("")
| summarize TotalBytes = sum(ReceivedBytes) by DestinationIP
| where TotalBytes > 5000000

It’s a nightmare scenario when your line of defense becomes the attack vector. Alongside internal monitoring, I suggest periodically auditing your provider's IP reputation against external blocklists. If they're being used to hammer others, their "clean" IPs might end up blacklisted, impacting your deliverability.

Here is a quick snippet to check a list of IPs against an abuse feed:

import requests

def check_reputation(ip_list):
    for ip in ip_list:
        # Placeholder URL for your preferred abuse checker API
        response = requests.get(f'https://api.abuseipdb.com/api/v2/check?ipAddress={ip}')
        print(f"{ip}: {response.()['data']['abuseConfidenceScore']}%")

Excellent points on NetFlow and reputation audits. We’ve also started strictly validating IRR (Internet Routing Registry) records for our scrubbing providers. If a mitigation center is compromised, route hijacking is a real risk.

You can automate a verification check using the RIPE Stat API to ensure traffic originates from the expected ASN. Here is a quick Python snippet to validate an IP's origin:

import requests

def verify_origin(ip, expected_asn):
    r = requests.get(f'https://stat.ripe.net/data/prefix-overview/data.?resource={ip}')
    data = r.()
    return str(expected_asn) in data.get('asns', [])

This helps ensure the traffic is actually coming from the verified provider infrastructure.

I’d emphasize testing your failover mechanisms. We treat anti-DDoS providers like any other single point of failure—by ensuring we can instantly bypass them. We run scheduled drills to verify we can withdraw BGP announcements or switch to a secondary scrubber within minutes.

If you are using Cisco gear for this failover, you might automate the route withdrawal:

configure terminal
router bgp 65001
neighbor 203.0.113.5 shutdown