A new comprehensive study shows how much hackers pay for Dark Web user data. To quote the researchers, “This is just the tip of the iceberg. There’s stuff we can’t even publish as it is so horrible”.
Hackers pay for Dark Web user data to implement Identity Theft, take over networks, plant malware and set up botnets. The Privacy Affairs report is an update of previous studies. It covers credit cards, payment processing services, social media, hacked services (like Uber), official documents, email databases, dormant malware installs, DDoS attacks, and Botnets, and so much more.
Here are a few examples from a very comprehensive list of Dark Web user data
– prices are USD, and where there is a range that depends on the likely success rate.
- Credit card with PIN – $25-35
- Online banking logins – $40-120
- Australian credit cards with CVV $30
- PayPal $5-180
- Crypto-coin accounts – $300-810
- Facebook – $65
- Gmail – $80
- LinkedIn – $12
- Uber – $8-14
- Netflix – $44 (plus $4 for 4K)
- Adobe Creative Cloud – $160
- Drivers licence (scan) – From $20 (NSW Australia) to $100
- Passport scan – from 100-$6500
- Selfie photo with ID – $100
- ID cards – from $125-185
- Malware infected per 1000 – $50 (low yield) to $5000 (high yield)
- DDoS Botnets 50,000 pings per second – From $15 per hour to $200 for 24 hours
The significant sources of data theft
- Public, unsecured Wi-Fi, especially at large shopping centres and food courts
- ATM Skimmers
- Data breaches
- Malware recording computer key stokes when accessing a finance site
- Brute force password attacks
Always use a VPN, Password manager and take precautions against ID Theft (GadgetGuy’s strong recommendations here.
Stolen data is gold. And that data is freely and cheaply available on the Dark Web. It is a smorgasbord of how to rip people off.
Of course, the data has already been massaged into your master dark web profile that the super organised crime cybercriminals use. This is the ‘dregs’ that can still yield pennies from heaven.
The reality is that ‘mug’ hackers rarely target specific people. With the sheer quantity of data available, they just need to play the numbers game – it only takes a tiny percentage to make a decent living if you are in a third-world country.