Unknown number of customers with reused passwords
PcComponentes, a major online technology retailer in Spain, has publicly refuted allegations of a system breach after a threat actor named 'daghetiaw' claimed to have stolen and was selling a database of 16.3 million customer records. The company's internal investigation found no evidence of unauthorized access to its core databases. Instead, PcComponentes identified the activity as a large-scale credential stuffing attack. This involves automated attempts to log into accounts using username and password combinations leaked from previous breaches at other companies. While customer passwords and financial data were not compromised from PcComponentes' systems, personal data from accounts with reused passwords was accessed. To mitigate the threat, the company has enforced mandatory two-factor authentication (2FA) for all accounts and logged out all users to force a secure re-authentication.
The incident was initiated by a threat actor, 'daghetiaw,' who posted on a hacking forum claiming to possess a database of 16.3 million PcComponentes customers. The actor offered a sample of 500,000 records to prove the claim, which included full names, tax IDs, addresses, phone numbers, and IP addresses.
PcComponentes' investigation determined this was not a direct breach of their infrastructure but an account takeover (ATO) campaign via credential stuffing. The attack likely unfolded as follows:
This attack highlights the pervasive risk of password reuse across different online services.
This incident is a classic example of a credential stuffing attack, a subset of brute-force attacks.
T1110.003 - Credential Stuffing. This relies on the high probability that users reuse passwords across multiple sites. Threat intelligence firm Hudson Rock suggested the credentials may have been sourced from info-stealer malware logs.Although not a direct breach of PcComponentes' core systems, the impact on affected customers is significant:
PcComponentes' response demonstrates best practices for handling a credential stuffing attack:
M1032 - Multi-factor Authentication.Organizations can detect such attacks by monitoring for high volumes of failed login attempts from disparate IP addresses and sudden spikes in successful logins for accounts that have been dormant. User behavior analysis (D3-UBA) can help identify anomalous login patterns.
Users and organizations can take several steps to mitigate the risk of credential stuffing:
For Users:
For Organizations:
M1032 - Multi-factor Authentication): Offer and encourage (or mandate) MFA for all user accounts.M1040 - Behavior Prevention on Endpoint): Implement tools to detect high-frequency login attempts, impossible travel scenarios, and other indicators of automated attacks.M1027 - Password Policies): Block the use of common or previously breached passwords by checking new passwords against a known-breached list.The most effective defense against credential stuffing, as it requires a second factor that the attacker does not possess.
Mapped D3FEND Techniques:
Enforce strong password requirements and check new passwords against a database of known-breached credentials to prevent reuse.
Mapped D3FEND Techniques:
Use analytics to detect and block anomalous login behavior, such as high rates of failures, impossible travel, and headless browser automation.
Mapped D3FEND Techniques:
Educate users on the importance of using unique passwords for every service and the benefits of using a password manager.

Cybersecurity professional with over 10 years of specialized experience in security operations, threat intelligence, incident response, and security automation. Expertise spans SOAR/XSOAR orchestration, threat intelligence platforms, SIEM/UEBA analytics, and building cyber fusion centers. Background includes technical enablement, solution architecture for enterprise and government clients, and implementing security automation workflows across IR, TIP, and SOC use cases.
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Every tactic, technique, and sub-technique used in this threat has been identified and mapped to the MITRE ATT&CK framework for consistent, actionable threat language.
Observables and indicators of compromise (IOCs) have been extracted and cataloged. Risk has been assessed and correlated with known threat actors and historical campaigns.
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