Dark Web Marketplaces and Cybercrime
How underground marketplaces facilitate the cybercrime economy, what is sold, and how this intelligence informs defensive strategies.
Last updated Apr 7, 2026Current
Continue your mission
How underground marketplaces facilitate the cybercrime economy, what is sold, and how this intelligence informs defensive strategies.
How underground marketplaces facilitate the cybercrime economy, what is sold, and how this intelligence informs defensive strategies. Understanding this threat helps security teams prioritize defenses and develop appropriate detection and response strategies.
This threat represents a significant concern for organizations across industries. The attack methodology typically involves multiple phases: initial reconnaissance, gaining access through exploited vulnerabilities or social engineering, establishing persistence, lateral movement through the target environment, and achieving the attacker's ultimate objective.
The technical sophistication varies, but the common thread is that attackers optimize for efficiency and return on investment. They reuse proven techniques, leverage publicly available tools and exploits, and target organizations with known security gaps.
The initial access vector often involves one of several common approaches: exploitation of internet-facing vulnerabilities, phishing campaigns targeting employees with privileged access, abuse of trusted relationships with third-party vendors, or compromise of exposed credentials from previous data breaches.
Once inside the environment, threat actors employ a range of techniques for privilege escalation and lateral movement. They target credential stores, exploit misconfigurations in Active Directory, and abuse legitimate remote administration tools to move between systems while blending in with normal activity.
Detection should focus on behavioral indicators rather than specific malware signatures, which change frequently. Key detection opportunities include: unusual authentication patterns, unexpected process execution chains, abnormal network traffic to external destinations, modifications to scheduled tasks or startup items, and access to sensitive data stores outside normal patterns.
SIEM correlation rules should combine multiple weak signals into high-confidence alerts. A single anomalous event may be noise; several related anomalies in a short time window likely indicate an active threat.
Endpoint detection and response (EDR) tools provide critical visibility into process-level activity that network monitoring alone cannot capture.
Preventive controls include: prompt patching of internet-facing systems, enforcement of multi-factor authentication on all remote access, network segmentation to limit lateral movement, privileged access management to reduce credential exposure, and email filtering with link and attachment sandboxing.
Detective controls include: SIEM monitoring with tuned alert rules, endpoint detection and response on all systems, network traffic analysis for command-and-control patterns, and regular threat hunting focused on known TTPs associated with this threat.
Response preparedness includes: documented incident response procedures, tested communication plans, preserved forensic collection capability, and established relationships with law enforcement and incident response firms.
Historical incidents involving this threat consistently reveal common organizational failures: delayed patching of known vulnerabilities, insufficient network segmentation allowing unrestricted lateral movement, lack of MFA on critical systems, and inadequate logging that hinders forensic investigation.
Organizations that invest in fundamentals, including patching, MFA, segmentation, and monitoring, dramatically reduce their exposure to this threat. Sophisticated defenses matter less than consistent execution of basic controls.
CDA Theater missions that address topics covered in this article.
Rogue access point detection identifies unauthorized wireless APs on the network using WIPS sensors, wired-side monitoring, and signal triangulation to prevent network bypass.
LLM security risks include data leakage, prompt injection, model supply chain attacks, and unauthorized tool execution, requiring organizations to treat AI models as high-privilege components.
How physical security failures enable cyber attacks, from tailgating and shoulder surfing to device theft and dumpster diving.
Written by CDA Wiki Team
Found an issue? Help improve this article.