Inside the Scattered Lapsus$ Honeypot: How Researchers Turned the Tables in 2024
Executive Summary
In early 2024, cybersecurity researchers staged a sophisticated deception operation targeting Scattered Lapsus$, also known as ShinyHunters, by deploying a realistic but fake dataset as a honeypot. The operation was designed to lure threat actors with what appeared to be sensitive credentials and data, allowing security experts to monitor the attackers' methods and behaviors in real time. Once engaged, Scattered Lapsus$ actors attempted lateral movement and data exfiltration using various covert tools and techniques, but their actions were closely tracked and documented. This resulted in a rare glimpse into the group's tactics, techniques, and procedures, as well as validation of multiple defensive controls.
This incident is particularly noteworthy as it demonstrates the growing effectiveness of proactive threat intelligence gathering through deception and honeypots. With threat groups like Lapsus$ and ShinyHunters targeting high-value data across industries, similar methods are being adopted by defenders to preemptively understand and disrupt sophisticated adversaries.
Why This Matters Now
The use of advanced honeypots to proactively monitor and disrupt threat actors like Scattered Lapsus$ reflects a trend toward more aggressive, intelligence-driven defense strategies. As cybercriminal groups evolve their data theft tactics and target broader sectors, organizations urgently need similar proactive controls to anticipate threats and fulfill increasing compliance and regulatory requirements.
Attack Path Analysis
Attackers, posing as genuine researchers, were lured into a honeypot environment where they gained an initial foothold through interactions with fake datasets. They attempted to escalate privileges via potentially vulnerable services or misconfigurations. Subsequent lateral movement efforts aimed to discover and access additional internal resources. Command and Control was established through outbound communication channels for further instructions and exfiltration preparation. Data exfiltration involved attempting to move or copy decoy data out of the environment. The impact was minimal as the dataset was counterfeit and no actual customer data was compromised.
Kill Chain Progression
Initial Compromise
Description
Attackers accessed the honeypot environment by engaging with exposed research assets and datasets, simulating initial infiltration via public-facing cloud services.
Related CVEs
CVE-2025-31324
CVSS 9.8A missing authentication vulnerability in SAP NetWeaver Visual Composer allows remote attackers to access critical functionality without proper authentication.
Affected Products:
SAP NetWeaver Visual Composer – All versions prior to the latest patch
Exploit Status:
exploited in the wildCVE-2025-42999
CVSS 9.8A deserialization vulnerability in SAP NetWeaver Visual Composer allows remote attackers to execute arbitrary code via crafted payloads.
Affected Products:
SAP NetWeaver Visual Composer – All versions prior to the latest patch
Exploit Status:
exploited in the wild
MITRE ATT&CK® Techniques
Valid Accounts
Exploit Public-Facing Application
Phishing
Data from Local System
Transfer Data to Cloud Account
Exfiltration Over C2 Channel
Application Layer Protocol
Potential Compliance Exposure
Mapping incident impact across multiple compliance frameworks.
PCI DSS 4.0 – Multi-factor authentication (MFA) for all access
Control ID: 8.2.4
NYDFS 23 NYCRR 500 – Cybersecurity Policy
Control ID: 500.03
DORA (Digital Operational Resilience Act) – ICT Risk Management Framework
Control ID: Article 9
CISA Zero Trust Maturity Model (ZTMM) 2.0 – Identity and Access Management
Control ID: Identity Pillar, Step 3: Least Privilege
NIS2 Directive – Risk management measures
Control ID: Article 21
Sector Implications
Industry-specific impact of the vulnerabilities, including operational, regulatory, and cloud security risks.
Financial Services
Lapsus$ data theft targeting creates critical exposure requiring encrypted traffic, zero trust segmentation, and enhanced anomaly detection capabilities for customer data protection.
Health Care / Life Sciences
Healthcare systems face elevated data exfiltration risks from Lapsus$ hunters, necessitating HIPAA-compliant east-west traffic security and threat detection frameworks.
Information Technology/IT
IT sector honeypot incidents expose infrastructure vulnerabilities requiring multicloud visibility, Kubernetes security, and inline IPS protection against sophisticated threat actors.
Computer Software/Engineering
Software engineering environments targeted by ShinyHunters require cloud-native security fabric and egress policy enforcement to prevent intellectual property theft.
Sources
- Scattered Lapsus$ Hunters Snared in Cyber Researcher Honeypothttps://www.darkreading.com/endpoint-security/scattered-lapsus-hunters-researcher-honeypotVerified
- Critical SAP Vulns Under Exploitationhttps://www.darkreading.com/cyberattacks-data-breaches/critical-sap-vulns-under-exploitationVerified
- NVD - CVE-2025-31324https://nvd.nist.gov/vuln/detail/CVE-2025-31324Verified
- NVD - CVE-2025-42999https://nvd.nist.gov/vuln/detail/CVE-2025-42999Verified
Frequently Asked Questions
Cloud Native Security Fabric Mitigations and ControlsCNSF
Implementation of CNSF controls such as Zero Trust Segmentation, East-West Traffic Security, Egress Policy Enforcement, and threat-aware inline inspection could have stifled attacker progression at several kill chain stages. These controls provide layered defense, containing movement, detecting anomalies, and preventing sensitive data egress even if initial access is achieved.
Control: Zero Trust Segmentation
Mitigation: Access to resources would be restricted to vetted identities and services.
Control: Threat Detection & Anomaly Response
Mitigation: Suspicious privilege escalation attempts would be quickly detected and alerted.
Control: East-West Traffic Security
Mitigation: Lateral moves between workloads/services are blocked or logged for investigation.
Control: Inline IPS (Suricata)
Mitigation: C2 traffic is detected or blocked in real time.
Control: Egress Security & Policy Enforcement
Mitigation: Unapproved data transfers are blocked and/or flagged.
Potential impacts are rapidly detected, investigated, and contained.
Impact at a Glance
Affected Business Functions
- Data Management
- Customer Relationship Management
Estimated downtime: 3 days
Estimated loss: $500,000
Potential exposure of sensitive customer data, including personally identifiable information (PII), due to unauthorized access facilitated by the exploited vulnerabilities.
Recommended Actions
Key Takeaways & Next Steps
- • Implement Zero Trust Segmentation to restrict access to cloud assets by default and enable least-privilege connectivity.
- • Enforce strong east-west traffic controls to contain lateral movement within and across cloud environments.
- • Deploy continuous anomaly detection and real-time threat response to quickly identify abnormal privilege usage or suspicious behaviors.
- • Apply robust egress filtering and encryption to prevent unauthorized data exfiltration and C2 communications.
- • Centralize multicloud visibility and policy management to maintain situational awareness and enforce consistent controls against evolving threats.
