Incident Score: Analysis & Impact (KALPOL1777451477)
The details regarding individual company incidents & reports gives you full view from every side.
Rankiteo Score Impact Analysis
Key Highlights From The Incident Analysis
- Timeline of Polymarket's Cyber Attack and lateral movement inside company's environment.
- Overview of affected data sets, including SSNs and PHI, and why they materially increase incident severity.
- How Rankiteo’s incident engine converts technical details into a normalized incident score.
- How this cyber incident impacts Polymarket Rankiteo cyber scoring and cyber rating.
- Rankiteo’s MITRE ATT&CK correlation analysis for this incident, with associated confidence level.
Full Incident Analysis Transcript
In this Rankiteo incident briefing, we review the Polymarket breach identified under incident ID KALPOL1777451477.
The analysis begins with a detailed overview of Polymarket's information like the linkedin page: https://www.linkedin.com/company/polymarket, the number of followers: 34857, the industry type: Software Development and the number of employees: 242 employees
After the initial compromise, the video explains how Rankiteo's incident engine converts technical details into a normalized incident score. The incident score before the incident was 754 and after the incident was 736 with a difference of -18 which is could be a good indicator of the severity and impact of the incident.
In the next step of the video, we will analyze in more details the incident and the impact it had on Polymarket and their customers.
Kalshi recently reported "Cybercriminals Exploit Prediction Markets to Profit from Insider Knowledge", a noteworthy cybersecurity incident.
Cybercriminals are leveraging prediction markets like Kalshi and Polymarket to profit from foreknowledge of real-world events, turning future outcomes into tradable assets.
The disruption is felt across the environment, affecting Prediction markets (Kalshi, Polymarket), DeFi platforms and Regulatory filing systems, and exposing Nonpublic corporate data, Regulatory filings and Court documents.
Formal response steps have not been shared publicly yet.
The case underscores how teams are taking away lessons such as Prediction markets introduce a financial layer where events become tradable commodities, creating new opportunities for cybercriminals to exploit nonpublic information or manipulate systems. Existing laws like data breach disclosure requirements may inadvertently create exploitable windows for attackers, and recommending next steps like Enhance monitoring of prediction markets for suspicious betting patterns tied to undisclosed events, Improve security of regulatory filing systems (e.g., EDGAR, PACER) to prevent unauthorized access and Strengthen DeFi project security to mitigate vulnerabilities that could be exploited for market manipulation.
Finally, we try to match the incident with the MITRE ATT&CK framework to see if there is any correlation between the incident and the MITRE ATT&CK framework.
The MITRE ATT&CK framework is a knowledge base of techniques and sub-techniques that are used to describe the tactics and procedures of cyber adversaries. It is a powerful tool for understanding the threat landscape and for developing effective defense strategies.
MITRE ATT&CK® Correlation Analysis
Rankiteo's analysis has identified several MITRE ATT&CK tactics and techniques associated with this incident, each with varying levels of confidence based on available evidence. Under the Initial Access tactic, the analysis identified Exploit Public-Facing Application (T1190) with moderate to high confidence (70%), supported by evidence indicating vulnerability exploited such as Regulatory Filing Systems (e.g., EDGAR, PACER) and Valid Accounts (T1078) with moderate confidence (60%), supported by evidence indicating unauthorized access to nonpublic corporate data and regulatory filings. Under the Credential Access tactic, the analysis identified Unsecured Credentials (T1552) with moderate to high confidence (70%), supported by evidence indicating early access to nonpublic information via regulatory filing systems and OS Credential Dumping (T1003) with moderate confidence (50%), supported by evidence indicating access to undisclosed data breaches and court documents. Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating nonpublic corporate data, regulatory filings, court documents compromised and Data from Information Repositories (T1213) with high confidence (90%), supported by evidence indicating access to EDGAR (SEC Filing System) and PACER (Court Document System). Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate confidence (60%), supported by evidence indicating nonpublic data used to manipulate prediction markets for financial gain. Under the Impact tactic, the analysis identified Defacement (T1491) with moderate confidence (50%), supported by evidence indicating disinformation campaigns to skew prediction market outcomes and Data Manipulation (T1565) with moderate to high confidence (80%), supported by evidence indicating sensor manipulation, oracle exploits, and voting tampering to rig results. Under the Defense Evasion tactic, the analysis identified Masquerading (T1036) with moderate confidence (60%), supported by evidence indicating attackers act as passive predictors while manipulating outcomes and Hide Artifacts (T1564) with moderate confidence (50%), supported by evidence indicating exploitation of undisclosed data breaches and vulnerabilities. Under the Resource Development tactic, the analysis identified Obtain Capabilities (T1588) with moderate to high confidence (70%), supported by evidence indicating leveraging prediction markets as a new revenue stream for cybercrime. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.