Incident Score: Analysis & Impact (NOM1785422646)
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 Noma Security's Vulnerability 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 Noma Security 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 Noma Security breach identified under incident ID NOM1785422646.
The analysis begins with a detailed overview of Noma Security's information like the linkedin page: https://www.linkedin.com/company/noma-security, the number of followers: 8735, the industry type: Computer and Network Security and the number of employees: 109 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 751 and after the incident was 745 with a difference of -6 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 Noma Security and their customers.
Ruflo recently reported "Critical Ruflo AI Agent Flaw Exposes Enterprises to Full System Takeover", a noteworthy cybersecurity incident.
Researchers at Noma Security uncovered a critical vulnerability (CVE-2024-XXXX, CVSS 10.0) in Ruflo, an open-source AI agent platform, allowing unauthenticated attackers to hijack enterprise AI environments with a single HTTP request.
The disruption is felt across the environment, affecting Ruflo AI agent platform (versions prior to 3.16.3), and exposing LLM API keys, user conversations, AI memory (AgentDB).
In response, moved swiftly to contain the threat with measures like Close firewall access to ports 3001 (MCP Bridge) and 27017 (MongoDB), and began remediation that includes Upgrade to Ruflo version 3.16.3, rotate LLM API keys, audit AgentDB for malicious entries, while recovery efforts such as Inspect MongoDB for tampering continue, and stakeholders are being briefed through Public security advisory issued by Ruflo.
The case underscores how Vulnerability disclosed and patched, teams are taking away lessons such as Systemic risks in AI orchestration platforms, including exposed control interfaces, lack of authentication defaults, and writable AI memory stores. Highlights the need for strict access controls, memory audits, and credential rotation in AI deployments, and recommending next steps like Upgrade to Ruflo version 3.16.3 or later, Close firewall access to ports 3001 (MCP Bridge) and 27017 (MongoDB) and Rotate all LLM API keys, with advisories going out to stakeholders covering Public security advisory issued by Ruflo.
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 high confidence (90%), supported by evidence indicating exposed Model Context Protocol (MCP) bridge...allowing unauthenticated attackers and External Remote Services (T1133) with moderate to high confidence (80%), supported by evidence indicating mCP bridge, an Express.js server, handles all AI agent tool invocations. Under the Execution tactic, the analysis identified Command and Scripting Interpreter (T1059) with high confidence (90%), supported by evidence indicating remote code execution via Ruflo’s `terminal_execute` tool. Under the Credential Access tactic, the analysis identified Unsecured Credentials: Cloud Instance Metadata API (T1552.005) with moderate to high confidence (80%), supported by evidence indicating theft of LLM API keys from environment variables. Under the Collection tactic, the analysis identified Data from Local System (T1005) with high confidence (90%), supported by evidence indicating access to user conversations stored in MongoDB and Data from Information Repositories (T1213) with moderate to high confidence (80%), supported by evidence indicating aI memory poisoning by injecting malicious entries into Ruflo’s AgentDB. Under the Persistence tactic, the analysis identified Server Software Component: Web Shell (T1505.003) with moderate to high confidence (70%), supported by evidence indicating deployment of attacker-controlled AI agent swarms and Event Triggered Execution: Windows Management Instrumentation Event Subscription (T1546.003) with moderate confidence (60%), supported by evidence indicating aI memory poisoning enabling persistent influence over future AI responses. Under the Lateral Movement tactic, the analysis identified Exploitation of Remote Services (T1210) with moderate to high confidence (70%), supported by evidence indicating mCP bridge handles database operations and shell commands. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (80%), supported by evidence indicating potential data exfiltration via remote code execution. Under the Impact tactic, the analysis identified Resource Hijacking (T1496) with high confidence (90%), supported by evidence indicating full system takeover of enterprise AI environments and Data Manipulation: Transmitted Data Manipulation (T1565.002) with moderate to high confidence (80%), supported by evidence indicating aI memory poisoning by injecting malicious entries into AgentDB. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.
Sources & References
- Noma Security Rankiteo Cyber Incident Details: https://www.rankiteo.com/company/noma-security/incident/NOM1785422646
- Noma Security CyberSecurity Rating page: https://www.rankiteo.com/company/noma-security
- Noma Security Rankiteo Cyber Incident Blog Article: https://blog.rankiteo.com/nom1785422646-ruflo-vulnerability-july-2026/
- Noma Security CyberSecurity Score History: https://www.rankiteo.com/company/noma-security/history
- Noma Security CyberSecurity Incident Source: https://www.csoonline.com/article/4203408/critical-ruflo-flaw-lets-attackers-hijack-ai-agents-through-exposed-mcp-bridge.html
- Rankiteo A.I CyberSecurity Rating methodology: https://www.rankiteo.com/Images/rankiteo_algo.pdf
- Rankiteo TPRM Scoring methodology: https://static.rankiteo.com/model/rankiteo_tprm_methodology.pdf