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Analyze » Ollama » OPEOLL1769611516

Incident Score: Analysis & Impact (OPEOLL1769611516)

The details regarding individual company incidents & reports gives you full view from every side.

Rankiteo Score Impact Analysis

Rankiteo Incident Impact-20
Company Score Before Incident752 / 1000
Company Score After Incident732 / 1000
Company LinkView Ollama Profile
INCIDENT NUMBEROPEOLL1769611516
Type of Cyber IncidentCyber Attack
ATTACK VECTORExposed AI endpoints, Misconfigured APIs, Unauthenticated MCP servers, Publicly accessible development environments
DATA EXPOSEDSensitive data from prompts and...
INCIDENT DATE27/01/2026
STATUSActive

Key Highlights From The Incident Analysis

  • Timeline of Ollama'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 Ollama 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 Ollama breach identified under incident ID OPEOLL1769611516.

The analysis begins with a detailed overview of Ollama's information like the linkedin page: https://www.linkedin.com/company/ollama, the number of followers: 146140, the industry type: Technology, Information and Internet and the number of employees: 45 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 752 and after the incident was 732 with a difference of -20 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 Ollama and their customers.

A newly reported cybersecurity incident, "Large-Scale 'LLMjacking' Campaign Exploits Exposed AI Endpoints for Profit", has drawn attention.

Researchers at Pillar Security uncovered a sophisticated cybercrime operation dubbed 'Bizarre Bazaar,' targeting exposed or poorly secured AI infrastructure for financial gain.

The disruption is felt across the environment, affecting Self-hosted LLMs, Exposed APIs and MCP servers, and exposing Sensitive data from prompts and conversation histories.

Formal response steps have not been shared publicly yet.

The case underscores how Active.

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%), with evidence including exploits misconfigured or unauthenticated AI services, and exposed APIs, publicly accessible MCP servers and External Remote Services (T1133) with moderate to high confidence (80%), with evidence including targeting exposed Ollama endpoints on port 11434, and openAI-compatible APIs on port 8000. Under the Execution tactic, the analysis identified Command and Scripting Interpreter (T1059) with moderate to high confidence (70%), supported by evidence indicating pivoting into internal systems via MCP servers for lateral movement. Under the Persistence tactic, the analysis identified Valid Accounts (T1078) with moderate confidence (60%), supported by evidence indicating reselling API access on darknet markets via Silver.inc. Under the Privilege Escalation tactic, the analysis identified Exploitation for Privilege Escalation (T1068) with moderate to high confidence (70%), supported by evidence indicating pivoting into internal systems via MCP servers. Under the Defense Evasion tactic, the analysis identified Impair Defenses: Disable or Modify Tools (T1562.001) with moderate confidence (50%), supported by evidence indicating misconfigured or unauthenticated AI services exploited. Under the Credential Access tactic, the analysis identified Steal Application Access Token (T1528) with moderate to high confidence (80%), supported by evidence indicating unauthorized access to LLM endpoints for reselling API access. Under the Discovery tactic, the analysis identified Active Scanning (T1595) with high confidence (90%), supported by evidence indicating scans the internet for vulnerable endpoints via Shodan/Censys. Under the Lateral Movement tactic, the analysis identified Exploitation of Remote Services (T1210) with moderate to high confidence (80%), supported by evidence indicating pivoting into internal systems via MCP servers. Under the Collection tactic, the analysis identified Data from Local System (T1005) with moderate to high confidence (80%), supported by evidence indicating exfiltrating sensitive data from prompts and conversation histories. Under the Command and Control tactic, the analysis identified Application Layer Protocol (T1071) with moderate to high confidence (70%), supported by evidence indicating compromised AI endpoints used for cryptocurrency mining. Under the Exfiltration tactic, the analysis identified Exfiltration Over C2 Channel (T1041) with moderate to high confidence (80%), supported by evidence indicating exfiltrating sensitive data from prompts and conversation histories and Transfer Data to Cloud Account (T1537) with moderate confidence (60%), supported by evidence indicating reselling access to compromised AI infrastructure via Silver.inc. Under the Impact tactic, the analysis identified Resource Hijacking (T1496) with high confidence (90%), supported by evidence indicating cryptocurrency mining using stolen computing resources. These correlations help security teams understand the attack chain and develop appropriate defensive measures based on the observed tactics and techniques.

Initial Access
Exploit Public-Facing Application (90%)
External Remote Services (80%)
Execution
Command and Scripting Interpreter (70%)
Persistence
Valid Accounts (60%)
Privilege Escalation
Exploitation for Privilege Escalation (70%)
Defense Evasion
Impair Defenses: Disable or Modify Tools (50%)
Credential Access
Steal Application Access Token (80%)
Discovery
Active Scanning (90%)
Lateral Movement
Exploitation of Remote Services (80%)
Collection
Data from Local System (80%)
Command and Control
Application Layer Protocol (70%)
Exfiltration
Exfiltration Over C2 Channel (80%)
Transfer Data to Cloud Account (60%)
Impact
Resource Hijacking (90%)

Sources & References