Comparison Overview
TATA AIG General Insurance Company Limited

TATA AIG General Insurance Company Limited
15th floor , Tower A , Peninsula Business Park, Lower Parel, Mumbai , 400013, IN
Last Update: 01/04/2026
At TATA AIG General Insurance, we wear our achievements like a badge of honour – proudly and with gratitude! We have been recognized as one of India’s Top 100 Best Companies to Work For and among the Top 25 Best Workplaces in BFSI in 2024. As a joint venture between th...

ICICI Lombard
ICICI Lombard House 414, P Balu Marg Near Siddhi Vinayak Temple, Prabhadevi , Mumbai , 400025, IN
Last Update: 01/04/2026
ICICI Lombard is one of the leading private general insurance company in the country. The Company offers a well-diversified range of products through multiple distribution channels, including motor, health, crop, fire, personal accident, marine, engineering, and liabi...
Compliance Ranges Comparison

TATA AIG General Insurance Company Limited







ICICI Lombard






Benchmark & Cyber Underwriting Signals
Incidents vs Insurance Industry Avg (This Year)
No incidents recorded for TATA AIG General Insurance Company Limited in 2026.
Incidents vs Insurance Industry Avg (This Year)
No incidents recorded for ICICI Lombard in 2026.
Incident History - TATA AIG General Insurance Company Limited (X = Date, Y = Severity)
TATA AIG General Insurance Company Limited cyber incidents detection timeline including parent company and subsidiaries.
Incident History - ICICI Lombard (X = Date, Y = Severity)
ICICI Lombard cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

TATA AIG General Insurance Company Limited

ICICI Lombard
FAQ
Latest Global CVEs
Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). A low-privileged authenticated user can send a specially crafted request to a Kibana machine learning feature, causing the server to exhaust available memory and become unavailable to all users.
Uncontrolled Resource Consumption (CWE-400) in Kibana can lead to denial of service via Excessive Allocation (CAPEC-130). An authenticated attacker with low-privilege access can trigger a denial of service condition in Kibana by sending a specially crafted, oversized request payload. Processing this user-supplied input requires resource-intensive memory allocation that can exhaust the available heap memory in the Kibana process, causing it to crash and become unavailable to all users.
Authorization Bypass Through User-Controlled Key (CWE-639) in Kibana can lead to information disclosure via user-supplied identifiers that reference scheduled query result data from Kibana Spaces the requester is not authorized to access.
Incorrect Authorization (CWE-863) in Kibana can lead to integrity compromise of Machine Learning audit and notification records via Accessing Functionality Not Properly Constrained by ACLs (CAPEC-1). A vulnerability exists in Kibana's Machine Learning functionality where a Machine Learning management endpoint performs an insufficient authorization check. The endpoint validates only a coarse privilege level but does not verify that the requesting user has access to the specific Machine Learning job or notification resources provided in the request. As a result, a low-privileged user with Machine Learning access in any Kibana space can manipulate Machine Learning audit and notification records for arbitrary jobs—including jobs in other spaces or belonging to other users—by leveraging Kibana's internally elevated credentials to write to restricted Machine Learning system indices that the user cannot access directly.
Uncontrolled Recursion (CWE-674) in Elasticsearch can lead to denial of service via a specially crafted search request submitted by a low-privileged authenticated user. A user with read-level index access can submit a request that triggers unbounded recursive processing within the Elasticsearch query evaluation component, causing a fatal error that terminates the affected node. In single-node deployments, this results in complete service outage; in multi-node clusters, it causes repeated node restarts and sustained availability degradation.