Comparison Overview
J&J Innovative Medicine Medical Affairs

J&J Innovative Medicine Medical Affairs
920 US-202, Raritan, New Jersey, US, 08869
Last Update: 22/04/2026
The goal of the J&J Innovative Medicine Medical Affairs LinkedIn page is to share oncology, immunology, and neuroscience-related medical & scientific information with US healthcare professionals (HCPs). We will share information about scientific data presented at medica...

Intas Pharmaceuticals
Sarkhej Gandhinagar Highway, Near Sola Bridge, Ahmedabad, Gujarat, IN
Last Update: 01/04/2026
Intas is one of the leading multinational pharmaceutical formulation development, manufacturing, and marketing organization in the world. It has been growing at 19% CAGR and crossed the $2.5 billion mark in the past financial year. The company has set up a network of su...
Compliance Ranges Comparison

J&J Innovative Medicine Medical Affairs







Intas Pharmaceuticals






Benchmark & Cyber Underwriting Signals
Incidents vs Pharmaceutical Manufacturing Industry Avg (This Year)
No incidents recorded for J&J Innovative Medicine Medical Affairs in 2026.
Incidents vs Pharmaceutical Manufacturing Industry Avg (This Year)
No incidents recorded for Intas Pharmaceuticals in 2026.
Incident History - J&J Innovative Medicine Medical Affairs (X = Date, Y = Severity)
J&J Innovative Medicine Medical Affairs cyber incidents detection timeline including parent company and subsidiaries.
Incident History - Intas Pharmaceuticals (X = Date, Y = Severity)
Intas Pharmaceuticals cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents

J&J Innovative Medicine Medical Affairs

Intas Pharmaceuticals
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.