Comparison Overview
TLGG Consulting
TLGG Consulting
Paul-Lincke-Ufer 40, Berlin, undefined, 10999, DE
Last Update: 23/04/2026
Our world is changing faster than ever – TLGG Consulting helps businesses stay ahead. We believe technological, social and economic change presents challenges and opportunities. For consumers, employees, shareholders and society. We stand for new approaches, unique solu...
McKinsey & Company
US
Last Update: 18/09/2026
McKinsey & Company is a global management consulting firm. We are the trusted advisor to the world's leading businesses, governments, and institutions. We work with leading organizations across the private, public and social sectors. Our scale, scope, and knowledge ...
Compliance Ranges Comparison
TLGG Consulting
McKinsey & Company
Benchmark & Cyber Underwriting Signals
Incidents vs Business Consulting and Services Industry Avg (This Year)
No incidents recorded for TLGG Consulting in 2026.
Incidents vs Business Consulting and Services Industry Avg (This Year)
McKinsey & Company has 2.91% fewer incidents than the average of all companies with at least one recorded incident.
Incident History - TLGG Consulting (X = Date, Y = Severity)
TLGG Consulting cyber incidents detection timeline including parent company and subsidiaries.
Incident History - McKinsey & Company (X = Date, Y = Severity)
McKinsey & Company cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
TLGG Consulting
McKinsey & Company
FAQ
Latest Global CVEs
vLLM through 0.29.0 fails to properly clean up decode-side metadata for rejected inference requests in prefill/decode disaggregated deployments. Remote attackers can submit requests with max_tokens=0 to exhaust decode-worker memory without bound until the worker restarts.
- https://github.com/vllm-project/vllm
- https://github.com/vllm-project/vllm/blob/v0.29.0/vllm/distributed/kv_transfer/kv_connector/v1/nixl/push_worker.py#L162-L181
- https://github.com/vllm-project/vllm/pull/55677
- https://www.vulncheck.com/advisories/vllm-through-0.29.0-memory-exhaustion-via-rejected-requests
redis-parser through 3.0.0 contains a denial of service vulnerability in the RESP protocol parser that allows malicious Redis endpoints to crash the client process through unbounded recursion on nested arrays. Attackers can send crafted RESP byte streams with repeated array headers that exhaust the V8 call stack, causing an uncaught RangeError that terminates the Node.js process without triggering error handling callbacks.
- https://github.com/NodeRedis/node-redis-parser
- https://github.com/NodeRedis/node-redis-parser/blob/701655430f5f7d9ca00892a02f7eefcbc1193a98/lib/parser.js#L204-L213
- https://github.com/NodeRedis/node-redis-parser/blob/701655430f5f7d9ca00892a02f7eefcbc1193a98/lib/parser.js#L291-L306
- https://github.com/redis/ioredis/issues/2108
- https://www.vulncheck.com/advisories/redis-parser-through-3.0.0-denial-of-service-via-unbounded-recursion
Improper neutralization of special elements in output used by a downstream component ('injection') in Azure Cosmos DB allows an authorized attacker to elevate privileges over a network.
Server-side request forgery (ssrf) in Azure AI Foundry allows an unauthorized attacker to elevate privileges over a network.
Missing authentication for critical function in Azure AI Foundry allows an unauthorized attacker to elevate privileges over a network.