Comparison Overview
Sigma Software Group![[24]7.ai](https://images.rankiteo.com/companyimages/24-7-ai.jpeg)
Sigma Software Group
Hornsgatan 1, Stockholm, 18 46, SE
Last Update: 02/04/2026
Sigma Software Group, an award-winning and trusted IT partner, has been serving customers for over 22 years, providing comprehensive IT solutions to various businesses, ranging from startups to established software product houses. As one of Europe's substantial IT consu...
[24]7.ai
2105 S Bascom Ave, Campbell, 95008, US
Last Update: 13/09/2026
[24]7.ai™ customer engagement solutions use conversational artificial intelligence to understand customer intent, enabling companies to create personalized, predictive, and effortless customer experiences across all channels; attract and retain customers; boost agent pr...
Compliance Ranges Comparison
Sigma Software Group
[24]7.ai
Benchmark & Cyber Underwriting Signals
Incidents vs Software Development Industry Avg (This Year)
No incidents recorded for Sigma Software Group in 2026.
Incidents vs Software Development Industry Avg (This Year)
No incidents recorded for [24]7.ai in 2026.
Incident History - Sigma Software Group (X = Date, Y = Severity)
Sigma Software Group cyber incidents detection timeline including parent company and subsidiaries.
Incident History - [24]7.ai (X = Date, Y = Severity)
[24]7.ai cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
Sigma Software Group
[24]7.ai
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.