Comparison Overview
Best Buy
Best Buy
7601 Penn Ave South, Richfield, 55423-3645, US
Last Update: 07/09/2026
At Best Buy, our purpose is to enrich lives through technology. We do that by leveraging our unique combination of tech expertise and human touch to meet our customers’ everyday needs, whether they come to us online, visit our stores or invite us into their homes. With ...
CarMax
12800 Tuckahoe Creek Parkway, Richmond, Virginia, US, 23238
Last Update: 14/09/2026
We're fueled by a common goal: creating an iconic car-buying experience. We make car-buying fair, accessible, and joyful for all. We are committed to making progress in how we positively impact our society, now and in the future. Above all, we care about people. We are ...
Compliance Ranges Comparison
Best Buy
CarMax
Benchmark & Cyber Underwriting Signals
Incidents vs Retail Industry Avg (This Year)
Best Buy has 44.44% fewer incidents than the average of same-industry companies with at least one recorded incident.
Incidents vs Retail Industry Avg (This Year)
CarMax has 2.91% fewer incidents than the average of all companies with at least one recorded incident.
Incident History - Best Buy (X = Date, Y = Severity)
Best Buy cyber incidents detection timeline including parent company and subsidiaries.
Incident History - CarMax (X = Date, Y = Severity)
CarMax cyber incidents detection timeline including parent company and subsidiaries.
Notable Incidents
Best Buy
CarMax
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.