WTI Transport A.I CyberSecurity Scoring
22/06/2026
Access Monitoring Plan
Access Monitoring Plan
WTI Transport has 36.31% fewer incidents than the average of same-industry companies with at least one recorded incident.
WTI Transport has 0.99% fewer incidents than the average of all companies with at least one recorded incident.
WTI Transport reported 1 incidents this year: 0 cyber attacks, 1 ransomware, 0 vulnerabilities, 0 data breaches, compared to industry peers with at least 1 incident.
Transportation/Trucking/Railroad
Swift Transportation is the largest full-truckload motor carrier in North America. Based in Phoenix, Arizona, the Swift terminal network includes over thirty full-service facilities in the United States and Mexico. Swift provides a full line of service solutions, including linehaul, flatbed, intermodal, refrigerated, dedicated and logistics management. Swift also offers careers in operations, finance, account management, shop mechanics, human resources, payroll, and many more. Swift offers more than just a career - Swift is family. Want to join our team? https://www.swifttrans.com/careers Want to be a Driver? Apply Now! driveswift.com
We shape the mobility of the future – simple, personal, connected. SBB’s comprehensive service contract allows us to offer a wide range of exciting careers in all areas of business. Thanks to this variety, many different career paths are possible, which we support through education and training opportunities as well as targeted further development and promotion. Dedicated and active employees are the key to our success, which is why we offer modern employment conditions and promote modern working practices. Join SBB and help us keep Switzerland on the move. For more information about SBB and our exciting range of job opportunities, please visit http://www.sbb.ch/en/group/jobs-careers/working-for-sbb.html and www.sbb.ch/jobs. We look forward to meeting you!
Latest updates, reports, and threat intel affecting the global network.
Dify is an open-source LLM app development platform. Prior to 1.16.0, the PUT /console/api/apps/<app_id>/server endpoint in api/controllers/console/app/mcp_server.py used AppMCPServerController.put() to retrieve an AppMCPServer by the client-supplied server ID without verifying that the server belonged to the requested application and tenant. An authenticated workspace member could therefore change another application's MCP server status and parameters, potentially redirecting data or disabling the service. This issue is fixed in version 1.16.0.
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, a caller can use the request-level media_io_kwargs field to select the GLMGA video backend and supply large values for the fps and max_frames options without a strict work ceiling. GLMGA constructs and deduplicates an attacker-sized pre-decode frame-index list, allowing a compact request and tiny valid video to consume disproportionate CPU time and memory in the shared media-loading executor. This issue is fixed in version 0.30.0.
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, the Rust frontend's track_http_metrics middleware records the raw HTTP method token as a Prometheus label for requests reaching registered routes. An unauthenticated attacker can send unique arbitrary method tokens to unguarded routes such as /tokenize, causing Prometheus's Family::get_or_create function to permanently create counter and histogram label sets. Those label sets increase process memory usage and enlarge the /metrics response until the service or monitoring path is exhausted. This issue is fixed in version 0.30.0.
vLLM is an inference and serving engine for large language models. From 0.24.0 until 0.30.0, the Qwen2VLVideoBackend and Qwen3VLVideoBackend classes accept request-level values for the media_io_kwargs.video.max_frames and media_io_kwargs.video.fps fields without enforcing server-side ceilings. An unauthenticated caller can submit these values to the /tokenize endpoint, causing the sampler to decode every frame selected from attacker-controlled video input, consume disproportionate frontend memory, and potentially terminate the API process before scheduling or admission control. The Rust frontend is not affected because it rejects the media_io_kwargs field. This issue is fixed in version 0.30.0.
vLLM is an inference and serving engine for large language models. Prior to 0.30.0, structured-output request failures can escape request-scoped validation and reach the EngineCore fatal-error path. A per-request backend mismatch can re-raise a grammar compilation exception, padding produced by the ngram_gpu speculative-decoding mode can pass a negative token to guidance validation, and the Rust frontend can admit empty structured-output values that the Python frontend rejects, allowing ordinary constrained-generation requests to terminate the shared engine. This issue is fixed in version 0.30.0.
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