Vogue A.I CyberSecurity Scoring
04/04/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Vogue in 2026.
No incidents recorded for Vogue in 2026.
No incidents recorded for Vogue in 2026.
Book and Periodical Publishing
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Latest updates, reports, and threat intel affecting the global network.
The hack affects all Condé Nast entities as well, including the 'New Yorker,' 'Vogue,' and 'Vanity Fair.'
Hackers have leaked a database containing over 2.3 million WIRED subscriber records, marking a major breach at Condé Nast,...
With an evolving nature of cyber threats accelerating at a speed considered too quick to be processed by most establishments, the demand for...
As fashion's reliance on AI deepens — from design and forecasting, to digital clienteling and chatbots — new risks are emerging.
Securing AI systems represents cybersecurity's next frontier, creating specialized career paths as organizations grapple with novel...
Discover how the CommBank-sponsored Vogue Codes Summit helps to inspire more women to pursue careers in Artificial Intelligence (AI) and...
Cisco is talking up the integration of security into network infrastructure such as its latest Catalyst switches, claiming this is vital to AI applications.
A host of big tech companies are relying on AI-generated code, and developers globally are following suit.
M&S is not the only fashion business to have been targeted in recent weeks. Last week, Dior confirmed it had been struck by a cyber attack. In a...
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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