PPG A.I CyberSecurity Scoring
29/03/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for Penguin Publishing Group in 2026.
No incidents recorded for Penguin Publishing Group in 2026.
No incidents recorded for Penguin Publishing Group in 2026.
Forbes Media is a global media, branding and technology company, with a focus on news and information about business, investing, technology, entrepreneurship, leadership and affluent lifestyles. The company publishes Forbes, Forbes Asia, and Forbes Europe magazines as well as Forbes.com. The Forbes brand today reaches more than 94 million people worldwide with its business message each month through its magazines and 37 licensed local editions around the globe, Forbes.com, TV, conferences, research, social and mobile platforms. Forbes Media’s brand extensions include conferences, real estate, education, financial services, and technology license agreements. Forbes is an equal opportunity employer.
Latest updates, reports, and threat intel affecting the global network.
The black-and-white message flickering across computer screens sparked panic at Knights of Old, a 158-year-old U.K. delivery company: “If...
The search giant's negotiations to buy Wiz, a cybersecurity start-up, for $23 billion, come as the Biden administration has taken a hard...
Premium subscribers of Spotify will now be able to listen to free audiobooks as well as music and podcasts.
Penguin Random House, the world's largest book publisher, and smaller U.S. rival Simon & Schuster have scrapped a $2.2 billion deal to merge...
The US Justice Department filed a lawsuit on Tuesday aimed at stopping Penguin Random House, the world's biggest book publisher, from buying competitor Simon &...
Barnes & Noble has concluded that a cyberattack against its computer systems last month didn't compromise customer data — even as publishers...
It's a time of enormous promise, but also of new challenges. Digital technologies literally have become both tools and weapons.
The Penguin Random House Book Fair reached a significant milestone in 2017 as we celebrate twenty years of partnership to raise money to benefit the...
A scam that typically involves a literary agent, scout, or publisher being asked for a manuscript by what appears to be a trusted...
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.
curl -i -X GET 'https://api.rankiteo.com/underwriter-getcompany-history?
linkedin_id=axa' -H 'apikey: YOUR_API_KEY_HERE'
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Rankiteo is a unified scoring and risk platform that analyzes billions of signals weekly to help organizations gain faster, more actionable insights into emerging threats. Empowering teams to outpace adversaries and reduce exposure.