LSI A.I CyberSecurity Scoring
15/02/2026
Access Monitoring Plan
Access Monitoring Plan
No incidents recorded for LinkedIn Social Impact in 2026.
No incidents recorded for LinkedIn Social Impact in 2026.
No incidents recorded for LinkedIn Social Impact in 2026.
At Mercado Libre, we are transforming the way people buy, sell, advertise, pay, finance, and ship across Latin America. We are the leading e-commerce and fintech company in the region, with a presence in 18 countries and a team of more than 120,000 people. We are one of the best places to work in Latin America. Being part of MELI means working with intensity and excellence because we are passionate about what we do and we believe in the value of meritocracy. We overcome our own limits and learn by tackling big challenges. We have an entrepreneurial mindset, we take risks, we reinvent ourselves, and we innovate. We compete as a team to win in a flexible and fun work environment. And so, every day, we create sustainable results that transform the lives of millions of people. We look for people who are passionate about big challenges, who are willing to step out of their comfort zone, give their maximum effort, and take risks as entrepreneurs. Join the team that makes the purpose of democratizing commerce and financial services a reality, transforming the lives of millions across Latin America. Be part of the MELI experience!
Equinix (Nasdaq: EQIX) is the world’s digital infrastructure company®, enabling digital leaders to harness a trusted platform to bring together and interconnect the foundational infrastructure that powers their success. Equinix enables today’s businesses to access all the right places, partners and possibilities they need to accelerate advantage. With Equinix, they can scale with agility, speed the launch of digital services, deliver world-class experiences and multiply their value.
Latest updates, reports, and threat intel affecting the global network.
Is social media good for society? Learn the pros and cons of the debate.
Cybersecurity Researchers at ReliaQuest warn of an ongoing campaign delivered to “high-value individuals” via LinkedIn messages.
A complete list of all the known layoffs in tech, from Big Tech to startups, broken down by month throughout 2024 and 2025.
Exposing the Shadows: North Korean Hackers' Elaborate Remote Work Deception. In the shadowy realm of cyber espionage, few groups cast as...
Social media marketing offers businesses an opportunity to find and communicate directly with their target audience. LinkedIn, in particular...
Discover the impact of social media, its advantages, and disadvantages. Learn how social platforms shape communication, business, and daily...
The fast-growing Brisbane company has delivered more than $3.2 million value to First Nations peoples while helping solve a national skills...
The comfort zone in cybersecurity is gone. Attackers are scaling down, focusing tighter, and squeezing more value from fewer, high-impact...
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, the fix for CVE-2026-22778, which introduced a sanitize_message helper that strips object-repr memory addresses from error messages before they reach the client, is incomplete: several response paths echo str(exc) directly to clients without calling sanitize_message. The unsanitized sites include the Anthropic API router in vllm/entrypoints/anthropic/api_router.py (the POST /v1/messages and POST /v1/messages/count_tokens handlers), the Server-Sent Events streaming converter in vllm/entrypoints/anthropic/serving.py, and the realtime speech-to-text WebSocket in vllm/entrypoints/speech_to_text/realtime/connection.py. These paths catch the exception inside the route coroutine and construct the JSONResponse themselves, bypassing the sanitizing global FastAPI exception handler, and WebSocket frames do not traverse that handler chain at all. Using the same primitive as the parent issue, an unauthenticated attacker can send malformed image bytes through the Anthropic Messages API image content parts so that PIL.Image.open raises an UnidentifiedImageError whose message contains the BytesIO object repr, leaking the heap memory address verbatim in the error.message field of the response body. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, ll temperature validation gates use comparison operators (<, >), which silently evaluate to False for NaN and for positive Infinity in Python's IEEE 754 float semantics. Both values pass every guard and propagate to GPU sampling kernels, where they produce undefined behavior or CUDA errors that can crash the inference worker. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.23.1rc0, vLLM's /v1/audio/transcriptions endpoint limits compressed upload size but not decoded PCM output. A 25MB OPUS file expands to ~14.9GB of float32 PCM at decode time. This vulnerability is fixed in 0.23.1rc0.
vLLM is an inference and serving engine for large language models (LLMs). Prior to 0.22.1, the vLLM Dockerfile is vulnerable to a dependency confusion attack through the flashinfer-jit-cache package. The package is installed from a custom index (flashinfer.ai/whl/) using --extra-index-url, but the package name was not registered on PyPI, and UV_INDEX_STRATEGY="unsafe-best-match" is set globally. An attacker who registers flashinfer-jit-cache on PyPI with version 0.6.11.post2 can execute arbitrary code as root during the Docker build and backdoor every resulting container image, enabling exfiltration of all user prompts, API credentials, and model data from production vLLM deployments This vulnerability is fixed in 0.22.1.
vLLM is an inference and serving engine for large language models (LLMs). From 0.5.5 until 0.23.1rc0, integer truncation of tensor dimensions in vLLM's GGUF dequantize kernels (csrc/quantization/gguf/gguf_kernel.cu) causes partial tensor processing. The output tensor is allocated at full size via torch::empty (uninitialized memory), but the dequantize CUDA kernel processes only a truncated number of elements. The unfilled portion of the output tensor retains whatever was previously in GPU memory. In multi-tenant inference deployments, this residual GPU memory may contain tensor data from other users' inference requests, constituting information disclosure. This vulnerability is fixed in 0.23.1rc0.
curl -i -X GET 'https://api.rankiteo.com/underwriter-getcompany-history?
linkedin_id=axa' -H 'apikey: YOUR_API_KEY_HERE'
Every week, Rankiteo analyzes billions of signals to give organizations a sharper, faster view of emerging risks. With deeper, more actionable intelligence at their fingertips, security teams can outpace threat actors, respond instantly to Zero-Day attacks, and dramatically shrink their risk exposure window.
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.