News
Data Of 700 Million LinkedIn Users Is For Sale On The Dark Web
Who would pay for this information, you wonder? Spammers, phishers, and other cybercriminals are definitely the target audience here.
The security team at LinkedIn doesn’t get much rest lately. In April, 500 million of its user’s data was exposed by hackers, and the same data collection technique was apparently used by a dark web user called TomLiner, who is currently selling 700 million LinkedIn user records (92% of all LinkedIn users) in a convenient bundle for just $5,000.
The data collection technique in question is called scraping, which is the act of extracting useful information from a website. Since any public website can be scraped using readily available tools, it wouldn’t be correct to call this incident a breach, as LinkedIn quickly pointed out.
“While we’re still investigating this issue, our initial analysis indicates that the dataset includes information scraped from LinkedIn as well as information obtained from other sources,” said Leonna Spilman, Corporate Communications Manager at LinkedIn. “This was not a LinkedIn data breach, and our investigation has determined that no private LinkedIn member data was exposed.”
So, what data has been exposed? Fortunately, no passwords or dates of birth. Here’s what a sample of one million records published by the scrapper contains:
- Email addresses
- Full names
- Phone numbers
- Physical addresses
- Geolocation records
- LinkedIn username and profile URL
- Personal and professional experience/background
- Genders
- Other social media accounts and usernames
Who would pay $5,000 for this information, you wonder? Spammers, phishers, and other cybercriminals are definitely the target audience here.
Also Read: Is Your Phone Hacked? How To Find Out & Protect Yourself
Having all this information in one place makes it much easier for them to create detailed profiles of their potential victims and launch sophisticated targeted attacks against them. Sure, they could simply scape it by themselves using LinkedIn’s own API (application program interface) just like the seller did, but cybercrime can be so profitable that their time is often more valuable.
If you have a LinkedIn account, then you should assume that your personal information is included in the dataset and act accordingly. More specifically, you should enable multi-factor authentication (MFA) and avoid replying to email messages from unknown senders, let alone opening any attachments they may contain.
News
UAE-Built Falcon-H1 Arabic Leads LLM Benchmarks
The lean Emirati-built language model beats larger global systems and puts Arabic at the center of training.
Abu Dhabi’s Technology Innovation Institute has released an Arabic-first large language model that tops global test boards, an uncommon edge for a region long served by English-centric systems.
Falcon-H1 Arabic comes in 3B, 7B and 34B versions. The flagship posts 75.36% accuracy on comprehensive Arabic tasks and ranks first on the Open Arabic LLM Leaderboard. It also outperforms Meta’s Llama-70B and Alibaba’s Qwen-72B while using less than half their parameters. The smallest model beats Microsoft’s Phi-4 Mini by ten percentage points on equivalent benchmarks.
Arabic remains hard territory for AI. Flexible word order, dense morphology and constant switching between regional dialects and Modern Standard Arabic leave many global models missing context or tone. Academic research has pointed to a shortage of annotated datasets for dialect and informal speech. The impact shows up in classrooms, call centers and government portals where Arabic chatbots lag their English counterparts.
TII trained Falcon-H1 Arabic on formal writing, dialects and culturally grounded content. Beyond scores, it handles practical use: long conversations, reasoning rather than literal translation, and inputs of up to 192,000 words — enough for medical records or legal filings.
“The aim is innovation that is accessible, relevant, and impactful,” said Faisal Al Bannai, Adviser to the UAE President and Secretary-General of the Advanced Technology Research Council.
Also Read: Governata Raises $4M For Saudi AI Data-Governance Push
Arabic is spoken by more than 450 million people across over 20 countries, yet has often been treated as a secondary language for foundation models. The UAE move signals a push to flip that logic and build Arabic-native stacks rather than wait for global systems to improve.
Falcon models have led their categories since 2023. With H1 Arabic, TII is offering free access via chat.falconllm.tii.ae for developers, media, healthcare and public-sector users looking to automate in natural Arabic.
As the region continues to invest in sovereign computing and data localization, the addition of Falcon-H1 Arabic adds a powerful tool built for the native language, instead of an afterthought attached to an English-trained system.
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