Robin’s Newsletter #401

22 February 2026. Volume 9, Issue 8
UK gov cyber campgin. Copilot ignored email DLP flags. Don't use LLMs to generate passwords.
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This week

Need to Know, 22nd February 2026

  • UK gov campaign pushes better cyber security; says avg incident costs £195K
  • Copilot ignored DLP labels when processing Outlook emails
  • Don’t use LLMs to generate passwords

Interesting stats

65% of 750 incident response cases worked by Palo Alto’s Unit 42 in the year to September 2025 stemmed from identity, with the primary initial access being obtained via: 33% social engineering,  13% previously compromised /reused credentials,  8% brute force,  8% insider abuse of legitimate creds, and  2% IAM misconfiguration.

Arctic Wolf has also published details of their IR case load between November 2024 and November 2025 (PDF): 44% were ransomware attacks,  26% were business email compromise, 11% related to vulnerability exploitation, and interestingly, in  22% of cases, attackers only threatened to expose information, rather than encrypt it.

1.75 million apps rejected from being published on the Google Play Store in 2025, down from  2.36 million in 2024, last year also saw   80,000 ‘bad’ developer accounts banned. 

700 ATM jackpotting incidents were reported to the FBI in 2025, out of  1,900 since 2020 (~1/3).

Five Three things

  1. The UK government is encouraging organisations to ‘lock the door’ on cyber criminals. The new campaign follows a longitudinal study that founds 82% of businesses and 77% of charities experienced some form of cyber incident in the past year, costing an estimated £14.7 billion. Significant attacks cost an average of £195,000, and the UK’s Cyber Essentials scheme gets a shout out as 92% fewer insurance claims came from certified organisations. A free online readiness check and 30-minute consultations with NCSC-approved firms are on offer to help companies get started.

  2. Microsoft has fixed an issue with Copilot where Copilot Chat for Outlook would summarise emails flagged with “confidential” Data Loss Prevention policies prohibiting their use, if the messages were in the user’s Drafts or Sent folders. While a Microsoft spokesperson told BleepingComputer that “This did not provide anyone access to information they weren’t already authorized to see,” it is a pretty major design flaw. Especially when you consider that, in many cases, copies of emails flagged confidential would end up in the sent items simply by replying to them.

  3. It’s hard to be random: Researchers at Irregular have tested different artificial intelligence chatbots and found them terrible at suggesting strong passwords. Claude, ChatGPT, and Gemini all returned what, on the surface, appear to be strong passwords when prompted to generate 16-character passwords that include lowercase and uppercase letters, numbers, and special characters. However, over 50 repeated prompts, Claude returned 20 duplicates, with the same one string being returned 18 times. All three chatbots generated passwords following patterns that reduce randomness in the results, potentially allowing attackers to fine-tune how they might brute-force or guess them. Entropy-wise, the results are estimated to be between 20 bits and 27 bits, where 98 bits is recognised as being the standard for a ‘truly random’ password. Chatbots are just prediction machines — their job is to predict the next most probable character — and so it’s no wonder they return the same or similar results. They are a particularly poor choice for generating passwords. Use a proper password manager.

In brief

And finally

  • Dutch dude detained during data deletion dispute: A ’technical error’ resulted in the 40-year-old man being sent a link that allowed him to download the sensitive law enforcement information, instead of uploading his own images relating to a police investigation. The man said he would only delete the files if he “received something in return”. That confidence in extorting the police appears misplaced, as he was promptly arrested. Golf. Clap.
Robin
  United Kingdom Large Language Models (LLMs) Microsoft Artificial Intelligence (AI)