Author: admin

Criminal network may have made hundreds of millions from scams

The ransomware gang allegedly used its latest encryptor, ‘Black,’ which borrows parts of the late Black Matter group’s encryptor

Cisco did not release updates to address the vulnerabilities and no workarounds address them

Hackers discussed how to use stolen payment cards and bypass geo controls and SMS limitations

The group used Telegram channels, a DDoS payment program, a multi-OS supported toolkit and GitHub

Good advice on buying squid. I like to buy whole fresh squid and clean it myself.

As usual, you can also use this squid post to talk about the security stories in the news that I haven’t covered.

Read my blog posting guidelines here.

Aflac and Zurich reveal major breaches this week

Sanctioned entities accounted for the largest volume

With the release of ChatGPT, I’ve read many random articles about this or that threat from the technology. This paper is a good survey of the field: what the threats are, how we might detect machine-generated text, directions for future research. It’s a solid grounding amongst all of the hype.

Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods

Abstract: Advances in natural language generation (NLG) have resulted in machine generated text that is increasingly difficult to distinguish from human authored text. Powerful open-source models are freely available, and user-friendly tools democratizing access to generative models are proliferating. The great potential of state-of-the-art NLG systems is tempered by the multitude of avenues for abuse. Detection of machine generated text is a key countermeasure for reducing abuse of NLG models, with significant technical challenges and numerous open problems. We provide a survey that includes both 1) an extensive analysis of threat models posed by contemporary NLG systems, and 2) the most complete review of machine generated text detection methods to date. This survey places machine generated text within its cybersecurity and social context, and provides strong guidance for future work addressing the most critical threat models, and ensuring detection systems themselves demonstrate trustworthiness through fairness, robustness, and accountability.

The growth is associated with using HTML attachments as a delivery method in credential phishing