A vulnerability classified as problematic was found in jianlinwei cool-php-captcha up to 0.2. This vulnerability affects unknown code of the file example-form.php. The manipulation of the argument captcha with the input %3Cscript%3Ealert(1)%3C/script%3E leads to cross site scripting. The attack can be initiated remotely. The exploit has been disclosed to the public and may be used. Upgrading to version 0.3 is able to address this issue. The name of the patch is c84fb6b153bebaf228feee0cbf50728d27ae3f80. It is recommended to upgrade the affected component. The identifier of this vulnerability is VDB-218296.
European law enforcement agencies have dealt a blow to scammers running call centres across the continent that stole millions of Euros from cryptocurrency investors.
Crime-fighting authorities teamed up to tackle organised criminal groups who tricked unwary members of the public into investing in fake cryptocurrency schemes.
Read more in my article on the Hot for Security blog.
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 Royal ransomware group is believed to be actively exploiting a critical security flaw affecting Citrix systems, according to the cyber research team at cyber insurance provider At-Bay. Announced by Citrix on November 8, 2022, the vulnerability, identified as CVE-2022-27510, allows for the potential bypass of authentication measures on two Citrix products: the Application Delivery Controller (ADC) and Gateway.
