Researchers found that tools like OpenAI’s GPT-3 helped craft devilishly effective spearphishing messages.
NATURAL LANGUAGE PROCESSING continues to find its way into unexpected corners. This time, it’s phishing emails. In a small study, researchers found that they could use the deep learning language model GPT-3, along with other AI-as-a-service platforms, to significantly lower the barrier to entry for crafting spearphishing campaigns at a massive scale.
Researchers have long debated whether it would be worth the effort for scammers to train machine learning algorithms that could then generate compelling phishing messages. Mass phishing messages are simple and formulaic, after all, and are already highly effective. Highly targeted and tailored “spearphishing” messages are more labor intensive to compose, though. That’s where NLP may come in surprisingly handy.
At the Black Hat and Defcon security conferences in Las Vegas this week, a team from Singapore’s Government Technology Agency presented a recent experiment in which they sent targeted phishing emails they crafted themselves and others generated by an AI-as-a-service platform to 200 of their colleagues. Both messages contained links that were not actually malicious but simply reported back clickthrough rates to the researchers. They were surprised to find that more people clicked the links in the AI-generated messages than the human-written ones—by a significant margin. Read More
Daily Archives: August 9, 2021
How to Become Data Scientist – A Complete Roadmap
Why CAPTCHA Pictures Are So Unbearably Depressing
I hate doing Google’s CAPTCHAs.
Part of it is the sheer hassle of repeatedly identifying objects — traffic lights, staircases, palm trees and buses — just so I can finish a web search. I also don’t like being forced to donate free labor to AI companies to help train their visual-recognition systems.
But a while ago, while numbly clicking on grainy images of fire hydrants, I was struck by another reason:
The images are deeply, overwhelmingly depressing. Read More
Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution
A master face is a face image that passes face based identity-authentication for a large portion of the population. These faces can be used to impersonate, with a high probability of success, any user, without having access to
any user-information. We optimize these faces, by using an evolutionary algorithm in the latent embedding space of the StyleGAN face generator. Multiple evolutionary strategies are compared, and we propose a novel approach that employs a neural network in order to direct the search in the direction of promising samples, without adding fitness evaluations. The results we present demonstrate that it is possible to obtain a high coverage of the population (over 40%) with less than 10 master faces, for three leading deep face recognition systems. Read More
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