News

Roger Grimes on why multifactor authentication isn’t a panacea:

The first time I heard of this issue was from a Midwest CEO. His organization had been hit by ransomware to the tune of $10M. Operationally, they were still recovering nearly a year later. And, embarrassingly, it was his most trusted VP who let the attackers in. It turns out that the VP had approved over 10 different push-based messages for logins that he was not involved in. When the VP was asked why he approved logins for logins he was not actually doing, his response was, “They (IT) told me that I needed to click on Approve when the message appeared!”…

The NSA and CISA have released a document on how to harden your VPN.

The Washington Post reports that the FBI had a decryption key for the REvil ransomware, but didn’t pass it along to victims because it would have disrupted an ongoing operation.

The key was obtained through access to the servers of the Russia-based criminal gang behind the July attack. Deploying it immediately could have helped the victims, including schools and hospitals, avoid what analysts estimate was millions of dollars in recovery costs.

But the FBI held on to the key, with the agreement of other agencies, in part because it was planning to carry out an operation to disrupt the hackers, a group known as REvil, and the bureau did not want to tip them off. Also, a government assessment found the harm was not as severe as initially feared…

Fascinating research: “Generating Master Faces for Dictionary Attacks with a Network-Assisted Latent Space Evolution.”

Abstract: 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…

First California. Then Virginia. Now Colorado.
Here’s a good comparison of the three states’ laws.