News

Individual accused of draining funds from trading platform Mango Markets

Similar number expect surge in cyber-attacks this year

A BlackBerry survey reveals 51% of security leaders expect ChatGPT to be at the heart of a successful cyber-attack within a year

It’s a bug fix for a bug fix. A memory leak was turned into a double-free that has now been turned into correct code…

A survey of giant squid science.

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.

A Hacker’s Mind will be published on Tuesday.

I have done a written interview and a podcast interview about the book. It’s been chosen as a “February 2023 Must-Read Book” by the Next Big Idea Club. And an “Editor’s Pick”—whatever that means—on Amazon.

There have been three reviews so far. I am hoping for more. And maybe even a published excerpt or two.

Amazon and others will start shipping the book on Tuesday. If you ordered a signed copy from me, it is already in the mail.

If you can leave a review somewhere, I would appreciate it.

Interesting research: “Facial Misrecognition Systems: Simple Weight Manipulations Force DNNs to Err Only on Specific Persons“:

Abstract: In this paper we describe how to plant novel types of backdoors in any facial recognition model based on the popular architecture of deep Siamese neural networks, by mathematically changing a small fraction of its weights (i.e., without using any additional training or optimization). These backdoors force the system to err only on specific persons which are preselected by the attacker. For example, we show how such a backdoored system can take any two images of a particular person and decide that they represent different persons (an anonymity attack), or take any two images of a particular pair of persons and decide that they represent the same person (a confusion attack), with almost no effect on the correctness of its decisions for other persons. Uniquely, we show that multiple backdoors can be independently installed by multiple attackers who may not be aware of each other’s existence with almost no interference.

We have experimentally verified the attacks on a FaceNet-based facial recognition system, which achieves SOTA accuracy on the standard LFW dataset of 99.35%. When we tried to individually anonymize ten celebrities, the network failed to recognize two of their images as being the same person in 96.97% to 98.29% of the time. When we tried to confuse between the extremely different looking Morgan Freeman and Scarlett Johansson, for example, their images were declared to be the same person in 91.51% of the time. For each type of backdoor, we sequentially installed multiple backdoors with minimal effect on the performance of each one (for example, anonymizing all ten celebrities on the same model reduced the success rate for each celebrity by no more than 0.91%). In all of our experiments, the benign accuracy of the network on other persons was degraded by no more than 0.48% (and in most cases, it remained above 99.30%).

It’s a weird attack. On the one hand, the attacker has access to the internals of the facial recognition system. On the other hand, this is a novel attack in that it manipulates internal weights to achieve a specific outcome. Given that we have no idea how those weights work, it’s an important result.

They created pages with malicious links and ultimately conducted credential-harvesting attacks

Affected data includes names, contact details, vehicle details and ID documents, among others

WiCyS is calling for workshop participants to inform its State of Inclusion of Women in Cybersecurity report