Collaborate Disseminate
It’s not yet very accurate or practical, but under ideal conditions it is possible to figure out the shape of a house key by listening to it being used.
Listen to Your Key: Towards Acoustics-based Physical Key Inference
Abstract: Physical locks are on… Continue reading Determining Key Shape from Sound
Interesting research: “Who Can Find My Devices? Security and Privacy of Apple’s Crowd-Sourced Bluetooth Location Tracking System“:
Abstract: Overnight, Apple has turned its hundreds-of-million-device ecosystem into the world’s largest crowd-sourced location tracking network called offline finding (OF). OF leverages online finder devices to detect the presence of missing offline devices using Bluetooth and report an approximate location back to the owner via the Internet. While OF is not the first system of its kind, it is the first to commit to strong privacy goals. In particular, OF aims to ensure finder anonymity, untrackability of owner devices, and confidentiality of location reports. This paper presents the first comprehensive security and privacy analysis of OF. To this end, we recover the specifications of the closed-source OF protocols by means of reverse engineering. We experimentally show that unauthorized access to the location reports allows for accurate device tracking and retrieving a user’s top locations with an error in the order of 10 meters in urban areas. While we find that OF’s design achieves its privacy goals, we discover two distinct design and implementation flaws that can lead to a location correlation attack and unauthorized access to the location history of the past seven days, which could deanonymize users. Apple has partially addressed the issues following our responsible disclosure. Finally, we make our research artifacts publicly available…
Continue reading Security Analysis of Apple’s “Find My…” Protocol
Really interesting research:
“Exploitation and Sanitization of Hidden Data in PDF Files”
Abstract: Organizations publish and share more and more electronic documents like PDF files. Unfortunately, most organizations are unaware that these documents can compromise sensitive information like authors names, details on the information system and architecture. All these information can be exploited easily by attackers to footprint and later attack an organization. In this paper, we analyze hidden data found in the PDF files published by an organization. We gathered a corpus of 39664 PDF files published by 75 security agencies from 47 countries. We have been able to measure the quality and quantity of information exposed in these PDF files. It can be effectively used to find weak links in an organization: employees who are running outdated software. We have also measured the adoption of PDF files sanitization by security agencies. We identified only 7 security agencies which sanitize few of their PDF files before publishing. Unfortunately, we were still able to find sensitive information within 65% of these sanitized PDF files. Some agencies are using weak sanitization techniques: it requires to remove all the hidden sensitive information from the file and not just to remove the data at the surface. Security agencies need to change their sanitization methods. …
Science has a paper (and commentary) on generating 250 random terabits per second with a laser. I don’t know how cryptographically secure they are, but that can be cleaned up with something like Fortuna.
EDITED TO ADD (3/12): Here are free versio… Continue reading Fast Random Bit Generation
Interesting paper: “Shadow Attacks: Hiding and Replacing Content in Signed PDFs“:
Abstract: Digitally signed PDFs are used in contracts and invoices to guarantee the authenticity and integrity of their content. A user opening a signed PDF expects to see a warning in case of any modification. In 2019, Mladenov et al. revealed various parsing vulnerabilities in PDF viewer implementations.They showed attacks that could modify PDF documents without invalidating the signature. As a consequence, affected vendors of PDF viewers implemented countermeasures preventing all attacks…
Interesting research on persistent web tracking using favicons. (For those who don’t know, favicons are those tiny icons that appear in browser tabs next to the page name.)
Abstract: The privacy threats of online tracking have garnered considerable attention in recent years from researchers and practitioners alike. This has resulted in users becoming more privacy-cautious and browser vendors gradually adopting countermeasures to mitigate certain forms of cookie-based and cookie-less tracking. Nonetheless, the complexity and feature-rich nature of modern browsers often lead to the deployment of seemingly innocuous functionality that can be readily abused by adversaries. In this paper we introduce a novel tracking mechanism that misuses a simple yet ubiquitous browser feature: …
New research:
Pile driving occurs during construction of marine platforms, including offshore windfarms, producing intense sounds that can adversely affect marine animals. We quantified how a commercially and economically important squid (Doryteuthis pealeii: Lesueur 1821) responded to pile driving sounds recorded from a windfarm installation within this species’ habitat. Fifteen-minute portions of these sounds were played to 16 individual squid. A subset of animals (n = 11) received a second exposure after a 24-h rest period. Body pattern changes, inking, jetting, and startle responses were observed and nearly all squid exhibited at least one response. These responses occurred primarily during the first 8 impulses and diminished quickly, indicating potential rapid, short-term habituation. Similar response rates were seen 24-h later, suggesting squid re-sensitized to the noise. Increased tolerance of anti-predatory alarm responses may alter squids’ ability to deter and evade predators. Noise exposure may also disrupt normal intraspecific communication and ecologically relevant responses to sound. …
Continue reading Friday Squid Blogging: Squids Don’t Like Pile-Driving Noises
Researchers have been able to find all sorts of personal information within GPT-2. This information was part of the training data, and can be extracted with the right sorts of queries.
Paper: “Extracting Training Data from Large Language Models.”
Abstract: It has become common to publish large (billion parameter) language models that have been trained on private datasets. This paper demonstrates that in such settings, an adversary can perform a training data extraction attack to recover individual training examples by querying the language model…
Continue reading Extracting Personal Information from Large Language Models Like GPT-2
The microphones on voice assistants are very sensitive, and can snoop on all sorts of data:
In Hey Alexa what did I just type? we show that when sitting up to half a meter away, a voice assistant can still hear the taps you make on your phone, even in… Continue reading Eavesdropping on Phone Taps from Voice Assistants
This is a deep-diving species that “fed on small prey items such as squid.”
Academic paper.
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 he… Continue reading Friday Squid Blogging: Newly Identified Ichthyosaur Species Probably Ate Squid