Crypto-Gram, January 15, 2024

A monthly newsletter about cybersecurity and related topics.

Crypto-Gram 
January 15, 2024

by Bruce Schneier 
Fellow and Lecturer, Harvard Kennedy School 
schneier@schneier.com 
https://www.schneier.com 

A free monthly newsletter providing summaries, analyses, insights, and
commentaries on security: computer and otherwise.

For back issues, or to subscribe, visit Crypto-Gram's web page.

Read this issue on the web

These same essays and news items appear in the Schneier on Security blog, along
with a lively and intelligent comment section. An RSS feed is available.

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In this issue:

If these links don't work in your email client, try reading this issue of
Crypto-Gram on the web.

A Robot the Size of the World

Police Get Medical Records without a Warrant

OpenAI Is Not Training on Your Dropbox Documents -- Today

GCHQ Christmas Codebreaking Challenge

Cyberattack on UkraineΓÇÖs Kyivstar Seems to Be Russian Hacktivists

Data Exfiltration Using Indirect Prompt Injection

Ben RothkeΓÇÖs Review of A HackerΓÇÖs Mind

Google Stops Collecting Location Data from Maps

New iPhone Security Features to Protect Stolen Devices

AI and Lossy Bottlenecks

AI Is Scarily Good at Guessing the Location of Random Photos

TikTok Editorial Analysis

Facial Recognition Systems in the US

New iPhone Exploit Uses Four Zero-Days

Improving ShorΓÇÖs Algorithm

Second Interdisciplinary Workshop on Reimagining Democracy

PIN-Stealing Android Malware

Facial Scanning by Burger King in Brazil

Pharmacies Giving Patient Records to Police without Warrants

On IoT Devices and Software Liability

Upcoming Speaking Engagements

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A Robot the Size of the World

[2023.12.15] In 2016, I wrote about an Internet that affected the world in a
direct, physical manner. It was connected to your smartphone. It had sensors
like cameras and thermostats. It had actuators: Drones, autonomous cars. And it
had smarts in the middle, using sensor data to figure out what to do and then
actually do it. This was the Internet of Things (IoT).

The classical definition of a robot is something that senses, thinks, and acts
-- thatΓÇÖs todayΓÇÖs Internet. WeΓÇÖve been building a world-sized robot
without even realizing it.

In 2023, we upgraded the ΓÇ£thinkingΓÇ¥ part with large-language models (LLMs)
like GPT. ChatGPT both surprised and amazed the world with its ability to
understand human language and generate credible, on-topic, humanlike responses.
But what these are really good at is interacting with systems formerly designed
for humans. Their accuracy will get better, and they will be used to replace
actual humans.

In 2024, weΓÇÖre going to start connecting those LLMs and other AI systems to
both sensors and actuators. In other words, they will be connected to the larger
world, through APIs. They will receive direct inputs from our environment, in
all the forms I thought about in 2016. And they will increasingly control our
environment, through IoT devices and beyond.

It will start small: Summarizing emails and writing limited responses. Arguing
with customer service -- on chat -- for service changes and refunds. Making
travel reservations.

But these AIs will interact with the physical world as well, first controlling
robots and then having those robots as part of them. Your AI-driven thermostat
will turn the heat and air conditioning on based also on whoΓÇÖs in what room,
their preferences, and where they are likely to go next. It will negotiate with
the power company for the cheapest rates by scheduling usage of high-energy
appliances or car recharging.

This is the easy stuff. The real changes will happen when these AIs group
together in a larger intelligence: A vast network of power generation and power
consumption with each building just a node, like an ant colony or a human army.

Future industrial-control systems will include traditional factory robots, as
well as AI systems to schedule their operation. It will automatically order
supplies, as well as coordinate final product shipping. The AI will manage its
own finances, interacting with other systems in the banking world. It will call
on humans as needed: to repair individual subsystems or to do things too
specialized for the robots.

Consider driverless cars. Individual vehicles have sensors, of course, but they
also make use of sensors embedded in the roads and on poles. The real processing
is done in the cloud, by a centralized system that is piloting all the vehicles.
This allows individual cars to coordinate their movement for more efficiency:
braking in synchronization, for example.

These are robots, but not the sort familiar from movies and television. We think
of robots as discrete metal objects, with sensors and actuators on their
surface, and processing logic inside. But our new robots are different. Their
sensors and actuators are distributed in the environment. Their processing is
somewhere else. TheyΓÇÖre a network of individual units that become a robot only
in aggregate.

This turns our notion of security on its head. If massive, decentralized AIs run
everything, then who controls those AIs matters a lot. ItΓÇÖs as if all the
executive assistants or lawyers in an industry worked for the same agency. An AI
that is both trusted and trustworthy will become a critical requirement.

This future requires us to see ourselves less as individuals, and more as parts
of larger systems. ItΓÇÖs AI as nature, as Gaia -- everything as one system.
ItΓÇÖs a future more aligned with the Buddhist philosophy of interconnectedness
than Western ideas of individuality. (And also with science-fiction dystopias,
like Skynet from the Terminator movies.) It will require a rethinking of much of
our assumptions about governance and economy. ThatΓÇÖs not going to happen soon,
but in 2024 we will see the first steps along that path.

This essay previously appeared in Wired.

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Police Get Medical Records without a Warrant

[2023.12.18] More unconstrained surveillance:

Lawmakers noted the pharmaciesΓÇÖ policies for releasing medical records in a
letter dated Tuesday to the Department of Health and Human Services (HHS)
Secretary Xavier Becerra. The letter -- signed by Sen. Ron Wyden (D-Ore.), Rep.
Pramila Jayapal (D-Wash.), and Rep. Sara Jacobs (D-Calif.) -- said their
investigation pulled information from briefings with eight big prescription drug
suppliers.

They include the seven largest pharmacy chains in the country: CVS Health,
Walgreens Boots Alliance, Cigna, Optum Rx, Walmart Stores, Inc., The Kroger
Company, and Rite Aid Corporation. The lawmakers also spoke with Amazon
Pharmacy.

All eight of the pharmacies said they do not require law enforcement to have a
warrant prior to sharing private and sensitive medical records, which can
include the prescription drugs a person used or uses and their medical
conditions. Instead, all the pharmacies hand over such information with nothing
more than a subpoena, which can be issued by government agencies and does not
require review or approval by a judge.

Three pharmacies -- CVS Health, The Kroger Company, and Rite Aid Corporation --
told lawmakers they didnΓÇÖt even require their pharmacy staff to consult legal
professionals before responding to law enforcement requests at pharmacy
counters. According to the lawmakers, CVS, Kroger, and Rite Aid said that
ΓÇ£their pharmacy staff face extreme pressure to immediately respond to law
enforcement demands and, as such, the companies instruct their staff to process
those requests in store.ΓÇ¥

The rest of the pharmacies -- Amazon, Cigna, Optum Rx, Walmart, and Walgreens
Boots Alliance -- at least require that law enforcement requests be reviewed by
legal professionals before pharmacists respond. But, only Amazon said it had a
policy of notifying customers of law enforcement demands for pharmacy records
unless there were legal prohibitions to doing so, such as a gag order.

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OpenAI Is Not Training on Your Dropbox Documents -- Today

[2023.12.19] ThereΓÇÖs a rumor flying around the Internet that OpenAI is
training foundation models on your Dropbox documents.

HereΓÇÖs CNBC. HereΓÇÖs Boing Boing. Some articles are more nuanced, but
thereΓÇÖs still a lot of confusion.

It seems not to be true. Dropbox isnΓÇÖt sharing all of your documents with
OpenAI. But hereΓÇÖs the problem: we donΓÇÖt trust OpenAI. We donΓÇÖt trust tech
corporations. And -- to be fair -- corporations in general. We have no reason
to.

Simon Willison nails it in a tweet:

ΓÇ£OpenAI are training on every piece of data they see, even when they say they
arenΓÇÖtΓÇ¥ is the new ΓÇ£Facebook are showing you ads based on overhearing
everything you say through your phoneΓÇÖs microphone.ΓÇ¥

Willison expands this in a blog post, which I strongly recommend reading in its
entirety. His point is that these companies have lost our trust:

Trust is really important. Companies lying about what they do with your privacy
is a very serious allegation.

A society where big companies tell blatant lies about how they are handling our
data -- and get away with it without consequences -- is a very unhealthy
society.

A key role of government is to prevent this from happening. If OpenAI are
training on data that they said they wouldnΓÇÖt train on, or if Facebook are
spying on us through our phoneΓÇÖs microphones, they should be hauled in front
of regulators and/or sued into the ground.

If we believe that they are doing this without consequence, and have been
getting away with it for years, our intolerance for corporate misbehavior
becomes a victim as well. We risk letting companies get away with real
misconduct because we incorrectly believed in conspiracy theories.

Privacy is important, and very easily misunderstood. People both overestimate
and underestimate what companies are doing, and whatΓÇÖs possible. This isnΓÇÖt
helped by the fact that AI technology means the scope of whatΓÇÖs possible is
changing at a rate thatΓÇÖs hard to appreciate even if youΓÇÖre deeply aware of
the space.

If we want to protect our privacy, we need to understand whatΓÇÖs going on. More
importantly, we need to be able to trust companies to honestly and clearly
explain what they are doing with our data.

On a personal level we risk losing out on useful tools. How many people
cancelled their Dropbox accounts in the last 48 hours? How many more turned off
that AI toggle, ruling out ever evaluating if those features were useful for
them or not?

And while Dropbox is not sending your data to OpenAI today, it could do so
tomorrow with a simple change of its terms of service. So could your bank, or
credit card company, your phone company, or any other company that owns your
data. Any of the tens of thousands of data brokers could be sending your data to
train AI models right now, without your knowledge or consent. (At least, in the
US. Hooray for the EU and GDPR.)

Or, as Thomas Claburn wrote:

ΓÇ£Your info wonΓÇÖt be harvested for trainingΓÇ¥ is the new ΓÇ£Your private
chatter wonΓÇÖt be used for ads.ΓÇ¥

These foundation models want our data. The corporations that have our data want
the money. ItΓÇÖs only a matter of time, unless we get serious government
privacy regulation.

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GCHQ Christmas Codebreaking Challenge

[2023.12.20] Looks like fun.

Details here.

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Cyberattack on UkraineΓÇÖs Kyivstar Seems to Be Russian Hacktivists

[2023.12.21] The Solntsepek group has taken credit for the attack. TheyΓÇÖre
linked to the Russian military, so itΓÇÖs unclear whether the attack was
government directed or freelance.

This is one of the most significant cyberattacks since Russia invaded in
February 2022.

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Data Exfiltration Using Indirect Prompt Injection

[2023.12.22] Interesting attack on a LLM:

In Writer, users can enter a ChatGPT-like session to edit or create their
documents. In this chat session, the LLM can retrieve information from sources
on the web to assist users in creation of their documents. We show that
attackers can prepare websites that, when a user adds them as a source,
manipulate the LLM into sending private information to the attacker or perform
other malicious activities.

The data theft can include documents the user has uploaded, their chat history
or potentially specific private information the chat model can convince the user
to divulge at the attackerΓÇÖs behest.

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Ben RothkeΓÇÖs Review of A HackerΓÇÖs Mind

[2023.12.22] Ben Rothke chose A HackerΓÇÖs Mind as ΓÇ£the best information
security book of 2023.ΓÇ¥

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Google Stops Collecting Location Data from Maps

[2023.12.26] Google Maps now stores location data locally on your device,
meaning that Google no longer has that data to turn over to the police.

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New iPhone Security Features to Protect Stolen Devices

[2023.12.27] Apple is rolling out a new ΓÇ£Stolen Device ProtectionΓÇ¥ feature
that seems well thought out:

When Stolen Device Protection is turned on, Face ID or Touch ID authentication
is required for additional actions, including viewing passwords or passkeys
stored in iCloud Keychain, applying for a new Apple Card, turning off Lost Mode,
erasing all content and settings, using payment methods saved in Safari, and
more. No passcode fallback is available in the event that the user is unable to
complete Face ID or Touch ID authentication.

For especially sensitive actions, including changing the password of the Apple
ID account associated with the iPhone, the feature adds a security delay on top
of biometric authentication. In these cases, the user must authenticate with
Face ID or Touch ID, wait one hour, and authenticate with Face ID or Touch ID
again. However, Apple said there will be no delay when the iPhone is in familiar
locations, such as at home or work.

More details at the link.

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AI and Lossy Bottlenecks

[2023.12.28] Artificial intelligence is poised to upend much of society,
removing human limitations inherent in many systems. One such limitation is
information and logistical bottlenecks in decision-making.

Traditionally, people have been forced to reduce complex choices to a small
handful of options that donΓÇÖt do justice to their true desires. Artificial
intelligence has the potential to remove that limitation. And it has the
potential to drastically change how democracy functions.

AI researcher Tantum Collins and I, a public-interest technology scholar, call
this AI overcoming ΓÇ£lossy bottlenecks.ΓÇ¥ Lossy is a term from information
theory that refers to imperfect communications channels -- that is, channels
that lose information.

Multiple-choice practicality

Imagine your next sit-down dinner and being able to have a long conversation
with a chef about your meal. You could end up with a bespoke dinner based on
your desires, the chefΓÇÖs abilities and the available ingredients. This is
possible if you are cooking at home or hosted by accommodating friends.

But it is infeasible at your average restaurant: The limitations of the kitchen,
the way supplies have to be ordered and the realities of restaurant cooking make
this kind of rich interaction between diner and chef impossible. You get a menu
of a few dozen standardized options, with the possibility of some modifications
around the edges.

ThatΓÇÖs a lossy bottleneck. Your wants and desires are rich and multifaceted.
The array of culinary outcomes are equally rich and multifaceted. But thereΓÇÖs
no scalable way to connect the two. People are forced to use multiple-choice
systems like menus to simplify decision-making, and they lose so much
information in the process.

People are so used to these bottlenecks that we donΓÇÖt even notice them. And
when we do, we tend to assume they are the inevitable cost of scale and
efficiency. And they are. Or, at least, they were.

The possibilities

Artificial intelligence has the potential to overcome this limitation. By
storing rich representations of peopleΓÇÖs preferences and histories on the
demand side, along with equally rich representations of capabilities, costs and
creative possibilities on the supply side, AI systems enable complex
customization at scale and low cost. Imagine walking into a restaurant and
knowing that the kitchen has already started work on a meal optimized for your
tastes, or being presented with a personalized list of choices.

There have been some early attempts at this. People have used ChatGPT to design
meals based on dietary restrictions and what they have in the fridge. ItΓÇÖs
still early days for these technologies, but once they get working, the
possibilities are nearly endless. Lossy bottlenecks are everywhere.

Take labor markets. Employers look to grades, diplomas and certifications to
gauge candidatesΓÇÖ suitability for roles. These are a very coarse
representation of a job candidateΓÇÖs abilities. An AI system with access to,
for example, a studentΓÇÖs coursework, exams and teacher feedback as well as
detailed information about possible jobs could provide much richer assessments
of which employment matches do and donΓÇÖt make sense.

Or apparel. People with money for tailors and time for fittings can get clothes
made from scratch, but most of us are limited to mass-produced options. AI could
hugely reduce the costs of customization by learning your style, taking
measurements based on photos, generating designs that match your taste and using
available materials. It would then convert your selections into a series of
production instructions and place an order to an AI-enabled robotic production
line.

Or software. TodayΓÇÖs computer programs typically use one-size-fits-all
interfaces, with only minor room for modification, but individuals have widely
varying needs and working styles. AI systems that observe each userΓÇÖs
interaction styles and know what that person wants out of a given piece of
software could take this personalization far deeper, completely redesigning
interfaces to suit individual needs.

Removing democracyΓÇÖs bottleneck

These examples are all transformative, but the lossy bottleneck that has the
largest effect on society is in politics. ItΓÇÖs the same problem as the
restaurant. As a complicated citizen, your policy positions are probably
nuanced, trading off between different options and their effects. You care about
some issues more than others and some implementations more than others.

If you had the knowledge and time, you could engage in the deliberative process
and help create better laws than exist today. But you donΓÇÖt. And, anyway,
society canΓÇÖt hold policy debates involving hundreds of millions of people. So
you go to the ballot box and choose between two -- or if you are lucky, four or
five -- individual representatives or political parties.

Imagine a system where AI removes this lossy bottleneck. Instead of trying to
cram your preferences to fit into the available options, imagine conveying your
political preferences in detail to an AI system that would directly advocate for
specific policies on your behalf. This could revolutionize democracy.

One way is by enhancing voter representation. By capturing the nuances of each
individualΓÇÖs political preferences in a way that traditional voting systems
canΓÇÖt, this system could lead to policies that better reflect the desires of
the electorate. For example, you could have an AI device in your pocket -- your
future phone, for instance -- that knows your views and wishes and continually
votes in your name on an otherwise overwhelming number of issues large and
small.

Combined with AI systems that personalize political education, it could
encourage more people to participate in the democratic process and increase
political engagement. And it could eliminate the problems stemming from elected
representatives who reflect only the views of the majority that elected them --
and sometimes not even them.

On the other hand, the privacy concerns resulting from allowing an AI such
intimate access to personal data are considerable. And itΓÇÖs important to avoid
the pitfall of just allowing the AIs to figure out what to do: Human
deliberation is crucial to a functioning democracy.

Also, there is no clear transition path from the representative democracies of
today to these AI-enhanced direct democracies of tomorrow. And, of course, this
is still science fiction.

First steps

These technologies are likely to be used first in other, less politically
charged, domains. Recommendation systems for digital media have steadily reduced
their reliance on traditional intermediaries. Radio stations are like menu
items: Regardless of how nuanced your taste in music is, you have to pick from a
handful of options. Early digital platforms were only a little better: ΓÇ£This
person likes jazz, so weΓÇÖll suggest more jazz.ΓÇ¥

TodayΓÇÖs streaming platforms use listener histories and a broad set of features
describing each track to provide each user with personalized music
recommendations. Similar systems suggest academic papers with far greater
granularity than a subscription to a given journal, and movies based on more
nuanced analysis than simply deferring to genres.

A world without artificial bottlenecks comes with risks -- loss of jobs in the
bottlenecks, for example -- but it also has the potential to free people from
the straitjackets that have long constrained large-scale human decision-making.
In some cases -- restaurants, for example -- the impact on most people might be
minor. But in others, like politics and hiring, the effects could be profound.

This essay originally appeared in The Conversation.

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AI Is Scarily Good at Guessing the Location of Random Photos

[2023.12.29] Wow:

To test PIGEONΓÇÖs performance, I gave it five personal photos from a trip I
took across America years ago, none of which have been published online. Some
photos were snapped in cities, but a few were taken in places nowhere near roads
or other easily recognizable landmarks.

That didnΓÇÖt seem to matter much.

It guessed a campsite in Yellowstone to within around 35 miles of the actual
location. The program placed another photo, taken on a street in San Francisco,
to within a few city blocks.

Not every photo was an easy match: The program mistakenly linked one photo taken
on the front range of Wyoming to a spot along the front range of Colorado, more
than a hundred miles away. And it guessed that a picture of the Snake River
Canyon in Idaho was of the Kawarau Gorge in New Zealand (in fairness, the two
landscapes look remarkably similar).

This kind of thing will likely get better. And even if it is not perfect, it has
some pretty profound privacy implications (but so did geolocation in the EXIF
data that accompanies digital photos).

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TikTok Editorial Analysis

[2024.01.02] TikTok seems to be skewing things in the interests of the Chinese
Communist Party. (This is a serious analysis, and the methodology looks sound.)

Conclusion: Substantial Differences in Hashtag Ratios Raise

Concerns about TikTokΓÇÖs Impartiality

Given the research above, we assess a strong possibility that content on TikTok
is either amplified or suppressed based on its alignment with the interests of
the Chinese Government. Future research should aim towards a more comprehensive
analysis to determine the potential influence of TikTok on popular public
narratives. This research should determine if and how TikTok might be utilized
for furthering national/regional or international objectives of the Chinese
Government.

EDITED TO ADD (1/13): Blog readers have complaints about the methodology.

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Facial Recognition Systems in the US

[2024.01.03] A helpful summary of which US retail stores are using facial
recognition, thinking about using it, or currently not planning on using it.
(This, of course, can all change without notice.)

Three years ago, I wrote that campaigns to ban facial recognition are too
narrow. The problem here is identification, correlation, and then
discrimination. ThereΓÇÖs no difference whether the identification technology is
facial recognition, the MAC address of our phones, gait recognition, license
plate recognition, or anything else. Facial recognition is just the easiest
technology right now.

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New iPhone Exploit Uses Four Zero-Days

[2024.01.04] Kaspersky researchers are detailing ΓÇ£an attack that over four
years backdoored dozens if not thousands of iPhones, many of which belonged to
employees of Moscow-based security firm Kaspersky.ΓÇ¥ ItΓÇÖs a zero-click
exploit that makes use of four iPhone zero-days.

The most intriguing new detail is the targeting of the heretofore-unknown
hardware feature, which proved to be pivotal to the Operation Triangulation
campaign. A zero-day in the feature allowed the attackers to bypass advanced
hardware-based memory protections designed to safeguard device system integrity
even after an attacker gained the ability to tamper with memory of the
underlying kernel. On most other platforms, once attackers successfully exploit
a kernel vulnerability they have full control of the compromised system.

On Apple devices equipped with these protections, such attackers are still
unable to perform key post-exploitation techniques such as injecting malicious
code into other processes, or modifying kernel code or sensitive kernel data.
This powerful protection was bypassed by exploiting a vulnerability in the
secret function. The protection, which has rarely been defeated in exploits
found to date, is also present in AppleΓÇÖs M1 and M2 CPUs.

The details are staggering:

Here is a quick rundown of this 0-click iMessage attack, which used four
zero-days and was designed to work on iOS versions up to iOS 16.2.

Attackers send a malicious iMessage attachment, which the application processes
without showing any signs to the user. 

This attachment exploits the remote code execution vulnerability CVE-2023-41990
in the undocumented, Apple-only ADJUST TrueType font instruction. This
instruction had existed since the early nineties before a patch removed it. 

It uses return/jump oriented programming and multiple stages written in the
NSExpression/NSPredicate query language, patching the JavaScriptCore library
environment to execute a privilege escalation exploit written in JavaScript. 

This JavaScript exploit is obfuscated to make it completely unreadable and to
minimize its size. Still, it has around 11,000 lines of code, which are mainly
dedicated to JavaScriptCore and kernel memory parsing and manipulation. 

It exploits the JavaScriptCore debugging feature DollarVM ($vm) to gain the
ability to manipulate JavaScriptCoreΓÇÖs memory from the script and execute
native API functions. 

It was designed to support both old and new iPhones and included a Pointer
Authentication Code (PAC) bypass for exploitation of recent models. 

It uses the integer overflow vulnerability CVE-2023-32434 in XNUΓÇÖs memory
mapping syscalls (mach_make_memory_entry and vm_map) to obtain read/write access
to the entire physical memory of the device at user level. 

It uses hardware memory-mapped I/O (MMIO) registers to bypass the Page
Protection Layer (PPL). This was mitigated as CVE-2023-38606. 

After exploiting all the vulnerabilities, the JavaScript exploit can do whatever
it wants to the device including running spyware, but the attackers chose to:
(a) launch the IMAgent process and inject a payload that clears the exploitation
artefacts from the device; (b) run a Safari process in invisible mode and
forward it to a web page with the next stage. 

The web page has a script that verifies the victim and, if the checks pass,
receives the next stage: the Safari exploit. 

The Safari exploit uses CVE-2023-32435 to execute a shellcode. 

The shellcode executes another kernel exploit in the form of a Mach object file.
It uses the same vulnerabilities: CVE-2023-32434 and CVE-2023-38606. It is also
massive in terms of size and functionality, but completely different from the
kernel exploit written in JavaScript. Certain parts related to exploitation of
the above-mentioned vulnerabilities are all that the two share. Still, most of
its code is also dedicated to parsing and manipulation of the kernel memory. It
contains various post-exploitation utilities, which are mostly unused. 

The exploit obtains root privileges and proceeds to execute other stages, which
load spyware. We covered these stages in our previous posts.

This is nation-state stuff, absolutely crazy in its sophistication. Kaspersky
discovered it, so thereΓÇÖs no speculation as to the attacker.

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Improving ShorΓÇÖs Algorithm

[2024.01.05] We donΓÇÖt have a useful quantum computer yet, but we do have
quantum algorithms. ShorΓÇÖs algorithm has the potential to factor large numbers
faster than otherwise possible, which -- if the run times are actually feasible
-- could break both the RSA and Diffie-Hellman public-key algorithms.

Now, computer scientist Oded Regev has a significant speed-up to ShorΓÇÖs
algorithm, at the cost of more storage.

Details are in this article. HereΓÇÖs the result:

The improvement was profound. The number of elementary logical steps in the
quantum part of RegevΓÇÖs algorithm is proportional to n1.5 when factoring an
n-bit number, rather than n2 as in ShorΓÇÖs algorithm. The algorithm repeats
that quantum part a few dozen times and combines the results to map out a
high-dimensional lattice, from which it can deduce the period and factor the
number. So the algorithm as a whole may not run faster, but speeding up the
quantum part by reducing the number of required steps could make it easier to
put it into practice.

Of course, the time it takes to run a quantum algorithm is just one of several
considerations. Equally important is the number of qubits required, which is
analogous to the memory required to store intermediate values during an ordinary
classical computation. The number of qubits that ShorΓÇÖs algorithm requires to
factor an n-bit number is proportional to n, while RegevΓÇÖs algorithm in its
original form requires a number of qubits proportional to n1.5 -- a big
difference for 2,048-bit numbers.

Again, this is all still theoretical. But now itΓÇÖs theoretically faster.

Oded RegevΓÇÖs paper.

This is me from 2018 on the potential for quantum cryptanalysis. I still believe
now what I wrote then.

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Second Interdisciplinary Workshop on Reimagining Democracy

[2024.01.08] Last month, I convened the Second Interdisciplinary Workshop on
Reimagining Democracy (IWORD 2023) at the Harvard Kennedy School Ash Center. As
with IWORD 2022, the goal was to bring together a diverse set of thinkers and
practitioners to talk about how democracy might be reimagined for the
twenty-first century.

My thinking is very broad here. Modern democracy was invented in the
mid-eighteenth century, using mid-eighteenth-century technology. Were democracy
to be invented from scratch today, with todayΓÇÖs technologies, it would look
very different. Representation would look different. Adjudication would look
different. Resource allocation and reallocation would look different. Everything
would look different, because we would have much more powerful technology to
build on and no legacy systems to worry about.

Such speculation is not realistic, of course, but itΓÇÖs still valuable.
Everyone seems to be talking about ways to reform our existing systems. ThatΓÇÖs
critically important, but itΓÇÖs also myopic. It represents a hill-climbing
strategy of continuous improvements. We also need to think about discontinuous
changes that you canΓÇÖt easily get to from here; otherwise, weΓÇÖll be forever
stuck at local maxima.

I wrote about the philosophy more in this essay about IWORD 2022. IWORD 2023 was
equally fantastic, easily the most intellectually stimulating two days of my
year. The event is like that; the format results in a firehose of interesting.

Summaries of all the talks are in the first set of comments below. (You can read
a similar summary of IWORD 2022 here.) Thank you to the Ash Center and the
Belfer Center at Harvard Kennedy School, and the Knight Foundation, for the
funding to make this possible.

Next year, I hope to take the workshop out of Harvard and somewhere else. I
would like it to live on for as long as it is valuable.

Now, I really want to explain the format in detail, because it works so well.

I used a workshop format I and others invented for another interdisciplinary
workshop: Security and Human Behavior, or SHB. ItΓÇÖs a two-day event. Each day
has four ninety-minute panels. Each panel has six speakers, each of whom
presents for ten minutes. Then there are thirty minutes of questions and
comments from the audience. Breaks and meals round out the day.

The workshop is limited to forty-eight attendees, which means that everyone is
on a panel. This is important: every attendee is a speaker. And attendees commit
to being there for the whole workshop; no giving your talk and then leaving.
This makes for a very collaborative environment. The short presentations means
that no one can get too deep into details or jargon. This is important for an
interdisciplinary event. Everyone is interesting for ten minutes.

The final piece of the workshop is the social events. We have a night-before
opening reception, a conference dinner after the first day, and a final closing
reception after the second day. Good food is essential.

Honestly, itΓÇÖs great but itΓÇÖs also itΓÇÖs exhausting. Everybody is
interesting for ten minutes. ThereΓÇÖs no down time to zone out or check email.
And even though a shorter event would be easier to deal with, the numbers all
fit together in a way thatΓÇÖs hard to change. A one-day event means only
twenty-four attendees/speakers, and thatΓÇÖs not a critical mass. More people
per panel doesnΓÇÖt work. Not everyone speaking creates a speaker/audience
hierarchy, which I want to avoid. And a three-day, slower-paced event is too
long. IΓÇÖve thought about it long and hard; the format IΓÇÖm using is optimal.

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PIN-Stealing Android Malware

[2024.01.09] This is an old piece of malware -- the Chameleon Android banking
Trojan -- that now disables biometric authentication in order to steal the PIN:

The second notable new feature is the ability to interrupt biometric operations
on the device, like fingerprint and face unlock, by using the Accessibility
service to force a fallback to PIN or password authentication.

The malware captures any PINs and passwords the victim enters to unlock their
device and can later use them to unlock the device at will to perform malicious
activities hidden from view.

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Facial Scanning by Burger King in Brazil

[2024.01.10] In 2000, I wrote: ΓÇ£If McDonaldΓÇÖs offered three free Big Macs
for a DNA sample, there would be lines around the block.ΓÇ¥

Burger King in Brazil is almost there, offering discounts in exchange for a
facial scan. From a marketing video:

ΓÇ£At the end of the year, itΓÇÖs Friday every day, and the hangover kicks
in,ΓÇ¥ a vaguely robotic voice says as images of cheeseburgers glitch in and out
over fake computer code. ΓÇ£BK presents Hangover Whopper, a technology that
scans your hangover level and offers a discount on the ideal combo to help
combat it.ΓÇ¥ The stunt runs until January 2nd.

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Pharmacies Giving Patient Records to Police without Warrants

[2024.01.11] Add pharmacies to the list of industries that are giving private
data to the police without a warrant.

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On IoT Devices and Software Liability

[2024.01.12] New law journal article:

Smart Device Manufacturer Liability and Redress for Third-Party Cyberattack
Victims

Abstract: Smart devices are used to facilitate cyberattacks against both their
users and third parties. While users are generally able to seek redress
following a cyberattack via data protection legislation, there is no equivalent
pathway available to third-party victims who suffer harm at the hands of a
cyberattacker. Given how these cyberattacks are usually conducted by exploiting
a publicly known and yet un-remediated bug in the smart deviceΓÇÖs code, this
lacuna is unreasonable. This paper scrutinises recent judgments from both the
Supreme Court of the United Kingdom and the Supreme Court of the Republic of
Ireland to ascertain whether these rulings pave the way for third-party victims
to pursue negligence claims against the manufacturers of smart devices. From
this analysis, a narrow pathway, which outlines how given a limited set of
circumstances, a duty of care can be established between the third-party victim
and the manufacturer of the smart device is proposed.

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Upcoming Speaking Engagements

[2024.01.14] This is a current list of where and when I am scheduled to speak:

IΓÇÖm speaking at the International PolCampaigns Expo (IPE24) in Cape Town,
South Africa, January 25-26, 2024.

The list is maintained on this page.

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Bruce Schneier is an internationally renowned security technologist, called a
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Inrupt, Inc.

Copyright © 2024 by Bruce Schneier.

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 * Origin: High Portable Tosser at my node (618:500/14)