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*preroll music*

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Herald: Our next speaker for today is a
computer science PhD student at UC Santa

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Barbara. He is a member of the Shellfish
Hacking Team and he's also the organizer

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of the IECTF Hacking Competition. Please
give a big round of applause to Nilo

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Redini.

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*applause*

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Nilo: Thanks for the introduction, hello
to everyone. My name is Nilo, and today

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I'm going to present you my work Koronte:
identifying multi-binary vulnerabilities

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in embedded firmware at scale. This work
is a co-joint effort between me and

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several of my colleagues at University of
Santa Barbara and ASU. This talk is going

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to be about IoT devices. So before
starting, let's see an overview about IoT

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devices. IoT devices are everywhere. As
the research suggests, they will reach the

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20 billion units by the end of the next
year. And a recent study conducted this

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year in 2019 on 16 million households
showed that more than 70 percent of homes

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in North America already have an IoT
network connected device. IoT devices make

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everyday life smarter. You can literally
say "Alexa, I'm cold" and Alexa will

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interact with the thermostat and increase
the temperature of your room. Usually the

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way we interact with the IoT devices is
through our smartphone. We send a request

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to the local network, to some device,
router or door lock, or we might send the

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same request through a cloud endpoint,
which is usually managed by the vendor of

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the IoT device. Another way is through the
IoT hubs, smartphone will send the request

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to some IoT hub, which in turn will send
the request to some other IoT devices. As

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you can imagine, IoT devices use and
collect our data and some data is more

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sensitive than other. For instance, think
of all the data that is collected by my

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lightbulb or data that is collected by our
security camera. As such, IoT devices can

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compromise people's safety and privacy.
Things, for example, about the security

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implication of a faulty smartlock or the
brakes of your smart car. So the question

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that we asked is: Are IoT devices secure?
Well, like everything else, they are not.

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OK, in 2016 the Mirai botnet compromised
and leveraged millions of IoT devices to

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disrupt core Internet services such as
Twitter, GitHub and Netflix. And in 2018,

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154 vulnerabilities affecting IoT devices
were published, which represented an

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increment of 15% compared to 2017 and an
increase of 115% compared to 2016. So then

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we wonder: So why is it hard to secure IoT
devices? To answer this question we have

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to look up how IoT devices work and they
are made. Usually when you remove all the

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plastic and peripherals IoT devices look
like this. A board with some chips laying

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on it. Usually you can find the big chip,
the microcontroller which runs the

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firmware and one or more peripheral
controllers which interact with external

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peripherals such as the motor of, your
smart lock or cameras. Though the design

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is generic, implementations are very
diverse. For instance, firmware may run on

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several different architectures such as
ARM, MIPS, x86, PowerPC and so forth. And

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sometimes they are even proprietary, which
means that if a security analyst wants to

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understand what's going on in the
firmware, he'll have a hard time if he

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doesn't have the vendor specifics. Also,
they're operating in environments with

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limited resources, which means that they
run small and optimized code. For

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instance, vendors might implement their
own version of some known algorithm in an

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optimized way. Also, IoT devices manage
external peripherals that often use custom

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code. Again, with peripherals we mean like
cameras, sensors and so forth. The

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firmware of IoT devices can be either
Linux based or a blob firmware, Linux

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based are by far the most common. A study
showed that 86% of firmware are based on

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Linux and on the other hand, blobs
firmware are usually operating systems and

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user applications packaged in a single
binary. In any case, firmware samples are

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usually made of multiple components. For
instance, let's say that you have your

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smart phone and you send a request to your
IoT device. This request will be received

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by a binary which we term as body binary,
which in this example is an webserver. The

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request will be received, parsed, and then
it might be sent to another binary code,

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the handler binary, which will take the
request, work on it, produce an answer,

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send it back to the webserver, which in
turn would produce a response to send to

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the smartphone. So to come back to the
question why is it hard to secure IoT

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devices? Well, the answer is because IoT
devices are in practice very diverse. Of

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course, there have been various work that
have been proposed to analyze and secure

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firmware for IoT devices. Some of them
using static analysis. Others using

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dynamic analysis and several others using
a combination of both. Here I wrote

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several of them. Again at the end of the
presentation there is a bibliography with

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the title of these works. Of course, all
these approaches have some problems. For

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instance, the current dynamic analysis are
hard to apply to scale because of the

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customized environments that IoT devices
work on. Usually when you try to

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dynamically execute a firmware, it's gonna
check if the peripherals are connected and

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are working properly. In a case where you
can't have the peripherals, it's gonna be

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hard to actually run the firmware. Also
current static analysis approaches are

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based on what we call the single binary
approach, which means that binaries from a

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firmware are taken individually and
analysed. This approach might produce many

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false positives. For instance, so let's
say again that we have our two binaries.

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This is actually an example that we found
on one firmware, so the web server will

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take the user request, will parse the
request and produce some data, will set

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this data to an environment variable and
eventually will execute the handle binary.

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Now, if you see the parsing function
contains a string compare which checks if

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some keyword is present in the request.
And if so, it just returns the whole

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request. Otherwise, it will constrain the
size of the request to 128 bytes and

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return it. The handler binary in turn when
spawned will receive the data by doing a

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getenv on the query string, but also will
getenv on another environment variable

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which in this case is not user controlled
and they user cannot influence the content

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of this variable. Then it's gonna call
function process_request. This function

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eventually will do two string copies. One
from the user data, the other one from the

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log path on two different local variables
that are 128 bytes long. Now in the first

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case, as we have seen before, the data can
be greater than 128 bytes and this string

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copy may result in a bug. While in the
second case it will not. Because here we

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assume that the system handles its own
data in a good manner. So throughout this

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work, we're gonna call the first type of
binary, the setter binary, which means

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that it is the binary that takes the data
and set the data for another binary to be

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consumed. And the second type of binary we
called them the getter binary. So the

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current bug finding tools are inadequate
because other bugs are left undiscovered

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if the analysis only consider those
binaries that received network requests or

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they're likely to produce many false
positives if the analysis considers all of

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them individually. So then we wonder how
these different components actually

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communicate. They communicate through what
are called interprocess communication,

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which basically it's a finite set of
paradigms used by binaries to communicate

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such as files, environment variables, MMIO
and so forth. All these pieces are

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represented by data keys, which are file
names, or in the case of the example

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before here on the right, it's the query
string environment variable. Each binary

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that relies on some shared data must know
the endpoint where such data will be

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available, for instance, again, like a
file name or like even a socket endpoint

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or the environment variable. This means
that usually, data keys are coded in the

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program itself, as we saw before. To find
bugs in firmware, in a precise manner, we

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need to track how user data is introduced
and propagated across the different

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binaries. Okay, let's talk about our work.
Before you start talking about Karonte, we

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define our threat model. We hypotesized
that attacker sends arbitrary requests

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over the network, both LAN and WAN
directly to the IoT device. Though we said

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before that sometimes IoT device can
communicate through the clouds, research

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showed that some form of local
communication is usually available, for

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instance, during the setup phase of the
device. Karonte is defined as a static

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analysis tool that tracks data flow across
multiple binaries, to find

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vulnerabilities. Let's see how it works.
So the first step, Karonte find those

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binaries that introduce the user input
into the firmware. We call these border

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binaries, which are the binaries, that
basically interface the device to the

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outside world. Which in the example is our
web server. Then it tracks how a data is

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shared with other binaries within the
firmware sample. Which we'll understand in

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this example, the web server communicates
with the handle binary, and builds what we

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call the BDG. BDG which stands for binary
dependency graph. It's basically a graph

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representation of the data dependencies
among different binaries. Then we detect

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vulnerabilities that arise from the misuse
of the data using the BDG. This is an

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overview of our system. We start by taking
a packed firmware, we unpack it. We find

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the border binaries. Then we build the
binary dependency graph, which relies on a

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set of CPFs, as we will see soon. CPF
stands for Communication Paradigm Finder.

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Then we find the specifics of the
communication, for instance, like the

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constraints applied to the data that is
shared through our module multi-binary

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data-flow analysis. Eventually we run our
insecure interaction detection module,

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which basically takes all the information
and produces alerts. Our system is

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completely static and relies on our static
taint engine. So let's see each one of

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these steps, more in details. The
unpacking procedure is pretty easy, we use

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the off-the-shelf firmware unpacking tool
binwalk. And then we have to find the

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border binaries. Now we see that border
binaries basically are binaries that

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receive data from the network. And we
hypotesize that will contain parsers to

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validate the data that they received. So
in order to find them, we have to find

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parsers which accept data from network and
parse this data. To find parsers we rely

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on related work, which basically uses a
few metrics and define through a number

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the likelihood for a function to contain
parsing capabilities. These metrics that

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we used are number of basic blocks, number
of memory comparison operations and number

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of branches. Now while these define
parsers, we also have to find if a binary

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takes data from the network. As such, we
define two more metrics. The first one, we

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check if binary contains any network
related keywords as SOAP, http and so

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forth. And then we check if there exists a
data flow between read from socket and a

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memory comparison operation. Once for each
function, we got all these metrics, we

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compute what is called a parsing score,
which basically is just a sum of products.

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Once we got a parsing score for each
function in a binary, we represent the

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binary with its highest parsing score.
Once we got that for each binary in the

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firmware we cluster them using the DBSCAN
density based algorithm and consider the

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cluster with the highest parsing score as
containing the set of border binaries.

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After this, we build the binary dependency
graph. Again the binary dependency graph

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represents the data dependency among the
binaries in a firmware sample. For

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instance, this simple graph will tell us
that a binary A communicates with binary C

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using files and the same binary A
communicates with another binary B using

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environment variables. Let's see how this
works. So we start from the identified

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border binaries and then we taint the data
compared against network related keywords

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that we found and run a static analysis,
static taint analysis to detect whether

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the binary relies on any IPC paradigm to
share the data. If we find that it does,

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we establish if the binary is a setter or
a getter, which again means that if the

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binary is setting the data to be consumed
by another binary, or if the binary

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actually gets the data and consumes it.
Then we retrieve the employed data key

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which in the example before was the
keyword QUERY_STRING. And finally we scan

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the firmware sample to find other binaries
that may rely on the same data keys and

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schedule them for further analysis. To
understand whether a binary relies on any

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IPC, we use what we call CPFs, which again
means communication paradigm finder. We

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design a CPF for each IPC. And the CPFs
are also used to find the same data keys

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within the firmware sample. We also
provide Karonte with a generic CPF to

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cover those cases where the IPC is
unknown. Or those cases were the vendor

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implemented their own versions of some
IPC. So for example they don't use the

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setenv. But they implemented their own
setenv. The idea behind this generic CPF

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that we call the semantic CPF is that data
keys has to be used as index to set, or to

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get some data in this simple example. So
let's see how the BDG algorithm works. We

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start from the body binary, which again
will start from the server request and

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will pass the URI and we see that here. it
runs a string comparison against some

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network related keyword. As such, we taint
the variable P. And we see that the

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variable P is returned from the function
to these two different points. As such, we

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continue. And now we see that data gets
tainted and the variable data, it's passed

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to the function setenv. At this point, the
environment CPF will understand that

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tainted data is passed, is set to an
environment variable and will understand

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that this binary is indeed the setter
binary that uses the environment. Then we

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retrieve the data key QUERY_STRING and
we'll search within the firmware sample

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all the other binaries that rely on the
same data key. And it will find that this

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binary relies on the same data key and
will schedule this for further analysis.

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After this algorithm we build the BDG by
creating edges between setters and getters

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for each data key. The multi binary data
flow analysis uses the BDG to find and

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propagate the data constraints from a
setter to a getter. Now, through this we

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apply only the least three constraints,
which means that ideally between two

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program points, there might be an infinite
number of parts and ideally in theory an

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infinite amount of constraints that we can
propagate to the setter binary to the

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getter binary. But since our goal here is
to find bugs, we only propagate the least

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strict set of constraints. Let's see an
example. So again, we have our two

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binaries and we see that the variable that
is passed to the setenv function is data,

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which comes from two different parts from
the parse URI function. In the first case,

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the data that its passed is unconstrained
one in the second case, a line 8 is

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constrained to be at most 128 bytes. As
such, we only propagate the constraints of

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the first guy. In turn, the getter binary
will retrieve this variable from the

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environment and set the variable query.
Oh, sorry. Which in this case will be

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unconstrained. Insecure interaction
detection run a static taint analysis and

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check whether tainted data can reach a
sink in an unsafe way. We consider as

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sinks memcpy like functions which are
functions that implement semantically

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equivalent memcyp, strcpy and so forth. We
raise alert if we see that there is a

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dereference of a tainted variable and if
we see there are comparisons of tainted

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variables in loop conditions to detect
possible DoS vulnerabilities. Let's see an

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example again. So we got here. We know
that our query variable is tainted and

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it's unconstrained. And then we follow the
taint in the function process_request,

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which we see will eventually copy the data
from q to arg. Now we see that arg is 128

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bytes long while q is unconstrained and
therefore we generate an alert here. Our

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static taint engine is based on BootStomp
and is completely based on symbolic

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execution, which means that the taint is
propagated following the program data

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flow. Let's see an example. So assuming
that we have this code, the first

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instruction takes the result from some
seed function that might return for

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instance, some user input. And in a
symbolic world, what we do is we create a

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symbolic variable ty and assign to it a
tainted variable that we call TAINT_ty,

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which is the taint target. The next
destruction X takes the value ty plus 5

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and a symbolic word. We just follow the
data flow and x gets assigned TAINT_ty

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plus 5 which effectively taints also X. If
at some point X is overwritten with some

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constant data, the taint is automatically
removed. In its original design,

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BootStomp, the taint is removed also when
data is constrained. For instance, here we

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00:21:07,860 --> 00:21:11,880
can see that the variable n is tainted but
then is constrained between two values 0

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00:21:11,880 --> 00:21:19,770
and 255. And therefore, the taint is
removed. In our taint engine we have two

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additions. We added a path prioritization
strategy and we add taint dependencies.

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The path prioritization strategy valorizes
paths that propagate the taint and

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deprioritizes those that remove it. For
instance, say again that some user input

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comes from some function and the variable
user input gets tainted. Gets tainted and

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then is passed to another function called
parse. Here, if you see there are possibly

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an infinite number of symbolic parts in
this while. But only 1 will return tainted

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data. While the others won't. So the path
prioritization strategy valorizes this

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path instead of the others. This has been
implemented by finding basic blocks within

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00:22:09,990 --> 00:22:16,140
a function that return a nonconstant data.
And if one is found, we follow its return

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before considering the others. Taint
dependencies allows smart untaint

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strategies. Let's see again the example.
So we know that user input here is

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tainted, is then parsed and then we see
that it's length is checked and stored in

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a variable n. Its size is checked and if
it's higher than 512 bytes, the function

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will return. Otherwise it copies the data.
Now in this case, it might happen that if

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this strlen function is not analyzed
because of some static analysis input

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decisions, the taint tag of cmd might be
different from the taint tag of n and in

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this case, though, and gets untainted, cmd
is not untainted and the strcpy can raise,

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sorry, carries a false positive. So to fix
this problem. Basically we create a

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dependency between the taint tag of n and
the taint tag of cmd. And when n gets

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untainted, cmd gets untainted as well. So
we don't have more false positives. This

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procedure is automatic and we find
functions that implement streamlined

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semantically equivalent code and create
taint tag dependencies. OK. Let's see our

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evaluation. We ran 3 different evaluations
on 2 different data sets. The first one

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composed by 53 latest firmware samples
from seven vendors and a second one 899

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00:23:55,140 --> 00:24:02,340
firmware gathered from related work. In
the first case, we can see that the total

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00:24:02,340 --> 00:24:09,720
number of binaries considered are 8.5k,
few more than that. And our system

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00:24:09,720 --> 00:24:15,900
generated 87 alerts of which 51 were found
to be true positive and 34 of them were

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multibinary vulnerabilities, which means
that the vulnerability was found by

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tracking the data flow from the setter to
the getter binary. We also ran a

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comparative evaluation, which basically we
tried to measure the effort that an

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analyst would go through in analyzing
firmware using different strategies. In

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the first one, we consider each and every
binary in the firmware sample

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00:24:41,280 --> 00:24:49,050
independently and run the analysis for up
to seven days for each firmware. The

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00:24:49,050 --> 00:24:57,390
system generated almost 21000 alerts.
Considering only almost 2.5k binaries. In

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the second case we found the border
binaries, the parsers and we statically

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00:25:04,020 --> 00:25:11,070
analyzed only them, and the system
generated 9.3k alerts. Notice that in this

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00:25:11,070 --> 00:25:15,630
case, since we don't know how the user
input is introduced, like in this

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00:25:15,630 --> 00:25:21,120
experiment, we consider every IPC that we
find in the binary as a possible source of

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00:25:21,120 --> 00:25:28,470
user input. And this is true for all of
them. In the third case we ran the BDG but

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we consider each binaries independently.
Which means that we don't propagate

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constraints and we run a static single
corner analysis on each one of them. And

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00:25:37,800 --> 00:25:45,750
the system generated almost 15000 alerts.
Finally, we run Karonte and the generated

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00:25:45,750 --> 00:25:55,230
alerts were only 74. We also run a larger
scale analysis on 899 firmware samples.

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And we found that almost 40% of them were
multi binary, which means that the network

262
00:26:01,380 --> 00:26:08,220
functionalities were carried on by more
than one binary. And the system generated

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1000 alerts. Now, there is a lot going on
in this table, like details are on the

264
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paper. Here in this presentation I just go
through some as I'll motivate. So we found

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that on average, a firmware contains 4
border binaries. A BDG contains 5 binaries

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and some BDG have more than 10 binaries.
Also, we plot some statistics and we found

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that 80% of the firmware were analysed
within a day, as you can see from the top

268
00:26:39,030 --> 00:26:46,350
left figure. However, experiments
presented a great variance which we found

269
00:26:46,350 --> 00:26:51,300
was due to implementation details. For
instance we found that angr would take

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00:26:51,300 --> 00:26:56,220
more than seven hours to build some CFGs.
And sometimes they were due to a high

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00:26:56,220 --> 00:27:01,650
number of data keys. Also, we found that
the number of paths, as you can see from

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this second picture from the top, the
number of paths do not have an impact on

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00:27:09,480 --> 00:27:15,030
the total time. And as you can see from
the bottom two pictures, performance not

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00:27:15,870 --> 00:27:23,610
heavily affected by firmware size.
Firmware size here we mean the number of

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binaries in a firmware sample and the
total number of basic blocks. So let's see

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how to run Karonte. The procedure is
pretty straightforward. So first you get a

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firmware sample. You create a
configuration file containing information

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of the firmware sample and then you run
it. So let's see how. So this is an

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example of a configuration file. It
contains the information, but most of them

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00:27:51,450 --> 00:27:55,290
are optional. The only ones that are not
are this one: Firmware path, that is the

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path to your firmware. And this too, the
architecture of the firmware and the base

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00:28:00,300 --> 00:28:07,170
address if the firmware is a blob, is a
firmware blob. All the other fields are

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00:28:07,170 --> 00:28:12,381
optional. And you can set them if you have
some information about the firmware. A

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detailed explanation of all of these
fields are on our GitHub repo. Once you

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set the configuration file, you can run
Karonte. Now we provide a Docker

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container, you can find the link on our
GitHub repo. And I'm gonna run it, but

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00:28:28,666 --> 00:28:41,402
it's not gonna finish because it's gonna
take several hours. But all you have to do

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is merely... *typing noises* just run it
on the configuration file and it's gonna

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do each step that we saw. Eventually I'm
going to stop it because it's going to

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take several hours anyway. Eventually it
will produce a result file that... I ran

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00:29:02,537 --> 00:29:07,857
this yesterday so you can see it here.
There is a lot going on here. I'm just

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00:29:07,857 --> 00:29:14,780
gonna go through some important like
information. So one thing that you can see

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is that these are the border binaries that
Karonte found. Now, there might be some

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00:29:21,923 --> 00:29:26,360
false positives. I'm not sure how many
there are here. But as long as there are

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00:29:26,360 --> 00:29:32,131
no false negatives or the number is very
low, it's fine. It's good. In this case,

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00:29:32,131 --> 00:29:38,879
wait. Oh, I might have removed something.
All right, here, perfect. In this case,

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00:29:38,879 --> 00:29:45,444
this guy httpd is a true positive, which
is the web server that we were talking

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00:29:45,444 --> 00:29:52,185
before. Then we have the BDG. In this
case, we can see that Karonte found that

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00:29:52,185 --> 00:30:00,252
httpd communicates with two different
binaries, fileaccess.cgi and cgibin. Then

300
00:30:00,252 --> 00:30:10,799
we have information about the CPFs. For
instance, here we can see that. Sorry. So

301
00:30:10,799 --> 00:30:19,775
we can see here that httpd has 28 data
keys. And that the semantics CPF found 27

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00:30:19,775 --> 00:30:26,823
of them and then there might be one other
here or somewhere that I don't see .

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00:30:26,823 --> 00:30:35,835
Anyway. And then we have a list of alerts.
Now, thanks. Now, some of those may be

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00:30:35,835 --> 00:30:44,135
duplicates because of loops, so you can go
ahead and inspect all of them manually.

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00:30:44,135 --> 00:30:50,982
But I wrote a utility that you can use,
which is basically it's gonna filter out

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00:30:50,982 --> 00:31:02,100
all the loops for you. Now to remember how
I called it. This guy? Yeah. And you can

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00:31:02,100 --> 00:31:13,368
see that in total it generated, the system
generated 6... 7... 8 alerts. So let's see

308
00:31:13,368 --> 00:31:20,579
one of them. Oh, and I recently realized
that the path that I'm reporting on the

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00:31:20,579 --> 00:31:25,970
log. It's not the path from the setter
binary to the getter binary, to the sink.

310
00:31:25,970 --> 00:31:31,426
But it's only related to the getter binary
up to the sink. I'm gonna fix this in the

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00:31:31,426 --> 00:31:37,552
next days and report the whole paths.
Anyway. So here we can see that the key

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00:31:37,552 --> 00:31:43,395
content type contains user input and it's
passed in an unsafe way to the sink

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address at this address. Now. And the
binary in question is called

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00:31:49,688 --> 00:32:02,416
fileaccess.cgi. So we can see what happens
there. *keyboard noises* If you see here,

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00:32:02,416 --> 00:32:12,480
we have a string copy that copies the
content of haystack to destination,

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00:32:12,480 --> 00:32:20,751
haystack comes basically from this getenv.
And if you see destination comes as

317
00:32:20,751 --> 00:32:30,001
parameter from this function and return
and these and this by for it's as big as

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00:32:30,001 --> 00:32:38,895
0x68 bytes. And this turned out to be
actually a positive. OK. So in summary, we

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00:32:38,895 --> 00:32:46,529
presented a strategy to track data flow
across different binaries. We evaluated

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00:32:46,529 --> 00:32:52,972
our system on 952 firmware samples and
some takeaways. Analyzing firmware is not

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00:32:52,972 --> 00:32:58,156
easy and vulnerabilities persist. We found
out that firmware are made of

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00:32:58,156 --> 00:33:02,660
interconnected components and static
analysis can still be used to efficiently

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00:33:02,660 --> 00:33:07,730
find vulnerabilities at scale and finding
that communication is key for precision.

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Here's a list of bibliography that I use
throughout the presentation and I'm gonna

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00:33:12,229 --> 00:33:12,956
take questions.

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00:33:12,956 --> 00:33:18,431
*applause*

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00:33:18,431 --> 00:33:27,366
Herald: So thank you, Nilo, for a very
interesting talk. If you have questions,

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00:33:27,366 --> 00:33:32,470
we have three microphones one, two and
three. If you have a question, please go

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00:33:32,470 --> 00:33:37,684
head to the microphone and we'll take your
question. Yes. Microphone number two.

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00:33:37,684 --> 00:33:41,995
Q: Do you rely on imports from libc or
something like that or do you have some

331
00:33:41,995 --> 00:33:46,733
issues with like statically linked
binaries, stripped binaries or is it all

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00:33:46,733 --> 00:33:51,895
semantic analysis of a function?
Nilo: So. Okay. We use angr. So for

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00:33:51,895 --> 00:33:57,277
example, if you have an indirect call, we
use angr to figure out, what's the target?

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00:33:57,277 --> 00:34:02,627
And to answer your question like if you
use libc some CPFs do, for instance, then

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00:34:02,627 --> 00:34:08,313
environment CPF do any checks, if the
setenv or getenv functions are called. But

336
00:34:08,313 --> 00:34:12,873
also we use the semantic CPF, which
basically in cases where information are

337
00:34:12,873 --> 00:34:17,687
missing like there is no such thing as
libc or some vendors reimplemented their

338
00:34:17,687 --> 00:34:21,977
own functions. We use the CPF to actually
try to understand the semantics of the

339
00:34:21,977 --> 00:34:25,888
function and understand if it's, for
example, a custom setenv.

340
00:34:25,888 --> 00:34:29,900
Q: Yeah, thanks.
Herald: Microphone number three.

341
00:34:29,900 --> 00:34:36,905
Q: In embedded environments you often have
also that the getter might work on a DMA,

342
00:34:36,905 --> 00:34:43,233
some kind of vendor driver on a DMA. Are
you considering this? And second part of

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00:34:43,233 --> 00:34:47,793
the question, how would you then
distinguish this from your generic IPC?

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00:34:47,793 --> 00:34:52,502
Because I can imagine that they look very
similar in the actual code.

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00:34:52,502 --> 00:34:58,752
Nilo: So if I understand correctly your
question, you mention a case of MMIO where

346
00:34:58,752 --> 00:35:03,956
some data is retrieved directly from some
address in memory. So what we found is

347
00:35:03,956 --> 00:35:08,434
that these addresses are usually hardcoded
somewhere. So the vendor knows that, for

348
00:35:08,434 --> 00:35:13,280
example, from this address A to this
address B if some data is some data from

349
00:35:13,280 --> 00:35:18,857
this peripheral. So when we find that some
hardcoded address, like we think that this

350
00:35:18,857 --> 00:35:21,688
is like some read from some interesting
data.

351
00:35:21,688 --> 00:35:28,073
Q: Okay. And this would be also
distinguishable from your sort of CPF, the

352
00:35:28,073 --> 00:35:32,180
generic CPF would be distinguishable...
Nilo: Yeah. Yeah, yeah.

353
00:35:32,180 --> 00:35:35,775
Q: ...from a DMA driver by using this
fixed address assuming.

354
00:35:35,775 --> 00:35:39,827
Nilo: Yeah. That's what the semantic CPF
does, among the other things.

355
00:35:39,827 --> 00:35:41,336
Q: Okay. Thank you.
Nilo: Sure.

356
00:35:41,336 --> 00:35:43,856
Herald: Another question for microphone
number 3.

357
00:35:43,856 --> 00:35:46,117
Q: What's the license for Karonte?
Nilo: Sorry?

358
00:35:46,117 --> 00:35:51,130
Q: I checked the software license, I
checked the git repository and there is no

359
00:35:51,130 --> 00:35:53,440
license like at all.
Nilo: That is a very good question. I

360
00:35:53,440 --> 00:36:00,610
haven't thought about it yet. I will.
Herald: Any more questions from here or

361
00:36:00,610 --> 00:36:04,410
from the Internet? Okay. Then a big round
of applause to Nilo again for your talk.

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*postroll music*

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