Experimental Security Analysis of a Modern Automobile
Karl Koscher, Alexei Czeskis, Franziska Roesner, Shwetak Patel, and Tadayoshi Kohno
Department of Computer Science and Engineering
University of Washington
Seattle, Washington 98195–2350
Email: {supersat,aczeskis,franzi,shwetak,yoshi}@cs.washington.edu
Stephen Checkoway, Damon McCoy, Brian Kantor, Danny Anderson, Hovav Shacham, and Stefan Savage
Department of Computer Science and Engineering
University of California San Diego
La Jolla, California 92093–0404
Email: {s,dlmccoy,brian,d8anders,hovav,savage}@cs.ucsd.edu

Abstract—Modern automobiles are no longer mere mechanical devices; they are pervasively monitored and controlled by
dozens of digital computers coordinated via internal vehicular
networks. While this transformation has driven major advancements in efficiency and safety, it has also introduced a range of
new potential risks. In this paper we experimentally evaluate
these issues on a modern automobile and demonstrate the
fragility of the underlying system structure. We demonstrate
that an attacker who is able to infiltrate virtually any Electronic
Control Unit (ECU) can leverage this ability to completely
circumvent a broad array of safety-critical systems. Over a
range of experiments, both in the lab and in road tests, we
demonstrate the ability to adversarially control a wide range
of automotive functions and completely ignore driver input —
including disabling the brakes, selectively braking individual
wheels on demand, stopping the engine, and so on. We find
that it is possible to bypass rudimentary network security
protections within the car, such as maliciously bridging between
our car’s two internal subnets. We also present composite
attacks that leverage individual weaknesses, including an attack
that embeds malicious code in a car’s telematics unit and
that will completely erase any evidence of its presence after a
crash. Looking forward, we discuss the complex challenges in
addressing these vulnerabilities while considering the existing
automotive ecosystem.
Keywords—Automobiles, communication standards, communication system security, computer security, data buses.

I. I NTRODUCTION
Through 80 years of mass-production, the passenger automobile has remained superficially static: a single gasolinepowered internal combustion engine; four wheels; and the
familiar user interface of steering wheel, throttle, gearshift,
and brake. However, in the past two decades the underlying
control systems have changed dramatically. Today’s automobile is no mere mechanical device, but contains a myriad of
computers. These computers coordinate and monitor sensors,
components, the driver, and the passengers. Indeed, one
recent estimate suggests that the typical luxury sedan now
contains over 100 MB of binary code spread across 50–70

independent computers — Electronic Control Units (ECUs)
in automotive vernacular — in turn communicating over one
or more shared internal network buses [8], [13].
While the automotive industry has always considered
safety a critical engineering concern (indeed, much of this
new software has been introduced specifically to increase
safety, e.g., Anti-lock Brake Systems) it is not clear whether
vehicle manufacturers have anticipated in their designs the
possibility of an adversary. Indeed, it seems likely that this
increasing degree of computerized control also brings with
it a corresponding array of potential threats.
Compounding this issue, the attack surface for modern
automobiles is growing swiftly as more sophisticated services and communications features are incorporated into
vehicles. In the United States, the federally-mandated OnBoard Diagnostics (OBD-II) port, under the dash in virtually all modern vehicles, provides direct and standard
access to internal automotive networks. User-upgradable
subsystems such as audio players are routinely attached to
these same internal networks, as are a variety of shortrange wireless devices (Bluetooth, wireless tire pressure
sensors, etc.). Telematics systems, exemplified by General
Motors’ (GM’s) OnStar, provide value-added features such
as automatic crash response, remote diagnostics, and stolen
vehicle recovery over a long-range wireless link. To do
so, these telematics systems integrate internal automotive
subsystems with a remote command center via a widearea cellular connection. Some have taken this concept
even further — proposing a “car as a platform” model for
third-party development. Hughes Telematics has described
plans for developing an “App Store” for automotive applications [22] while Ford recently announced that it will
open its Sync telematics system as a platform for third-party
applications [14]. Finally, proposed future vehicle-to-vehicle
(V2V) and vehicle-to-infrastructure (V2X) communications
systems [5], [6], [7], [25] will only broaden the attack
surface further.

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Overall, these trends suggest that a wide range of vectors
will be available by which an attacker might compromise a
component and gain access to internal vehicular networks —
with unknown consequences. Unfortunately, while previous
research efforts have largely considered vehicular security
risks in the abstract, very little is publicly known about the
practical security issues in automobiles on the road today.
Our research aims to fill this gap.
This paper investigates these issues through an empirical lens — with active experiments against two late-model
passenger cars (same make and model). We test these
cars’ components in isolation in the lab, as a complete
system in a controlled setting (with the car elevated on
jacks), and in live road tests on a closed course. We have
endeavored to comprehensively assess how much resilience a
conventional automobile has against a digital attack mounted
against its internal components. Our findings suggest that,
unfortunately, the answer is “little.”
Indeed, we have demonstrated the ability to systematically control a wide array of components including engine,
brakes, heating and cooling, lights, instrument panel, radio,
locks, and so on. Combining these we have been able to
mount attacks that represent potentially significant threats
to personal safety. For example, we are able to forcibly and
completely disengage the brakes while driving, making it
difficult for the driver to stop. Conversely, we are able to
forcibly activate the brakes, lurching the driver forward and
causing the car to stop suddenly.
Rather than focus just on individual attacks, we conduct a
comprehensive analysis of our cars’ digital components and
internal networks. We experimentally evaluate the security
properties of each of the key components within our cars,
and we analyze the security properties of the underlying
network substrate. Beyond measuring the real threats against
the computerized components within modern cars, as well
as the fundamental reasons those threats are possible, we
explore considerations and directions for reconciling the
tension between strategies for better security and the broader
context surrounding automobiles.
II. BACKGROUND
There are over 250 million registered passenger automobiles in the United States [4]. The vast majority of these
are computer controlled to a significant degree and virtually
all new cars are now pervasively computerized. However,
in spite of their prevalence, the structure of these systems,
the functionality they provide and the networks they use
internally are largely unfamiliar to the computer security
community. In this section, we provide basic background
context concerning automotive embedded systems architecture in general and an overview of prior related work
concerning automotive security.

A. Automotive Embedded Systems
Digital control, in the form of self-contained embedded
systems called Engine Control Units (ECUs), entered US
production vehicles in the late 1970s, largely due to requirements of the California Clean Air Act (and subsequent
federal legislation) and pressure from increasing gasoline
prices [21]. By dynamically measuring the oxygen present
in exhaust fumes, the ECU could then adjust the fuel/oxygen
mixture before combustion, thereby improving efficiency
and reducing pollutants. Since then, such systems have been
integrated into virtually every aspect of a car’s functioning
and diagnostics, including the throttle, transmission, brakes,
passenger climate and lighting controls, external lights,
entertainment, and so on, causing the term ECU to be
generalized to Electronic Control Units. Thus, over the last
few decades the amount of software in luxury sedans has
grown from virtually nothing to tens of millions of lines of
code, spread across 50–70 independent ECUs [8].
ECU Coupling. Many features require complex interactions across ECUs. For example, modern Electronic
Stability Control (ESC) systems monitor individual wheel
speed, steering angle, throttle position, and various accelerometers. The ESC automatically modulates engine
torque and wheel speed to increase traction when the car’s
line stops following the steering angle (i.e., a skid). If
brakes are applied they must also interact with the Antilock Braking System (ABS). More advanced versions also
offer Roll Stability Control (RSC), which may also apply
brakes, reduce the throttle, and modulate the steering angle
to prevent the car from rolling over. Active Cruise Control
(ACC) systems scan the road ahead and automatically increase or decrease the throttle (about some pre-programmed
cruising speed) depending on the presence of slower vehicles
in the path (e.g., the Audi Q7 will automatically apply
brakes, completely stopping the vehicle if necessary, with no
user input). Versions of this technology also provide “precrash” features in some cars including pre-charging brakes
and pre-tensioning seat belts. Some new luxury sedans (e.g.,
the Lexus LS460) even offer automated parallel parking
features in which steering is completely subsumed. These
trends are further accelerated by electric-driven vehicles that
require precise software control over power management
and regenerative braking to achieve high efficiency, by a
slew of emerging safety features, such as VW’s Lane Assist
system, and by a wide range of proposed entertainment and
communications features (e.g., it was recently announced
that GM’s OnStar will offer integration with Twitter [10]).
Even full “steer-by-wire” functionality has been seen in a
range of concept cars including GM’s widely publicized Hywire fuel cell vehicle [12].
While some early systems used one-off designs and
bilateral physical wire connections for such interactions
(e.g., between different sensors and an ECU), this approach

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does not scale. A combination of time-to-market pressures,
wiring overhead, interaction complexity, and economy of
scale pressures have driven manufacturers and suppliers to
standardize on a few key digital buses, such as Controller
Area Network (CAN) and FlexRay, and software technology
platforms (cf. Autosar [1]) shared across component manufacturers and vendors. Indeed, the distributed nature of the
automotive manufacturing sector has effectively mandated
such an approach — few manufacturers can afford the overhead of full soup-to-nuts designs anymore.
Thus, the typical car contains multiple buses (generally
based on the CAN standard) covering different component
groups (e.g., a high-speed bus may interconnect powertrain components that generate real-time telemetry while
a separate low-speed bus might control binary actuators
like lights and doors). While it seems that such buses
could be physically isolated (e.g., safety critical systems
on one, entertainment on the other), in practice they are
“bridged” to support subtle interaction requirements. For
example, consider a car’s Central Locking Systems (CLS),
which controls the power door locking mechanism. Clearly
this system must monitor the physical door lock switches,
wireless input from any remote key fob (for keyless entry), and remote telematics commands to open the doors.
However, unintuitively, the CLS must also be interconnected
with safety critical systems such as crash detection to ensure
that car locks are disengaged after airbags are deployed to
facilitate exit or rescue.
Telematics. Starting in the mid-1990’s automotive
manufacturers started marrying more powerful ECUs —
providing full Unix-like environments — with peripherals
such as Global Positioning Systems (GPS), and adding a
“reach-back” component using cellular back-haul links. By
far the best known and most innovative of such systems
is GM’s OnStar, which — now in its 8th generation —
provides a myriad of services. An OnStar-equipped car
can, for example, analyze the car’s On Board Diagnostics (OBD) as it is being driven, proactively detect likely
vehicle problems, and notify the driver that a trip to the
repair shop is warranted. OnStar ECUs monitor crash sensors and will automatically place emergency calls, provide
audio-links between passengers and emergency personnel,
and relay GPS-based locations. These systems even enable
properly authorized OnStar personnel to remotely unlock
cars, track the cars’ locations and, starting with some
2009 model years, remotely stop them (for the purposes
of recovery in case of theft) purportedly by stopping the
flow of fuel to the engines. To perform these functions,
OnStar units routinely bridge all important buses in the
automobile, thereby maximizing flexibility, and implement
an on-demand link to the Internet via Verizon’s digital
cellular service. However, GM is by no means unique and
virtually every manufacturer now has a significant telemat-

ics package in their lineup (e.g., Ford’s Sync, Chrysler’s
UConnect, BMW’s Connected Drive, and Lexus’s Enform), frequently provided in collaboration with third-party
specialist vendors such as Hughes Telematics and ATX
Group.
Taken together, ubiquitous computer control, distributed
internal connectivity, and telematics interfaces increasingly
combine to provide an application software platform with
external network access. There are thus ample reasons to
reconsider the state of vehicular computer security.
B. Related Work
Indeed, we are not the first to observe the potential
fragility of the automotive environment. In the academic
context, several groups have described potential vulnerabilities in automotive systems, e.g., [19], [24], [26], [27],
[28]. They provide valuable contributions toward framing
the vehicle security and privacy problem space — notably
in outlining the security limitations of the popular CAN bus
protocol — as well as possible directions for securing vehicle
components. With some exceptions, e.g., [15], most of these
efforts consider threats abstractly; considering “what-if”
questions about a hypothetical attacker. Part of our paper’s
contribution is to make this framing concrete by providing
comprehensive experimental results assessing the behavior
of real automobiles and automotive components in response
to specific attacks.
Further afield, a broad array of researchers have considered the security problems of vehicle-to-vehicle (V2V)
systems (sometimes also called vehicular ad-hoc networks,
or VANETs); see [18] for a survey. Indeed, this work is
critical, as such future networks will otherwise present yet
another entry point by which attackers might infiltrate a
vehicle. However, our work is focused squarely on the
possibilities after any such infiltration. That is, what are the
security issues within a car, rather than external to it.
Still others have focused on theft-related access control
mechanisms, including successful attacks against vehicle
keyless entry systems [11], [16] and vehicle immobilizers [3].
Outside the academic realm, there is a small but vibrant
“tuner” subculture of automobile enthusiasts who employ
specialized software to improve performance (e.g., by removing electronic RPM limitations or changing spark timings, fuel ignition parameters, or valve timings) frequently
at the expense of regulatory compliance [20], [23]. These
groups are not adversaries; their modifications are done to
improve and personalize their own cars, not to cause harm.
In our work, we consider how an adversary with malicious
motives might disrupt or modify automotive systems.
Finally, we point out that while there is an emerging effort focused on designing fully autonomous vehicles
(e.g., DARPA Grand Challenge [9]), these are specifically

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designed to be robotically controlled. While such vehicles would undoubtedly introduce yet new security concerns, in this paper we concern ourselves solely with the
vulnerabilities in today’s commercially-available automobiles.
C. Threat Model
In this paper we intentionally and explicitly skirt the
question of a “threat model.” Instead, we focus primarily
on what an attacker could do to a car if she was able to
maliciously communicate on the car’s internal network. That
said, this does beg the question of how she might be able
to gain such access.
While we leave a full analysis of the modern automobile’s
attack surface to future research, we briefly describe here the
two “kinds” of vectors by which one might gain access to
a car’s internal networks.
The first is physical access. Someone — such as a mechanic, a valet, a person who rents a car, an ex-friend, a
disgruntled family member, or the car owner — can, with
even momentary access to the vehicle, insert a malicious
component into a car’s internal network via the ubiquitous
OBD-II port (typically under the dash). The attacker may
leave the malicious component permanently attached to the
car’s internal network or, as we show in this paper, they
may use a brief period of connectivity to embed the malware
within the car’s existing components and then disconnect. A
similar entry point is presented by counterfeit or malicious
components entering the vehicle parts supply chain — either
before the vehicle is sent to the dealer, or with a car owner’s
purchase of an aftermarket third-party component (such as
a counterfeit FM radio).
The other vector is via the numerous wireless interfaces
implemented in the modern automobile. In our car we
identified no fewer than five kinds of digital radio interfaces
accepting outside input, some over only a short range and
others over indefinite distance. While outside the scope of
this paper, we wish to be clear that vulnerabilities in such
services are not purely theoretical. We have developed the
ability to remotely compromise key ECUs in our car via
externally-facing vulnerabilities, amplify the impact of these
remote compromises using the results in this paper, and
ultimately monitor and control our car remotely over the
Internet.
III. E XPERIMENTAL E NVIRONMENT
Our experimental analyses focus on two 2009 automobiles
of the same make and model.1 We selected our particular vehicle because it contained both a large number of
1 We believe the risks identified in this paper arise from the architecture
of the modern automobile and not simply from design decisions made by
any single manufacturer. For this reason, we have chosen not to identify
the particular make and model used in our tests. We believe that other
automobile manufacturers and models with similar features may have
similar security properties.

electronically-controlled components (necessitated by complex safety features such as anti-lock brakes and stability
control) and a sophisticated telematics system. We purchased
two vehicles to allow differential testing and to validate that
our results were not tied to one individual vehicle. At times
we also purchased individual replacement ECUs via thirdparty dealers to allow additional testing. Table I lists some
of the most important ECUs in our car.
We experimented with these cars — and their internal
components — in three principal settings:
• Bench. We physically extracted hardware from the
car for analysis in our lab. As with most automobile manufacturers, our vehicles use a variant of the
Controller Area Network (CAN) protocol for communicating among vehicle components (in our case
both a high-speed and low-speed variant as well as
a variety of proprietary higher-layer network management services). Through this protocol, any component can be accessed and interrogated in isolation in
the lab. Figure 1 shows an example setup, with the
Electronic Brake Control Module (EBCM) hooked up
to a power supply, a CAN-to-USB converter, and an
oscilloscope.
•

Stationary car. We conducted most of our in-car experiments with the car stationary. For both safety and
convenience, we elevated the car on jack stands for
experiments that required the car to be “at speed”; see
Figure 3.
Figure 2 shows the experimental setup inside the car.
For these experiments, we connected a laptop to the
car’s standard On-Board Diagnostics II (OBD-II) port.
We used an off-the-shelf CAN-to-USB interface (the
CANCapture ECOM cable) to interact with the car’s
high-speed CAN network, and an Atmel AT90CAN128
development board (the Olimex AVR-CAN) with custom firmware to interact with the car’s low-speed
CAN network. The laptop ran our custom C AR S HARK
program (see below).

•

On the road. To obtain full experimental fidelity, for
some of our results we experimented at speed while on
a closed course.
We exercised numerous precautions to protect the
safety of both our car’s driver and any third parties. For
example, we used the runway of a de-commissioned
airport because the runway is long and straight, giving
us additional time to respond should an emergency
situation arise (see Figure 7).
For these experiments, one of us drove the car while
three others drove a chase car on a parallel service road;
one person drove the chase car, one documented much
of the process on video, and one wirelessly controlled
the test car via an 802.11 ad hoc connection to a laptop
in the test car that in turn accessed its CAN bus.

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Component
ECM
EBCM
TCM
BCM
Telematics
RCDLR

HVAC
SDM
IPC/DIC
Radio
TDM

Low-Speed
Comm. Bus

Functionality
Engine Control Module
Controls the engine using information from sensors to determine the amount
of fuel, ignition timing, and other engine parameters.
Electronic Brake Control Module
Controls the Antilock Brake System (ABS) pump motor and valves, preventing brakes from locking up and skidding by regulating hydraulic pressure.
Transmission Control Module
Controls electronic transmission using data from sensors and from the ECM
to determine when and how to change gears.
Body Control Module
Controls various vehicle functions, provides information to occupants, and
acts as a firewall between the two subnets.
Telematics Module
Enables remote data communication with the vehicle via cellular link.
Remote Control Door Lock Receiver
Receives the signal from the car’s key fob to lock/unlock the doors and
the trunk. It also receives data wirelessly from the Tire Pressure Monitoring
System sensors.
Heating, Ventilation, Air Conditioning
Controls cabin environment.
Inflatable Restraint Sensing and Diagnostic Module
Controls airbags and seat belt pretensioners.
Instrument Panel Cluster/Driver Information Center
Displays information to the driver about speed, fuel level, and various alerts
about the car’s status.
Radio
In addition to regular radio functions, funnels and generates most of the incabin sounds (beeps, buzzes, chimes).
Theft Deterrent Module
Prevents vehicle from starting without a legitimate key.

Table I.

High-Speed
Comm. Bus
X
X
X

X

X

X

X

X

X
X
X
X
X

Key Electronic Control Units (ECUs) within our cars, their roles, and which CAN buses they are on.

The C AR S HARK Tool. To facilitate our experimental
analysis, we wrote C AR S HARK — a custom CAN bus analyzer and packet injection tool (see Figure 4). While there
exist commercially available CAN sniffers, none were appropriate for our use. First, we needed the ability to process
and manipulate our vendor’s proprietary extensions to the
CAN protocol. Second, while we could have performed
limited testing using a commercial CAN sniffer coupled
with a manufacturer-specific diagnostic service tool, this
combination still doesn’t offer the flexibility to support our
full range of attack explorations, including reading out ECU
memory, loading custom code into ECUs, or generating
fuzz-testing packets over the CAN interface.
IV. I NTRA -V EHICLE N ETWORK S ECURITY
Before experimentally evaluating the security of individual car components, we assess the security properties
of the CAN bus in general, which we describe below.
We do so by first considering weaknesses inherent to the
protocol stack and then evaluating the degree to which
our car’s components comply with the standard’s specifications.

A. CAN Bus
There are a variety of protocols that can be implemented
on the vehicle bus, but starting in 2008 all cars sold in the
U.S. are required to implement the Controller Area Network
(CAN) bus (ISO 11898 [17]) for diagnostics. As a result,
CAN — roughly speaking, a link-layer data protocol — has
become the dominant communication network for in-car
networks (e.g., used by BMW, Ford, GM, Honda, and
Volkswagen).
A CAN packet (shown in Figure 5) does not include
addresses in the traditional sense and instead supports a
publish-and-subscribe communications model. The CAN ID
header is used to indicate the packet type, and each packet
is both physically and logically broadcast to all nodes,
which then decide for themselves whether to process the
packets.
The CAN variant for our car includes slight extensions
to framing (e.g., on the interpretation of certain CAN ID’s)
and two separate physical layers — a high-speed bus which
is differentially-signaled and primarily used by powertrain
systems and a low-speed bus (SAE J2411) using a single
wire and supporting less-demanding components. When
necessary, a gateway bridge can route selected data between

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5

Figure 1. Example bench setup within our
lab. The Electronic Brake Control Module
(ECBM) is hooked up to a power supply, a
CAN-to-USB converter, and an oscilloscope.

Figure 2. Example experimental setup. The
laptop is running our custom C AR S HARK
CAN network analyzer and attack tool. The
laptop is connected to the car’s OBD-II port.

Figure 3.
To test ECU behavior in a
controlled environment, we immobilized the
car on jack stands while mounting attacks.

Remote transmission
request

Identifier
Identifier
extension
11 bits

18 bits

Substitute remote
request
Start-offrame

CRC delimiter

Data length
code
4 bits 0–8 bytes

ACK
delimiter
15 bits

Reserved
2 bits

Extended identifier

7 bits

ACK
Data

CRC

End-offrame

Figure 5. CAN packet structure. Extended frame format is shown. Base
frame format is similar.

Figure 4. Screenshot of the C AR S HARK interface. C AR S HARK is being
used to sniff the CAN bus. Values that have been recently updated are in
yellow. The left panel lists all recognized nodes on high and low speed
subnets of the CAN bus and has some action buttons. The demo panel on
the right provides some proof-of-concept demos.

the two buses. Finally, the protocol standards define a range
of services to be implemented by ECUs.
B. CAN Security Challenges
The underlying CAN protocol has a number of inherent
weaknesses that are common to any implementation. Key
among these:
Broadcast Nature. Since CAN packets are both physically and logically broadcast to all nodes, a malicious
component on the network can easily snoop on all communications or send packets to any other node on the
network. C AR S HARK leverages this property, allowing us
to observe and reverse-engineer packets, as well as to inject
new packets to induce various actions.
Fragility to DoS. The CAN protocol is extremely
vulnerable to denial-of-service attacks. In addition to simple
packet flooding attacks, CAN’s priority-based arbitration
scheme allows a node to assert a “dominant” state on the
bus indefinitely and cause all other CAN nodes to back
off. While most controllers have logic to avoid accidentally

breaking the network this way, adversarially-controlled hardware would not need to exercise such precautions.
No Authenticator Fields. CAN packets contain no
authenticator fields — or even any source identifier fields —
meaning that any component can indistinguishably send a
packet to any other component. This means that any single
compromised component can be used to control all of the
other components on that bus, provided those components
themselves do not implement defenses; we consider the
security of individual components in Section V.
Weak Access Control. The protocol standards for our
car specify a challenge-response sequence to protect ECUs
against certain actions without authorization. A given ECU
may participate in zero, one, or two challenge-response
pairs:
• Reflashing and memory protection. One challengeresponse pair restricts access to reflashing the ECU and
reading out sensitive memory. By design, a service shop
might authenticate with this challenge-response pair in
order to upgrade the firmware on an ECU.
•

Tester capabilities. Modern automobiles are complex
and thus diagnosing their problems requires significant
support. Thus, a major use of the CAN bus is in
providing diagnostic access to service technicians. In
particular, external test equipment (the “tester”) must
be able to interrogate the internal state of the car’s
components and, at times, manipulate this state as well.

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6

Our car implements this capability via the DeviceControl service which is accessed in an RPC-like fashion
directly via CAN messages. In our car, the second
challenge-response pair described above is designed to
restrict access to the DeviceControl services.
Under the hood, ECUs are supposed to use a fixed challenge
(seed) for each of these challenge-response pairs; the corresponding responses (keys) are also fixed and stored in these
ECUs. The motivation for using fixed seeds and keys is to
avoid storing the challenge-response algorithm in the ECU
firmware itself (since that firmware could be read out if an
external flash chip is used). Indeed, the associated reference
standard states “under no circumstances shall the encryption
algorithm ever reside in the node.” (The tester, however, does
have the algorithm and uses it to compute the key.) Different
ECUs should have different seeds and keys.
Despite these apparent security precautions, to the best of
our knowledge many of the seed-to-key algorithms in use
today are known by the car tuning community.
Furthermore, as described in the protocol standards, the
challenges (seeds) and responses (keys) are both just 16 bits.
Because the ECUs are required to allow a key attempt every
10 seconds, an attacker could crack one ECU key in a little
over seven and a half days. If an attacker has access to
the car’s network for this amount of time (such as through
another compromised component), any reflashable ECU can
be compromised. Multiple ECUs can be cracked in parallel,
so this is an upper bound on the amount of time it could take
to crack a key in every ECU in the vehicle. Furthermore,
if an attacker can physically remove a component from
the car, she can further reduce the time needed to crack
a component’s key to roughly three and a half days by
powercycling the component every two key attempts (we
used this approach to perform an exhaustive search for the
Electronic Brake Control Module (EBCM) key on one of
our cars, recovering the key in about a day and a half; see
Figure 1 for our experimental setup).
In effect, there are numerous realistic scenarios in which
the challenge-response sequences defined in the protocol
specification can be circumvented by a determined attacker.
ECU Firmware Updates and Open Diagnostic Control.
Given the generic weaknesses with the aforementioned
access control mechanisms, it is worth stepping back and
reconsidering the benefits and risks associated with exposing
ECUs to reflashing and diagnostic testing.
First, the ability to do software-only upgrades to ECUs
can be extremely valuable to vehicle manufacturers, who
might otherwise have to bear the cost of physically replacing
ECUs for trivial defects in the software. For example, one
of us recently received a letter from a car dealer, inviting us
to visit an auto shop in order to upgrade the firmware on
our personal car’s ECM to correctly meet certain emission
requirements. However, it is also well known that attackers

can use software updates to inject malicious code into
systems [2]. The challenge-response sequences alone are
not sufficient to protect against malicious firmware updates;
in subsequent sections we investigate whether additional
protection mechanisms are deployed at a higher level (such
as the cryptographically signed firmware updates).
Similarly, the DeviceControl service is a tremendously
powerful tool for assisting in the diagnosis of a car’s
components. But, given the generic weaknesses of the CAN
access control mechanisms, the DeviceControl capabilities
present enumerable opportunities to an attacker (indeed, a
great number of our attacks are built on DeviceControl).
In many ways this challenge parallels the security vs.
functionality tension presented by debuggers in conventional
operating systems; to be effective debuggers need to be able
to examine and manipulate all state, but if they can do that
they can do anything. However, while traditional operating
systems generally finesse this problem via access-control
rights on a per-user basis, there is no equivalent concept in
CAN. Given the weaknesses with the CAN access control
sequence, the role of “tester” is effectively open to any node
on the bus and thus to any attacker.
Worse, in Section IV-C below we find that many ECUs in
our car deviate from their own protocol standards, making
it even easier for an attacker to initiate firmware updates or
DeviceControl sequences — without even needing to bypass
the challenge-response protocols.
C. Deviations from Standards
In several cases, our car’s protocol standards do prescribe
risk-mitigation strategies with which components should
comply. However, our experimental findings revealed that
not all components in the car always follow these specifications.
Disabling Communications. For example, the standard states that ECUs should reject the “disable CAN
communications” command when it is unsafe to accept and
act on it, such as when a car is moving. However, we
experimentally verified that this is not actually the case in
our car: we were able to disable communications to and from
all the ECUs in Table I even with the car’s wheels moving
at speed on jack stands and while driving on the closed road
course.
Reflashing ECUs While Driving. The standard also
states that ECUs should reject reflashing events if they deem
them unsafe. In fact, it states: “The engine control module
should reject a request to initiate a programming event if the
engine were running.” However, we experimentally verified
that we could place the Engine Control Module (ECM) and
Transmission Control Module (TCM) into reflashing mode
when our car was at speed on jack stands. When the ECM
enters this mode, the engine stops running. We also verified
that we could place the ECM into reflashing mode while
driving on the closed course.

Appears in 2010 IEEE Symposium on Security and Privacy. See http://www.autosec.org/ for more information.

7

Noncompliant Access Control: Firmware and Memory.
The standard states that ECUs with emissions, anti-theft,
or safety functionality must be protected by a challengeresponse access control protocol (as per Section IV-B).
Even disregarding the weakness of this protocol, we
found it was implemented less broadly than we would
have expected. For example, the telematics unit in our
car, which are connected to the car’s CAN buses, use a
hardcoded challenge and a hardcoded response common
to all similar units, seemingly in violation of the standard
(specifically, the standard states that “all nodes with the
same part number shall NOT have the same security seed”).
Even worse, the result of the challenge-response protocol
is never used anywhere; one can reflash the unit at any
time without completing the challenge-response protocol.
We verified experimentally that we can load our own code
onto our car’s telematics unit without authenticating.
Some access-controlled operations, such as reading sensitive memory areas (such as the ECU’s program or keys)
may be outright denied if deemed too risky. For example,
the standard states that an ECU can define memory addresses that “[it] will not allow a tester to read under any
circumstances (e.g., the addresses that contain the security
seed and key values).” However, in another instance of noncompliance, we experimentally verified that we could read
the reflashing keys out of the BCM without authenticating,
and the DeviceControl keys for the ECM and TCM just by
authenticating with the reflashing key. We were also able to
extract the telematics units’ entire memory, including their
keys, without authentication.
Noncompliant Access Control: Device Overrides. Recall that the DeviceControl service is used to override the
state of components. However, ECUs are expected to reject
unsafe DeviceControl override requests, such as releasing
the brakes when the car is in motion (an example mentioned
in the standard). Some of these unsafe overrides are needed
for testing during the manufacturing process, so those can be
enabled by authenticating with the DeviceControl key. However, we found during our experiments that certain unsafe
device control operations succeeded without authenticating;
we summarize these in Tables II, V-A, and IV.
Imperfect Network Segregation. The standard implicitly defines the high-speed network as more trusted than the
low-speed network. This difference is likely due to the fact
that the high-speed network includes the real-time safetycritical components (e.g., engine, brakes), while the lowspeed network commonly includes components less critical
to safety, like the radio and the HVAC system.
The standard states that gateways between the two networks must only be re-programmable from the high-speed
network, presumably to prevent a low-speed device from
compromising a gateway to attack the high-speed network.
In our car, there are two ECUs which are on both buses and

can potentially bridge signals: the Body Controller Module
(BCM) and the telematics unit. While the telematics unit
is not technically a gateway, it connects to both networks
and can only be reprogrammed (against the spirit of the
standard) from the low-speed network, allowing a lowspeed device to attack the high-speed network through the
telematics unit. We verified that we could bridge these
networks by uploading code to the telematics unit from the
low-speed network that, in turn, sent packets on the highspeed network.
V. C OMPONENT S ECURITY
We now examine individual components on our car’s
CAN network, and what an attacker could do by communicating with each one individually. We discuss compound
attacks involving multiple components in Section VI. We
omit certain details (such as complete packet payloads) to
prevent would-be attackers from using our results directly.
A. Attack Methodology
Recall that Table I gives an overview of our car’s critical
components, their functionality, and whether they are on
the car’s high-speed or low-speed CAN subnet. For each of
these components, our methodology for formulating attacks
consisted of some or all of the following three major
approaches, summarized below.
Packet Sniffing and Targeted Probing. To begin, we
used C AR S HARK to observe traffic on the CAN buses
in order to determine how ECUs communicate with each
other. This also revealed to us which packets were sent as
we activated various components (such as turning on the
headlights). Through a combination of replay and informed
probing, we were able to discover how to control the radio,
the Instrument Panel Cluster (IPC), and a number of the
Body Control Module (BCM) functions, as we discuss
below. This approach worked well for packets that come
up during normal operation, but was less useful in mapping
the interface to safety-critical powertrain components.
Fuzzing. Much to our surprise, significant attacks do
not require a complete understanding or reverse-engineering
of even a single component of the car. In fact, because
the range of valid CAN packets is rather small, significant
damage can be done by simple fuzzing of packets (i.e.,
iterative testing of random or partially random packets). Indeed, for attackers seeking indiscriminate disruption, fuzzing
is an effective attack by itself. (Unlike traditional uses of
fuzzing, we use fuzzing to aid in the reverse engineering of
functionality.)
As mentioned previously, the protocol standards for our
car define a CAN-based service called DeviceControl, which
allows testing devices (used during manufacturing quality
control or by mechanics) to override the normal output
functionality of an ECU or reset some learned internal

Appears in 2010 IEEE Symposium on Security and Privacy. See http://www.autosec.org/ for more information.

8

state. The DeviceControl service takes an argument called
a Control Packet Identifier (CPID), which specifies a group
of controls to override. Each CPID can take up to five bytes
as parameters, specifying which controls in the group are
being overridden, and how to override them. For example,
the Body Control Module (BCM) exports controls for the
various external lights (headlights, brakelights, etc.) and their
associated brightness can be set via the parameter data.
We discovered many of the DeviceControl functions
for select ECUs (specifically, those controlling the engine
(ECM), body components (BCM), brakes (EBCM), and
heating and air conditioning (HVAC) systems) largely by
fuzz testing. After enumerating all supported CPIDs for each
ECU, we sent random data as an argument to valid CPIDs
and correlated input bits with behaviors.
Reverse-Engineering. For a small subset of ECUs
(notably the telematics unit, for which we obtained multiple
instances via Internet-based used parts resellers) we dumped
their code via the CAN ReadMemory service and used a
third-party debugger (IDA Pro) to explicitly understand how
certain hardware features were controlled. This approach
is essential for attacks that require new functionality to be
added (e.g., bridging low and high-speed buses) rather than
simply manipulating existing software capabilities.
B. Stationary Testing
We now describe the results of our experiments with
controlling critical components of the car. All initial experiments were done with the car stationary, in many cases
immobilized on jack stands for safety, as shown in Figure 3.
Some of our results are summarized in Tables II, V-A,
and IV for fuzzing, and in Table V for other results.
Tables II, V-A, and IV indicate the packet that was sent
to the corresponding module, the resulting action, and four
additional pieces of information: (1) Can the result of this
packet be overridden manually, such as by pulling the
physical door unlock knob, pushing on the brakes, or some
other action? A No in this column means that we have found
no way to manually override the result. (2) Does this packet
have the same effect when the car is at speed? For this
column, “at speed” means when the car was up on jack
stands but the throttle was applied to bring the wheel speed
to 40 MPH. (3) Does the module in question need to be
unlocked with its DeviceControl key before these packets
can elicit results? The fourth (4) additional column reflects
our experiments during a live road test, which we will turn
to in subsection V-C. Table V is similar, except that only
the Kill Engine result is caused by a DeviceControl packet;
we did not need to unlock the ECU before initiating this
DeviceControl packet.
All of the controlled experiments were initially conducted
on one car, and then all were repeated on our second car
(road tests were only performed with the first car).

Figure 6. Displaying an arbitrary message and a false speedometer reading
on the Driver Information Center. Note that the car is in Park.

Radio. One of the first attacks we discovered was how
to control the radio and its display. We were able to completely control — and disable user control of — the radio,
and to display arbitrary messages. For example, we were
able to consistently increase the volume and prevent the user
from resetting it. As the radio is also the component which
controls various car sounds (e.g., turn signal clicks and seat
belt warning chimes), we were also able to produce clicks
and chimes at arbitrary frequencies, for various durations,
and at different intervals. Table V presents some of these
results.
Instrument Panel Cluster. We were able to fully control the Instrument Panel Cluster (IPC). We were able to
display arbitrary messages, falsify the fuel level and the
speedometer reading, adjust the illumination of instruments,
and so on (also shown in Table V). For example, Figure 6
shows the instrument panel display with a message that
we set by sending the appropriate packets over the CAN
network. We discuss a more sophisticated attack using our
control over the speedometer in Section VI.
Body Controller. Control of the BCM’s function is
split across the low-speed and high-speed buses. By reverseengineering packets sent on the low-speed bus (Table V) and
by fuzzing packets on the high-speed bus (as summarized
in Table II), we were able to control essentially all of the
BCM’s functions. This means that we were able to discover
packets to: lock and unlock the doors; jam the door locks
by continually activating the lock relay; pop the trunk;
adjust interior and exterior lighting levels; honk the horn
(indefinitely and at varying frequencies); disable and enable
the window relays; disable and enable the windshield wipers;
continuously shoot windshield fluid; and disable the key lock
relay to lock the key in the ignition.
Engine. Most of the attacks against the engine were
found by fuzzing DeviceControl requests to the ECM. These
findings are summarized in Table V-A. We were able to
boost the engine RPM temporarily, disturb engine timing by
resetting the learned crankshaft angle sensor error, disable

Appears in 2010 IEEE Symposium on Security and Privacy. See http://www.autosec.org/ for more information.

9

Packet
07
07
07
07
07
07
07
07
07
07
07
07
07
07
07

AE
AE
AE
AE
AE
AE
AE
AE
AE
AE
AE
AE
AE
AE
AE

...
...
...
...
...
...
...
...
...
...
...
...
...
...
...

Result
1F
C1
77
80
D8
9A
CE
34
F9
F8
15
15
22
00
1D

87
A8
09
1B
7D
F2
26
5F
46
2C
A2
A2
7A
00
1D

Continuously Activates Lock Relay
Windshield Wipers On Continuously
Pops Trunk
Releases Shift Lock Solenoid
Unlocks All Doors
Permanently Activates Horn
Disables Headlights in Auto Light Control
All Auxiliary Lights Off
Disables Window and Key Lock Relays
Windshield Fluid Shoots Continuously
Controls Horn Frequency
Controls Dome Light Brightness
Controls Instrument Brightness
All Brake/Auxiliary Lights Off
Forces Wipers Off and Shoots Windshield Fluid Continuously

Manual
Override

At
Speed

Need to
Unlock

Tested on
Runway

Yes
No
No
No
Yes
No
Yes
No
No
No
No
No
No
No
Yes†

Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes
Yes

No
No
No
No
No
No
No
No
No
No
No
No
No
No
No

X
X
X
X
X
X

X
X

Table II. Body Control Module (BCM) DeviceControl Packet Analysis. This table shows BCM DeviceControl packets and their effects that we discovered
during fuzz testing with one of our cars on jack stands. A Xin the last column indicates that we also tested the corresponding packet with the driving on a
runway. A “Yes” or “No” in the columns “Manual Override,” “At Speed,” and “Need to Unlock” indicate whether or not (1) the results could be manually
overridden by a car occupant, (2) the same effect was observed with the car at speed (the wheels spinning at about 40 MPH and/or on the runway), and
(3) the BCM needed to be unlocked with its DeviceControl key.
† The highest setting for the windshield wipers cannot be disabled and serves as a manual override.

Packet
07
07
07
07
07
07
Table III.

AE
AE
AE
AE
AE
AE

...
...
...
...
...
...

Result
E5
CE
5E
95
8D
00

EA
32
BD
DC
C8
00

Initiate Crankshaft Re-learn; Disturb Timing
Temporary RPM Increase
Disable Cylinders, Power Steering/Brakes
Kill Engine, Cause Knocking on Restart
Grind Starter
Increase Idle RPM

At
Speed

Need to
Unlock

Yes
No
Yes
Yes
No
No

Yes
Yes
Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes
Yes
Yes

Tested on
Runway
X
X
X

Engine Control Module (ECM) DeviceControl Packet Analysis. This table is similar to Table II.

Packet
07
07
07
07

Manual
Override

AE
AE
AE
AE

...
...
...
...

Result
25
20
86
FF

2B
88
07
FF

Engages Front Left Brake
Engages Front Right Brake/Unlocks Front Left
Unevenly Engages Right Brakes
Releases Brakes, Prevents Braking

Manual
Override

At
Speed

Need to
Unlock†

Tested on
Runway

No
No
No
No

Yes
Yes
Yes
Yes

Yes
Yes
Yes
Yes

X
X
X
X

Table IV. Electronic Brake Control Module (EBCM) DeviceControl Packet Analysis. This table is similar to Table II.
† The EBCM did not need to be unlocked with its DeviceControl key when the car was on jack stands. Later, when we tested these packets on the runway,
we discovered that the EBCM rejected these commands when the speed of the car exceeded 5 MPH without being unlocked.
Destination
ECU
IPC
Radio
Radio
IPC
BCM
BCM
BCM
BCM
Radio
ECM

Manual
Override

At
Speed

Tested on
Runway

Falsify Speedometer Reading
Increase Radio Volume
Change Radio Display
Change DIC Display

No
No
No
No

Yes
Yes
Yes
Yes

X

Unlock Car†
Lock Car†
Remote Start Car†
Car Alarm Honk†
Ticking Sound
Kill Engine

Yes
Yes
No
No
No
No

Yes
Yes
No
No
Yes
Yes

Packet
00
04
63
00
27
04
04
04
04
83
AE

00
00
01
02
01
03
01
0B
0E
32
0E

...
...
...
...
...

Result
00
00
39
00
65

00
00
00
00
00

... 00 00
... 00 7E

Table V. Other Example Packets. This table shows packets, their recipients, and their effects that we discovered via observation and reverse-engineering.
In contrast to the DeviceControl packets in Tables II, V-A and IV, these packets may be sent during normal operation of the car; we simply exploited the
broadcast nature of the CAN bus to send them from C AR S HARK instead of their normal sources. For this reason, we did not test most of them at the
runway, since they are naturally “tested” during normal operation.
† As ordinarily done by the key fob.

Appears in 2010 IEEE Symposium on Security and Privacy. See http://www.autosec.org/ for more information.

10

all cylinders simultaneously (even with the car’s wheels
spinning at 40 MPH when on jack stands), and disable the
engine such that it knocks excessively when restarted, or
cannot be restarted at all. Additionally, we can forge a packet
with the “airbag deployed" bit set to disable the engine.
Finally, we also discovered a packet that will adjust the
engine’s idle RPM.
Brakes. Our fuzzing of the Electronic Brake Control
Module (see Table IV) allowed us to discover how to lock
individual brakes and sets of brakes, notably without needing
to unlock the EBCM with its DeviceControl key. In one case,
we sent a random packet which not only engaged the front
left brake, but locked it resistant to manual override even
through a power cycle and battery removal. To remedy this,
we had to resort to continued fuzzing to find a packet that
would reverse this effect. Surprisingly, also without needing
to unlock the EBCM, we were also able to release the brakes
and prevent them from being enabled, even with car’s wheels
spinning at 40 MPH while on jack stands.
HVAC. We were able to control the cabin environment
via the HVAC system: we discovered packets to turn on and
off the fans, the A/C, and the heat, in some cases with no
manual override possible.
Generic Denial of Service. In another set of experiments, we disabled the communication of individual components on the CAN bus. This was possible at arbitrary times,
even with the car’s wheels spinning at speeds of 40 MPH
when up on jack stands. Disabling communication to/from
the ECM when the wheels are spinning at 40 MPH reduces
the car’s reported speed immediately to 0 MPH. Disabling
communication to/from the BCM freezes the instrument
panel cluster in its current state (e.g., if communication is
disabled when the car is going 40 MPH, the speedometer
will continue to report 40 MPH). The car can be turned off
in this state, but without re-enabling communication to/from
the BCM, the engine cannot be turned on again.
Thus, we were able to easily prevent a car from turning
on. We were also able to prevent the car from being turned
off: while the car was on, we caused the BCM to activate
its ignition output. This output is connected in a wired-OR
configuration with the ignition switch, so even if the switch
is turned to off and the key removed, the car will still run.
We can override the key lock solenoid, allowing the key to
be removed while the car is in drive, or preventing the key
from being removed at all.
C. Road Testing
Comprehensive and safe testing of these and other attacks
requires an open area where individuals and property are at
minimal risk. Fortunately, we were able to obtain access
to the runway of a de-commissioned airport to re-evaluate
many of the attacks we had identified with the car up on
jack stands. To maximize safety, we used a second, chase

Figure 7. Road testing on a closed course (a de-commissioned airport
runway). The experimented-on car, with our driver wearing a helmet, is in
the background; the chase car is in the foreground. Photo courtesy of Mike
Haslip.

car in addition to the experimental vehicle; see Figure 7.
This allowed us to have all but one person outside of the
experimented-on car. The experimented-on car was controlled via a laptop running C AR S HARK and connected to
the CAN bus via the OBD-II port. We in turn controlled this
laptop remotely via a wireless link to another laptop in the
chase car. To maintain the wireless connection between the
laptops, we drove the chase car parallel to the experimentedon car, which also allowed us to capture these experiments
on video.
Our experimental protocol was as follows: we started
the cars down the runway at the same time, transmitted
one or more packets on the experimented-on car’s CAN
network (indirectly through a command sent from the laptop in the chase car), waited for our driver’s verbal confirmation/description (using walkie-talkies to communicate
between the cars), and then sent one or more cancellation
packets. Had something gone wrong, our driver would
have yanked on a cord attached to the CAN cable and
pulled the laptop out of the OBD-II. As we verified in
preparatory safety tests, this disconnect would have caused
the car to revert back to normal within a few seconds;
fortunately, our driver never needed to make use of this
precaution.
Our allotted time at the airport prevented us from reverifying all of our attacks while driving, and hence we
experimentally re-tested a selected subset of those attacks;
the final column of Tables II, V-A, IV, and V contain a
check mark for the experiments that we re-evaluated while
driving. Most our results while driving were identical to our
results on jack stands, except that the EBCM needed to be
unlocked to issue DeviceControl packets when the car was
traveling over 5 MPH. This a minor caveat from an actual
attack perspective; as noted earlier, attack hardware attached
to the car’s CAN bus can recover the credentials necessary
to unlock the EBCM.

Appears in 2010 IEEE Symposium on Security and Privacy. See http://www.autosec.org/ for more information.

11

Even at speeds of up to 40 MPH on the runway, the attack
packets had their intended effect, whether it was honking the
horn, killing the engine, preventing the car from restarting,
or blasting the heat. Most dramatic were the effects of DeviceControl packets to the Electronic Brake Control Module
(EBCM) — the full effect of which we had previously not
been able to observe. In particular, we were able to release
the brakes and actually prevent our driver from braking; no
amount of pressure on the brake pedal was able to activate
the brakes. Even though we expected this effect, reversed it
quickly, and had a safety mechanism in place, it was still a
frightening experience for our driver. With another packet,
we were able to instantaneously lock the brakes unevenly;
this could have been dangerous at higher speeds. We sent
the same packet when the car was stationary (but still on
the closed road course), which prevented us from moving it
at all even by flooring the accelerator while in first gear.
These live road tests are effectively the “gold standard” for
our attacks as they represent realistic conditions (unlike our
controlled stationary environment). For example, we were
never able to completely characterize the brake behavior
until the car was on the road; the fact that the back wheels
were stationary when the car was on jack stands provided
additional input to the EBCM which resulted in illogical
behavior. The fact that many of these safety-critical attacks
are still effective in the road setting suggests that few
DeviceControl functions are actually disabled when the car
is at speed while driving, despite the clear capability and
intention in the standard to do so.

too fast. We implemented this attack both as a C AR S HARK
module and as custom firmware for the AVR-CAN board.
The custom firmware consisted of 105 lines of C code.
We tested this attack by comparing the displayed speed of
one of our cars with the car’s actual speed while driving
on a closed course and measuring the speed with a radar
gun.
Lights Out. Our analysis in Section V uncovered
packets that can disable certain interior and exterior lights
on the car. We combined these packets to disable all of the
car’s lights when the car is traveling at speeds of 40 MPH
or more, which is particularly dangerous when driving in
the dark. This includes the headlights, the brake lights, the
auxiliary lights, the interior dome light, and the illumination
of the instrument panel cluster and other display lights inside
the car. This attack requires the lighting control system to
be in the “automatic” setting, which is the default setting for
most drivers. One can imagine this attack to be extremely
dangerous in a situation where a victim is driving at high
speeds at night in a dark environment; the driver would not
be able to see the the road ahead, nor the speedometer, and
people in other cars would not be able to see the victim
car’s brake lights. We conducted this experiment on both
cars while they were on jack stands and while driving on a
closed course.

The previous section focused on assessing what an attacker might be able to do by controlling individual devices.
We now take a step back to discuss possible scenarios in
which multiple components are exploited in a composite
attack. The results in this section emphasize that the issue
of vehicle security is not simply a matter of securing
individual components; the car’s network is a heterogeneous
environment of interacting components, and must be viewed
and secured as such.

Self-Destruct. Combining our control over various
BCM components, we created a “Self-Destruct” demo in
which a 60-second count-down is displayed on the Driver
Information Center (the dash), accompanied by clicks at an
increasing rate and horn honks in the last few seconds. In our
demo, this sequence culminated with killing the engine and
activating the door lock relay (preventing the occupant from
using the electronic door unlock button). This demo, which
we tested on both cars, required fewer than 200 lines of code
added to C AR S HARK, most of them for timing the clicking
and the count-down. One could also extend this sequence to
include any of the other actions we learned how to control:
releasing or slamming the brakes, extinguishing the lights,
locking the doors, and so on.

A. Composite Attacks

B. Bridging Internal CAN Networks

Numerous composite attacks exist. Below we describe a
few that we implemented and experimentally verified.

Multiple components — including a wealth of aftermarket
devices like radios — are attached to or could be attached to
a car’s low-speed CAN bus. Critical components, like the
EBCM brake controller, are connected to the separate highspeed bus, with the Body Control Module (BCM) regulating
access between the two buses. One might therefore assume
that the devices attached to the low-speed bus, including
aftermarket devices, will not be able to adversely impact
critical components on the high-speed bus.
Our experiments and analyses found this assumption
to be false. Our car’s telematics unit is also connected
to both buses. We were able to successfully reprogram

VI. M ULTI -C OMPONENT I NTERACTIONS

Speedometer. In one attack, we manipulate the speedometer to display an arbitrary speed or an arbitrary offset
of the current speed — such as 10 MPH less than the actual
speed (halving the displayed speed up to a real speed of
20 MPH in order to minimize obvious anomalies to the
driver). This is a composite attack because it requires both
intercepting actual speed update packets on the low speed
CAN bus (sent by the BCM) and transmitting maliciouslycrafted speed update packets with the falsified speed. Such
an attack could, for example, trick a driver into driving

Appears in 2010 IEEE Symposium on Security and Privacy. See http://www.autosec.org/ for more information.

12

our car’s telematics unit from a device connected to the
car’s low-speed bus (in our experiments, a laptop running C AR S HARK). Once reprogrammed, our telematics
unit acts as a bridge, relaying packets from the lowspeed bus onto the high-speed bus. This demonstrates that
any device attached to the low-speed bus can bypass the
BCM gateway and influence the operation of the safetycritical components. Such a situation is particularly concerning given the abundance of potential aftermarket addons available for the low-speed bus. Our complete attack
consisted of only the following two steps: initiate a reprogramming request to the telematics unit via the lowspeed bus; and then upload 1184 bytes of binary code (291
instructions) to the telematics unit’s RAM via the low-speed
bus.
C. Hosting Code; Wiping Code
This method for injecting code into our car’s telematics unit, while sufficient for demonstrating that a lowspeed CAN device could compromise a high-speed CAN
device via the telematics unit, is also limiting. Specifically,
while that attack code is running, the telematics service is
not. A more sophisticated attack could implant malicious
code within the telematics environment itself (either in
RAM or by re-flashing the unit). Doing so would allow
the malicious code to co-exist with the existing telematics software (we have built such code in the lab). The
result provides the attack software with a rich Unix-like
environment (our car’s telematics unit uses the QNX Neutrino Real-Time Operating System) and provides standard
interfaces to additional hardware capabilities (e.g., GPS,
audio capture, cellular link) and software libraries (e.g.,
OpenSSL).
Hosting our own code within a car’s ECU enables yet
another extension to our attacks: complicating detection
and forensic evaluations following any malicious action.
For example, the attack code on the telematics unit could
perform some action (such as locking the brakes after
detecting a speed of over 80 MPH). The attack code could
then erase any evidence of its existence on the device. If
the attack code was installed per the method described in
Section VI-B, then it would be sufficient to simply reboot
the telematics unit, with the only evidence of something
potentially amiss being the lack of telematics records during
the time of the attack. If the attack code was implanted
within the telematics environment itself, then more sophisticated techniques may be necessary to erase evidence of
the attack code’s existence. In either case, such an attack
could complicate (or even prevent) a forensic investigation
of a crash scene. We have experimentally verified the
efficacy of a safe version of this attack while driving on
a runway: after the car reaches 20 MPH, the attack code on
the telematics unit forces the car’s windshield fluid pump
and wipers on. After the car stops, the attack code forces

the telematics unit to reboot, erasing any evidence of its
existence.
VII. D ISCUSSION AND C ONCLUSIONS
Although we are not the first to observe that computerized
automotive systems may present new risks, our empirical
approach has given us a unique perspective to reflect on the
actual vulnerabilities of modern cars as they are built and
deployed today. We summarize these findings here and then
discuss the complex challenges in addressing them within
the existing automotive ecosystem.
• Extent of Damage. Past work, e.g., [19], [24], [26],
[27], [28], discuss potential risks to cyber-physical
vehicles and thus we knew that adversaries might be
able to do damage by attacking the components within
cars. We did not, however, anticipate that we would be
able to directly manipulate safety critical ECUs (indeed,
all ECUs that we tested) or that we would be allowed
to create unsafe conditions of such magnitude.
•

Ease of Attack. In starting this project we expected
to spend significant effort reverse-engineering, with
non-trivial effort to identify and exploit each subtle
vulnerability. However, we found existing automotive
systems — at least those we tested — to be tremendously fragile. Indeed, our simple fuzzing infrastructure
was very effective and to our surprise, a large fraction
of the random packets we sent resulted in changes
to the state of our car. Based on this experience, we
believe that a fuzzer itself is likely be a universal
attack for disrupting arbitrary automobiles (similar to
how the “crashme” program that fuzzed system calls
was effective in crashing operating systems before the
syscall interface was hardened).

•

Unenforced Access Controls. While we believe that
standard access controls are weak, we were surprised
at the extent to which the controls that did exist were
frequently unused. For example, the firmware on an
ECU controls all of its critical functionality and thus the
standard for our car’s CAN protocol variant describes
methods for ECUs to protect against unauthorized
firmware updates. We were therefore surprised that
we could load firmware onto some key ECUs, like
our telematics unit (a critical ECU) and our Remote
Control Door Lock Receiver (RCDLR), without any
such authentication. Similarly, the protocol standard
also makes an earnest attempt to restrict access to
DeviceControl diagnostic capabilities. We were therefore also surprised to find that critical ECUs in our
car would respond to DeviceControl packets without
authentication first.

•

Attack Amplification. We found multiple opportunities
for attackers to amplify their capabilities — either in
reach or in stealth. For example, while the designated

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13

gateway node between the car’s low-speed and highspeed networks (the BCM) should not expose any
interface that would let a low-speed node compromise the high-speed network, we found that we could
maliciously bridge these networks through a compromised telematics unit. Thus, the compromise of any
ECU becomes sufficient to manipulate safety-critical
components such as the EBCM. As more and more
components integrate into vehicles, it may become
increasingly difficult to properly secure all bridging
points.
Finally, we also found that, in addition to being able
to load custom code onto an ECU via the CAN network,
it is straightforward to design this code to completely
erase any evidence of itself after executing its attack.
Thus, absent any such forensic trail, it may be infeasible
to determine if a particular crash is caused by an attack
or not. While a seemingly minor point, we believe
that this is in fact a very dangerous capability as it
minimizes the possibility of any law enforcement action
that might deter individuals from using such attacks.2
In reflecting on our overall experiences, we observe that
while automotive components are clearly and explicitly designed to safely tolerate failures — responding appropriately
when components are prevented from communicating — it
seems clear that tolerating attacks has not been part of the
same design criteria. Given our results and the observations
thus far, we consider below several potential defensive
directions and the tensions inherent in them.
To frame the following discussion, we once again stress
that the focus of this paper has been on analyzing the
security implications if an attacker is able to maliciously
compromise a car’s internal communication’s network, not
on how an attacker might be able to do so. While we
can demonstrably access our car’s internal networks via
several means (e.g., via devices physically attached to the
car’s internal network, such as a tiny “attack iPod” that
we implemented, or via a remote wireless vulnerability
that we uncovered), we defer a complete consideration of
entry points to future work. Although we consider some
specific entry points below (such as malicious aftermarket
components), our discussion below is framed broadly and
seeks to be as agnostic as possible to the potential entry
vector.
Diagnostic and Reflashing Services. Many of the vulnerabilities we discovered were made possible by weak
or unenforced protections of the diagnostic and reflashing
services. Because these services are never intended for
use during normal operation of the vehicle, it is tempting
to address these issues by completely locking down such
capabilities after the car leaves manufacturing. While it
2 As an aside, the lack of a strong forensic trail also creates the possibility
for a driver to, after an accident, blame the car’s computers for driver error.

is clearly unsafe for arbitrary ECUs to issue diagnostic
and reflashing commands, locking down these capabilities
ignores the needs of various stakeholders.
For instance, individuals desire and should be able to
do certain things to tune their own car (but not others).
Similarly, how could mechanics service and replace components in a “locked-down” automotive environment? Would
they receive special capabilities? If so, which mechanics and
why should they be trusted? Consider the recently proposed
“Motor Vehicle Owners’ Right to Repair Act” (H.R. 2057),
which would require manufacturers to provide diagnostic information and tools to vehicle owners and service providers,
and to provide information to aftermarket tool vendors that
enables them to make functionally-equivalent tools. The
motivation for this legislation is clear: encouraging healthy
competition within the broader automotive industry. Even
simple security mechanisms (including some we support,
such as signed firmware updates) can be at odds with the
vision of the proposed legislation. Indeed, providing smaller
and independent auto shops with the ability to service and
diagnose vehicles without letting adversaries co-opt those
same abilities appears to be a fundamental challenge.
The core problem is lack of access control for the use
of these services. Thus, we see desirable properties of a
solution to be threefold: arbitrary ECUs should not be able to
issue diagnostic and reflashing commands, such commands
can only be issued with some validation, and physical access
to the car should be required before issuing dangerous
commands.
Aftermarket Components. Even with diagnostic and
reflashing services secured, packets that appear on the vehicle bus during normal operation can still be spoofed by
third-party ECUs connected to the bus. Today a modern
automobile leaves the factory containing multiple third-party
ECUs, and owners often add aftermarket components (like
radios or alarms) to their car’s buses. This creates a tension
that, in the extreme, manifests itself as the need to either trust
all third-party components, or to lock down a car’s network
so that no third-party components — whether adversarial or
benign — can influence the state of the car.
One potential intermediate (and backwards compatible)
solution we envision is to allow owners to connect an
external filtering device between an untrusted component
(such as a radio) and the vehicle bus to function as a trusted
mediator, ensuring that the component sends and receives
only approved packets.
Detection Versus Prevention. More broadly, certain
considerations unique to cyber-physical vehicles raise the
possibility of security via detection and correction of anomalies, rather than prevention and locking down of capabilities.
For example, the operational and economic realities of
automotive design and manufacturing are stringent. Manufacturers must swiftly integrate parts from different suppliers

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14

(changing as needed to second and third source suppliers) in
order to quickly reach market and at low cost. Competitive
pressures drive vendors to reuse designs and thus engenders
significant heterogeneity. It is common that each ECU
may use a different processor and/or software architecture
and some cars may even use different communications
architectures — one grafted onto the other to integrate a
vendor assembly and bring the car to market in time. Today
the challenges of integration have become enormous and
manufacturers seek to reduce these overheads at all costs —
a natural obstacle for instituting strict security policies.
In addition, many of an automobile’s functions are safety
critical, and introducing additional delay into the processing
of, say, brake commands, may be unsafe.
These considerations raise the possibility of exploring the
tradeoff between preventing and correcting malicious actions: if rigorous prevention is too expensive, perhaps a quick
reversal is sufficient for certain classes of vulnerabilities.
Several questions come with this approach: Can anomalous
behavior be detected early enough, before any dangerous
packets are sent? Can a fail-safe mode or last safe state
be identified and safely reverted to? It is also unclear what
constitutes abnormal behavior on the bus in the first place, as
attacks can be staged entirely with packets that also appear
during normal vehicle operation.
Toward Security. These are just a few of many potential defensive directions and associated tensions. There
are deep-rooted tussles surrounding the security of cyberphysical vehicles, and it is not yet clear what the “right”
solution for security is or even if a single “right” solution
exists. More likely, there is a spectrum of solutions that each
trade off critical values (like security vs. support for independent auto shops). Thus, we argue that the future research
agenda for securing cyber-physical vehicles is not merely to
consider the necessary technical mechanisms, but to also
inform these designs by what is feasible practically and
compatible with the interests of a broader set of stakeholders.
This work serves as a critical piece in the puzzle, providing
the first experimentally guided study into the real security
risks with a modern automobile.
ACKNOWLEDGMENTS
We thank Mike Haslip, Gary Tomsic, and the City of
Blaine, Washington, for their support and for providing access to the Blaine decommissioned airport runway and Mike
Haslip specifically for providing Figure 7. We thank Ingolf
Krueger for his guidance on understanding automotive architectures, Cheryl Hile and Melody Kadenko for their support
on all aspects of the project, and Iva Dermendjieva, Dan
Halperin, Geoff Voelker and the anonymous reviewers for
comments on earlier versions of this paper. Portions of this
work was supported by NSF grants CNS-0722000, CNS0831532, CNS-0846065, CNS-0905384, CNS-0963695, and
CNS-0963702, by a MURI grant administered by the Air

Force Office of Scientific Research, by a CCC-CRA-NSF
Computing Innovation Fellowship, by a Marilyn Fries Endowed Regental Fellowship, and by an Alfred P. Sloan
Research Fellowship.
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