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Bitcoin over Tor isn't a Good Idea

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Bitcoin over Tor isn’t a good idea
Alex Biryukov
University of Luxembourg
Email: alex.biryukov@uni.lu
Ivan Pustogarov
University of Luxembourg
Email: ivan.pustogarov@uni.lu
Abstract—Bitcoin is a decentralized P2P digital currency
in which coins are generated by a distributed set of miners
and transactions are broadcasted via a peer-to-peer network.
While Bitcoin provides some level of anonymity (or rather
pseudonymity) by encouraging the users to have any number
of random-looking Bitcoin addresses, recent research shows that
this level of anonymity is rather low. This encourages users to
connect to the Bitcoin network through anonymizers like Tor and
motivates development of default Tor functionality for popular
mobile SPV clients. In this paper we show that combining Tor
and Bitcoin creates a new attack vector. A low-resource attacker
can gain full control of information flows between all users who
chose to use Bitcoin over Tor. In particular the attacker can
link together user’s transactions regardless of pseudonyms used,
control which Bitcoin blocks and transactions are relayed to
user and can delay or discard user’s transactions and blocks.
Moreover, we show how an attacker can fingerprint users and
then recognize them and learn their IP addresses when they
decide to connect to the Bitcoin network directly.
I. INTRODUCTION
Bitcoin is a decentralized virtual currency and a P2P
payment system in which coins are generated by miners and
double spending is prevented by that each peer keeps a local
copy of the constantly growing public ledger of all the previous
transactions. Though the original Bitcoin paper states that
privacy in such a system may still be maintained, the recent
findings disprove this. Anonymity and privacy of the plain
Bitcoin protocol is also not claimed by the Bitcoin developers.
There are two independent problems: a) ability of the
attacker to link transactions to the IP address of the
user [2], [15], [14] by studying connectivity and traffic of
the peers and b) linkability of the user’s pseudonyms and
transactions in the public ledger achieved via transaction flow
analysis [20], [17]. At the same time as Bitcoin increases its
user base and moves from mining and hoarding to the actual
use as a currency and payment protocol in various on-line
applications there is a growing demand in more privacy among
the Bitcoin users. While one could use a Bitcoin mixing ser-
vice1to break connections in the transaction graph, IP address
leakage is still possible. Bitcoin developers recommend to use
third party anonymization tools like Tor or VPNs to solve this
problem.
Some alternative currencies like Anoncoin, BitTor, Torcoin,
Stealthcoin, and others offer native support for Tor. There are
also several other use cases for Tor in the Bitcoin ecosystem.
For mobile payments it is of interest to use so called SPV
(simple payment verification) clients which cannot afford to
hold the full 20 Gbyte blockchain ledger. Such feature was
1This is always a matter of trust of the service operator.
already foreseen in the original Bitcoin whitepaper, see Sec-
tion 8 of [18]. Since such popular clients [5], [11] (around
1 Million expected userbase [13]) are vulnerable to spoofing
attacks which may result in double-spending (see appendix
B), the current trend is to bundle them with Tor by default to
avoid spoofing2and man-in-the-middle attacks. Tor can also
be a solution for services and online shops that want to prevent
DoS attacks against their public IP. Finally Tor is seen as a
countermeasure if Internet neutrality towards Bitcoin will start
to erode [7].
Tor is not a panacea however and not all applications
are anonymized equally well when combined with Tor. The
biggest effort has been made so far on improving protection
of the HTTP(S) protocol on top of Tor. Other protocols
are not researched that well. There were several documented
cases when application level leaked crucial user-identifying
information [16], [23]. Moreover, there is only limited number
of applications which are studied well enough to be considered
safe to use with Tor [25].
This paper contains two main contributions: first we show
that using Bitcoin through Tor not only provides limited level
of anonymity but also exposes the user to man-in-the-middle
attacks in which an attacker controls which Bitcoin blocks
and transactions the user is aware of. The estimated cost of
the attack is below 2500 USD per month.
The second main contribution is a fingerprinting technique
for Bitcoin users by setting an “address cookie” on user’s
computer. This can be used to correlate the same user across
different sessions, even if he uses Tor, hidden-services or
multiple proxies. If the user later decides to connect to the
Bitcoin network directly the cookie would be still present and
would reveal his IP address. A small set of Sybil nodes (about
a 100 attacker’s nodes) is sufficient to keep the cookies fresh
on all the Bitcoin peers (including clients behind NATs).
The man-in-the-middle attack exploits a Bitcoin built-in
reputation based DoS protection and the attacker is able to
force specific Bitcoin peers to ban Tor Exit nodes of her choice.
Combining it with some peculiarities of how Tor handles
data streams a stealthy and low-resource attacker with just
1-3% of overall Tor Exit bandwidth capacity and 1000-1500
cheap lightweight Bitcoin peers (for example, a small Botnet)
can force all Bitcoin over Tor traffic to go either through
her Exit nodes or through her peers. This opens numerous
attack vectors. First, it simplifies traffic correlation attack since
the attacker controls one end of the communication. Second,
the attacker can glue together different Bitcoin addresses
(pseudonyms) of the same user. Third, it opens possibilities of
2E.g. when connecting through a public Wi-Fi.
2015 IEEE Symposium on Security and Privacy
© 2015, Alex Biryukov. Under license to IEEE.
DOI 10.1109/SP.2015.15
122
2015 IEEE Symposium on Security and Privacy
© 2015, Alex Biryukov. Under license to IEEE.
DOI 10.1109/SP.2015.15
122
double spending attacks for mobile SPV clients, those which
it was supposed to protect from such attacks. Moreover in
collusion with a powerful miner double-spending becomes
possible and a totally virtual Bitcoin reality may be created
for such users (at least for a brief period of time).
The rest of the paper is organized as follows. In Section II,
we provide information on Bitcoin and Tor internals required
for understanding the attacks. In Section III we describe how
an attacker can get in the middle between Bitcoin clients and
Bitcoin network, effectively isolating the clients from the rest
of the Bitcoin P2P network. We also show that Bitcoin peers
available as Tor hidden services may not solve the problem.
In Section IV, we show how a user can be fingerprinted
and his activity linked across different sessions. Section V
describes how the attacker can increase the probability that
a user connecting to the Bitcoin network directly (i.e. without
using Tor) will choose her peers. In Section VI, we analyze
the man-in-the-middle attack and estimate connection delays
experienced by the user and check for which malicious Exit
bandwidth and number of malicious peers the attack becomes
unnoticeable to the user. Section VII calculates the costs
of the attack. In Section VIII, we describe several possible
countermeasures.
Ethical considerations. We reported the attacks described
in this paper to the Bitcoin developers. In addition we submit-
ted a patch which fixes the “address cookie” attack. Though
we carried out some experiments using our own relays in the
real Bitcoin and Tor networks we believe that no users were
harmed since the relays were limited in bandwidth and were
running only for a short time. Address cookie fingerprinting
was tested only on our own clients/transactions.
II. BACKGRO UND
In this section we provide details of the inner working of
Tor and Bitcoin protocols. Many of these details were obtained
by an analysis of the corresponding source code3. This is
especially true for Bitcoin for which there exists no official
documentation except for the original white paper [18] and
Bitcoin Wiki [4].
A. Bitcoin
Bitcoin is a decentralized virtual currency and a payment
system based on cryptography and a peer-to-peer network.
Its main components are transactions and blocks. Blocks are
created by Bitcoin miners by solving cryptographic puzzles
of controlled hardness (called proofs of work). The proof of
work consists of finding a cryptographic hash value for a
block of transactions which starts with a certain number of
leading zero bits (32 when Bitcoin was first proposed, 67
zero bits at present). With each solved block a miner creates
and earns 25 new Bitcoins. Hash of the previous block is
included into the new block, which results in a chain of blocks
or blockchain. The difficulty of the cryptographic puzzles is
adjusted automatically by the network so that the network
generates one block every 10 minutes on the average. Payers
and payees of the system are identified by Bitcoin addresses
which are base58-encoded hashes of their public keys. Money
transfers from one Bitcoin address to another are done by
3Bitcoin version 0.9.2 and Tor version 0.2.4.23
creating a signed transaction and broadcasting it to the P2P
network. Transactions are included into blocks by miners; once
a transaction is buried under a sufficient number of blocks, it
becomes computationally impractical to double spend coins in
this transaction.
Bitcoin is a peer-to-peer system where each peer is sup-
posed to keep its copy of the blockchain, which plays a role of
a public ledger. Whenever a block or a transaction is generated
by a peer, it is broadcasted to other peers in the network.
Upon receipt and verification of the block’s proof of work
the peer updates his copy of the blockchain. Bitcoin software
does not explicitly divide its functionality between clients and
servers, however Bitcoin peers can be grouped into those which
accept incoming connections (servers) and those which don’t
(clients), i.e. peers behind network address translation (NAT)
or firewalls. Bitcoin users connecting to the Bitcoin network
through Tor or VPN obviously also do not accept incoming
connections.
At the time of writing there are about 7,000 reachable
Bitcoin servers [6] and the number of clients is estimated
to be an order of magnitude larger [8]. By default Bitcoin
peers (both clients and servers) try to maintain 8 outgoing
connections to other peers in the network. If any of the 8
outgoing connections drop, a Bitcoin peer tries to replace them
with new connections. Using our terminology, a Bitcoin client
can only establish a connection to a Bitcoin server. We call
servers to which a client established connections entry nodes of
this client. By default a server can accept up to 117 incoming
connections. If this limit is reached all new connections are
dropped.
1) Bitcoin anti-DoS protection: As an anti-DoS protection,
Bitcoin peers implement a reputation-based protocol with each
node keeping a penalty score for every other Bitcoin peer
(identified by its IP address). Whenever a malformed message
is sent to the node, the latter increases the penalty score
(different messages incur different penalties) of the sender
and bans the “misbehaving” IP address for 24 hours when
the penalty reaches the value of 100.
2) Bitcoin peers as Tor hidden services: Tor hidden ser-
vices (see section II-B3) are service-agnostic in the sense that
any TCP-based service can be made available as a Tor hidden
service. This is used by Bitcoin which recognizes three types
of addresses: IPv4, IPv6, and OnionCat [19]. Onioncat address
format is a way to represent an onion address as an IPv6
address: the first 6 bytes of an OnionCat address are fixed
and set to FD87:D87E:EB43 and the other 10 bytes are the
hex version of the onion address (i.e. base32 decoded onion
address after removing the “.onion” part).
3) Bitcoin peer discovery and bootstrapping: Bitcoin im-
plements several mechanisms for peer discovery and bootstrap-
ping. First, each Bitcoin peer keeps a database of IP addresses
of peers previously seen in the network. This database survives
between Bitcoin client restarts. This is done by dumping the
database to the hard drive every 15 minutes and on exit (as
we will see later this facilitates setting a cookie on the user’s
computer). Bitcoin peers periodically broadcast their addresses
in the network. In addition peers can request addresses from
each other using GETADDR messages and advertise addresses
using ADDR messages.
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If Tor is not used, when a Bitcoin clients starts, it first
tries to populate its address database by resolving 6 hard-coded
hostnames4. If Tor is used, Bitcoin does not explicitly ask Tor
to resolve5them but rather asks it to establish connections to
these hostnames.
If Tor is not used, the addresses for outgoing connections
are taken from the addresses database only. In case Tor is used,
every second connection is established to a DNS hostname.
These DNS hostnames are called “oneshots” and once the
client establishes a connection to such a hostname it requests
a bunch of addresses from it and then disconnects and never
tries to connect to it again. As a fallback if no addresses can
be found at all, after 60 seconds of running the Bitcoin client
uses a list of 600 hard-coded IP addresses.
Bitcoin nodes recognize three types of addresses: IPv4,
IPv6, and OnionCat [19]. For each type of addresses the
peer maintains a state variable indicating if the Bitcoin node
is capable of using such address type. These state variables
become important when using Tor: the only address type which
is accepted from other peers is OnionCat type. Curiously,
this results in that all IPv4 and IPv6 addresses obtained
from oneshots are dropped and the client uses its original
database. The opposite case also holds: if Tor is not used,
onion addresses are not stored in the address database.
Finally each address is accompanied by a timestamp which
determines its freshness.
4) Choosing outgoing connections: For each address in
the address database, a Bitcoin peer maintains statistics which
among other things includes when the address was last seen
in the network, if a connection to this address was ever
established before, and the timestamp of such connection. All
addresses in the database are distributed between so called
buckets. There are 256 buckets for “new” addresses (addresses
to which the Bitcoin client has never established a connection)
and 64 for “tried” addresses (addresses to which there was
at least one successful connection). Each bucket can have at
most 64 entries (which means that there can be at most 20480
addresses in the database). When a peer establishes outgoing
connections, it chooses an address from “tried” buckets with
probability p=0.90.1n, where nis the number of already
established outgoing connections. If an address is advertised
frequently enough it can be put into up to 4 different “new”
buckets. This obviously increases its chances to be selected by
a user and to be transferred to a “tried” bucket.
B. Tor
Tor is the most popular low-latency anonymity network
which at the time of this writing comprised 6000-7000 routers
with an estimated number of daily users exceeding 500,000
(not counting the botnet-infected nodes). Tor is based on ideas
of onion routing and telescoping path-building design. When a
user wants to connect to an Internet server while keeping his IP
address in secret from the server he chooses a path consisting
of three Tor relays (called Guard,Middle and Exit), builds a
4At the time of this writing one of these hostnames constantly failed to
resolve into any IP address.
5When applications communicate with Tor they can either ask Tor to
establish a connection to a hostname by sending a CONNECT command or
to resolve a hostname by sending a RESOLVE command.
circuit and negotiates symmetric keys with each one of them
using the telescoping technique. Before sending a message to
the server, the user encrypts it using the negotiated keys. While
the message travels along the circuit, each relay strips off its
layer of encryption. In this way the message arrives at the final
destination in its original form and each party knows only the
previous and the next hop.
Tor tries hard to achieve low traffic latency to provide a
good user experience, thus sacrificing some anonymity for
performance. To keep latency low and network throughput
high, Tor relays do not delay incoming messages and do not
use padding. This makes Tor susceptible to traffic confirmation
attacks: if an attacker is able to sniff both ends of a communi-
cation, she is able to confirm that the user communicated with
the server. If the first hop of a circuit is chosen at random then
the probability that a malicious node will be chosen as the first
hop (and thus will know the IP address of the user) converges
to one with the number of circuits. Due to this, each user has
a set of three6Guard nodes. When a user builds a circuit the
first hop is chosen from the set of Guard nodes.
The list of all Tor relays is assembled and distributed in the
so called consensus document by nine trusted Tor authorities.
For the purpose of traffic balancing the bandwidth of each relay
is measured and reported. A user chooses relays for his circuits
with probability proportional to the relays’ weights listed in
the consensus7. Each relay in the consensus is identified by
his fingerprint (or ID) which is the SHA-1 hash of its public
key.
1) Tor stream timeout policy: Tor provides SOCKS in-
terface for applications willing to connect to the Internet
anonymously. Each connection to the SOCKS port by an
application is called a stream. For each new stream Tor tries to
attach it either to an existing circuit or to a newly built one. It
then sends a BEGIN cell down the circuit to the corresponding
Exit node asking it to establish a connection to the server
requested by the application. In order to improve user’s quality
of service, if Tor does not receive a reply from the Exit node
within 10 or 15 seconds8, it drops the circuit and tries another
one. If none of the circuits worked for the stream during 2
minutes, Tor gives up on it and sends a SOCKS general failure
error message.
2) Tor Exit policy: In order to access a Web resource
anonymously through a Tor circuit, the Exit relay (the final
relay in the circuit) should allow establishing connections
outside the Tor network. This makes Exit relay operators open
to numerous abuses. In order to make their life easier, Tor
allows them to specify an Exit Policy: a list of IP addresses
and ports to which the Exit node is willing to establish
connections and which destination are prohibited. When a
client establishes a circuit, he chooses only those Exit nodes
which allow connections to the corresponding IP addresses9
and port ranges.
6Will be reduced down to one Guard per user in the next Tor update [24].
7This is a simplification of the real selection procedure in which additional
weights are assigned to a relay based on its position in the circuit and its flags.
8Tor waits for 10 seconds for the first two attempt and 15 seconds for the
subsequent attempts.
9Note that usually at the time the path is selected, only the domain name
is known.
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3) Tor Hidden Services: Tor is mostly known for its ability
to provide anonymity for clients accessing Internet services.
Tor Hidden Services are another feature of Tor which enables
responder anonymity: a service can be contacted by clients
without revealing its physical location. In order to achieve this
a client and the hidden service choose at random and connect to
a Tor relay (rendezvous point) and forward all the data through
it. In more detail:
1) The hidden service generates a public key and
chooses at random a small number of Tor relays (typ-
ically three) which become its introduction points.
The service maintains permanent connection to these
relays.
2) It then generates an HS descriptor which contains the
public key and the list of introduction points, and
3) Publishes it at 6 different Tor relays having HSDir
flag10. These are called responsible HS directories.
The choice of responsible HS directories is determin-
istic and depends on the hash of the hidden service’s
public key and current day.
4) Introduction points are instructed by the hidden ser-
vice to forward connection requests from clients. The
base32 encoding of the hash of the hidden service’s
public key (onion address) is then communicated
to clients by conventional means (blog post, e-mail,
etc.).
When a client decides to connect to the hidden service, he:
1) Determines the list of the responsible HS directories
using the onion address and downloads the HS de-
scriptor.
2) Chooses a rendezvous point at random.
3) Communicates the ID of the rendezvous point to
the hidden service’s introduction points which then
forward it to the hidden service.
When the hidden service receives the ID of the rendezvous
point, it establishes a connection to it and the data transfer
between the service and the client can start. All communica-
tions between the client and the rendezvous point, between the
service and the rendezvous point and between the service and
the introduction points are established over three-hop circuits.
This hides the location of the hidden service and its clients
both from each other and from external observer.
The hidden service or a client can determine the finger-
prints of the responsible directories as follows. They first take
all Tor relays which have HSDir flag in the consensus and
sort their fingerprints in lexicographical order. Second, they
compute the descriptor ID’s of the hidden service which is the
SHA-1 hash of a value composed of the following items11:
public key of the hidden service, current day, and replica
(which can be 0 or 1). The exact expression for the ID is
of little importance here, the only important things are a) the
ID changes every 24 hours, b) there are two replicas of the ID.
10HSDir flag is assigned by Tor authorities to relays which wish to be a
part of a distributed database to store descriptors of Tor hidden services. A
relay should be running for at least 25 hours to get this flag.
11A hidden service may also decide to use a secret key (somewhat
misleadingly called descriptor-cookie), but for hidden services which are
meant to be accessed by everybody it is not relevant.
Third they find the place in the sorted list of the fingerprints
for the computed ID and take the next three relays’ fingerprints
(thus having 6 fingerprints it total since there are two replicas).
III. GETTING IN THE MIDDLE
By exploiting Bitcoin’s anti-DoS protection a low-resource
attacker can force users which decide to connect to the Bitcoin
network through Tor to connect exclusively through her Tor
Exit nodes or to her Bitcoin peers, totally isolating the client
from the rest of the Bitcoin P2P network. This means that
combining Tor with Bitcoin may have serious security impli-
cations for the users: 1) they are exposed to attacks in which
an attacker controls which Bitcoin blocks and transactions the
users are aware of; 2) they do not get the expected level of
anonymity.
The main building blocks of the attack are: Bitcoin’s
reputation-based anti-Dos protection, Tor’s stream manage-
ment policy, the fact that connections between Bitcoin peers
are not authenticated. Authors in [2] exploited the Bitcoin’s
reputation-based DoS protection to force all Bitcoin servers to
ban all Tor Exit nodes. In this section we exploit the DoS
protection, however we noticed that instead of just baning
Bitcoin clients from using Tor the attacker might achieve much
smarter results. The attack consists of four steps:
Inject a number of Bitcoin peers to the network.
Note that though Bitcoin allows only one peer per
IP address, it does not require high bandwidth. IP
addresses can be obtained relatively cheaply and on
per-hour basis.
Periodically advertise the newly injected peers in the
network so that they are included into the maximum
possible number of buckets at the client side.
Inject some number of medium-bandwidth Tor Exit
relays. Even a small fraction of the Exit bandwidth
would be enough for the attacker as will be shown
later.
Make non-attacker’s Bitcoin peers ban non-attacker’s
Tor Exit nodes.
We now explain each step of the attack in more detail. See
section VI for attack parameter estimation.
A. Injecting Bitcoin peers
This step is rather straightforward. In order to comply with
Bitcoin’s limitation “one peer per IP address”, the attacker
should obtain a large number of IP addresses. The easiest way
would be to rent IP addresses on per hour basis. The market
value is 1 cents per hour per IP address [21]. The important
note is that the obtained IP addresses will not be involved in
any abusive activity (like sending spam or DoS attacks) which
makes this part of the attack undetectable.
B. Advertising malicious peers
The attacker is interested in that her Bitcoin peers are
chosen by Bitcoin clients as frequently as possible. In order to
increase by factor four the chances for her peers to be included
into “tried” buckets, the attacker should advertise the addresses
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of her peers as frequently as possible. This mechanism would
allow the attacker to inject less malicious peers. Note also
that address advertisement is not logged by default and thus
requires special monitoring to be noticed.
C. Injecting Tor Exit nodes
During this step the attacker runs a number of Exit Tor
nodes. In order to get Exit flag from the Tor authorities, an
attacker’s Exit node should allow outgoing connections to any
two ports out of ports 80, 443, or 6667. Such an open Exit
policy might not be what a stealthy attacker wants. Fortunately
for the attacker she can provide incorrect information about her
exit policy in her descriptor and thus have Exit flag while in
reality providing access to port 8333 only. The attacker can
do even better, and dynamically change the exit policy of her
relays so that only connections to specific Bitcoin peers are
allowed. We implemented this part of the attack: while the Tor
consensus indicated that our relays allowed exiting on ports 80,
443, and 8333 for any IP address, the real exit policy of our
relays was accepting port 8333 for a couple of IP addresses12.
D. Banning Tor Exit nodes
In this phase, the attacker exploits the built-in Bitcoin anti-
DoS protection. The attacker chooses a non-attacker’s Bitcoin
peer and a non-attacker’s Tor Exit, builds a circuit through
this Exit node and sends a malformed message to the chosen
Bitcoin peer (e.g. a malformed coinbase transaction which is
60 bytes in size and which causes the immediate ban for 24
hours). As soon as the Bitcoin peer receives such message it
analyses the sender’s IP address which obviously belongs to
the Tor Exit node chosen by the attacker. The Bitcoin peer
then marks this IP address as misbehaving for 24 hours. If a
legitimate client then tries to connect to the same Bitcoin peer
over the banned Exit node, his connection will be rejected.
The attacker repeats this step for all non-attacker’s Bitcoin
peers and each non-attacker’s Tor Exit node. This results
in that a legitimate Bitcoin user is only able to connect to
Bitcoin over Tor if he chooses either one of the attacker’s
peers or establishes a circuit through an attacker’s Exit node.
We validated this part of the attack by forcing about 7500
running Bitcoin peers to ban our Exit node. To do this we
implemented a rudimentary Bitcoin client which is capable of
sending different custom-built Bitcoin messages.
E. Defeating onion peers
Bitcoin peers can be made reachable as Tor hidden services.
Banning Tor Exit nodes will obviously not prevent Bitcoin
clients from connecting to such peers. Nonetheless our obser-
vations show that this case can also be defeated by the attacker.
First the current design of Tor Hidden Services allows a
low-resource attacker to DoS a hidden service of her choice [3]
(this technique is called black-holing of hidden services).
Before a client can contact a hidden service he needs to
download the corresponding descriptor from one of the six
responsible hidden service directories. These directories are
chosen from the whole set of Tor relays in a deterministic way
based on the onion address and current day (see section II-B3).
12We also allowed exiting to IP addresses used by Tor bandwidth scanners.
The attacker needs to inject six malicious relays that would
become responsible directories. In other words she needs to
find the right public keys with fingerprints which would be
in-between the descriptor IDs of the hidden service and the
fingerprint of the currently first responsible hidden service
directory. Authors in [3] show that computationally it is easy
to do. It can become a problem though for a large number of
hidden services: for each hidden service the attacker needs to
run at least 6 Tor relays for at least 25 hours, 2 relays per IP
address.
Fortunately for the attacker the fraction of Bitcoin peers
available as Tor hidden services is quite small. During Au-
gust 2014 we queried address databases of reachable Bitcoin
peers [6] and among 1,153,586 unique addresses (port numbers
were ignored), only 228 were OnionCat addresses and only 39
of them were actually online; in November 2014 we repeated
the experiment and among 737,314 unique addresses 252 were
OnionCat addresses and 46 were online (see Appendix A for
the two lists of these Bitcoin onion addresses). This results
in (1) a very small probability for a client to choose a peer
available as a hidden service; (2) this makes black-holing of
existing Bitcoin hidden services practical.
Second, the attacker can at almost no cost inject a large
number of Bitcoin peers available as Tor hidden services. It
requires running only one bitcoind instance and binding it
with as many onion addresses as needed. Thus users will more
likely connect to attacker controlled “onion” peers.
Third, as was described in section II-A3, when running
Bitcoin without Tor, onion addresses received from peers are
silently dropped. Thus one can only obtain OnionCat addresses
by either connecting to an IPv4- or IPv6-reachable peers
through a proxy13 or by specifying an onion address in the
command line.
F. Attack vectors
The technique described in this section allows an attacker
to direct all Bitcoin-over-Tor traffic through servers under her
control. This creates several attack vectors which we will
briefly describe in this subsection.
Traffic confirmation attack. First, it becomes much cheaper
to mount a successful traffic confirmation attack. In traffic
confirmation attacks, the attacker controls a fraction of Guard
and Exit nodes. The attacker sees that one of her exit nodes
is requested to access a particular (e.g. censored) web-site and
the attacker is interested in finding out the user who made
this request. The attacker sends a traffic signature down the
corresponding circuit. If the attacker was lucky and the user
chose one of her Guard nodes, the attacker will see the traffic
signature going through this Guard to the target user. This
reveals the user’s IP address.
The success probability of the attack is computed as the
product of two factors: the probability for the user to choose
an attacker’s Guard and the probability for the user to choose
an attacker’s Exit. Since now all exit Bitcoin-over-Tor traffic
goes through the attacker, the second factor becomes 1.
Revealing Guard nodes. In case the attacker does not control
the user’s Guard node, he may try to find this Guard. We
13Not necessarily Tor.
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assume that the attacker controls a fraction of middle nodes.
As before the attacker would send a traffic signature down
the circuit and if none of the attacker’s middle nodes detects
this signature, the attacker drops the circuit. This will force
the user to build another circuit. After some number of circuit
tries, one of the attacker’s middle nodes will finally be chosen.
This middle node will know the user’s Guard node. The re-
identification of the user between different circuits is possible
e.g. using the fingerprinting technique from section IV.
Revealing the guards does not immediately allow an attacker
to reveal the location of the user but gives her the next point of
attack. Given that guard nodes are valid for more than a month,
this may be sufficient to mount a legal attack to recover traffic
meta data for the guard node, depending on the jurisdiction
the guard node is located in.
Linking different bitcoin addresses. Even without knowing
the user’s IP, the attacker can link together user’s transactions
regardless of pseudonyms used.
Possibility of double spending. Finally, after successfully
mounting the attack described in this section the attacker
controls the connectivity to the Bitcoin network for users
which chose to use Tor. This increases the success rate of
double-spend attacks described in Appendix B.
In addition the attacker can defer transactions and blocks and
send dead forks. In collusion with a powerful mining pool (for
example 10-20% of total Bitcoin mining capacity) the attacker
can create fake blocks. This enables additional possibilities for
double spending, however to make this relevant the amount
should exceed what such miner would be able to mine in
the real Bitcoin network. Also complete alternative Bitcoin
reality for all the users who access Bitcoin solely through Tor
is possible. This however would come at a cost of 5-10 times
slower confirmations, which after some time can be detected
by the wallet software.
IV. USER FINGERPRINTING
In this section we describe a technique which can be used
to fingerprint Bitcoin users by setting an “address cookie”
on their computers. A cookie can be set and checked even
when the user connects to the Bitcoin network through Tor or
through a chain of proxies. It can be used to correlate different
transactions of the same user even across different sessions (i.e.
after his computer was rebooted). If the user decides later to
send a non-sensitive transaction without Tor, his fingerprint
can be correlated to his IP address, thus deanonymizing all
his transactions sent previously through Tor. The fingerprinting
technique is based on the Bitcoin’s peer discovery mechanism.
More specifically on that a Bitcoin peer stores addresses
received from other peers and on that his database can be
queried.
As was described in section II-A3 whenever a peer receives
an unsolicited ADDR message, it stores the addresses from
this message in his local database. The attacker can use this
fact as follows. When a client connects to an attacker’s peer,
the peer sends him a unique combination of possibly fake
addresses (address cookie) or fingerprint (we will use these
two terms interchangeably below). Unique non-existent peer
addresses work best, however a more sophisticated and more
stealthy adversary may use existing Bitcoin peer addresses
as well (exploiting the combinatorics of the coupon collector
problem). The client stores these addresses and the next time
he connects to (another) malicious peer, the peer queries his
address database. If the fingerprint addresses are present in the
set of retrieved addresses, the attacker identifies the user.
Consider a user Cand a set of Bitcoin servers E1, ..., Ek
controlled by an attacker. Assume that one of the attacker’s
servers Elis among the user’s entry nodes. The attacker
executes the following steps:
1) Send a number of GETADDR messages to the user. The
user should reply with ADDR messages.
2) Check the received from the client addresses if they
already contain a fingerprint. If the user already has
a fingerprint, stop. Otherwise go to the next step.
3) Generate a unique combination of Nfake addresses
FP and send them in an ADDR message to the
client. The ADDR message should contain at least 11
addresses so that it is not forwarded by the client.
If Nis less than 11, pad the message with 11 N
legitimate14 addresses.
4) If the user connects to the Bitcoin network directly
(i.e. without Tor), store the correspondence between
the client’s IP address and his fingerprint as a tuple
(FP,IP
C). If the user connects through Tor save him
as (FP,NIL).
There is a detail of the Bitcoin protocol which an attacker
should take into account. As was described in subsection II-
A1, when a client connects to the Bitcoin network over Tor,
he will accept and store in his database OnionCat addresses
only (thus ignoring IPv4 addresses). It means that in case of
Tor, the fingerprint generated by the attacker should consist
of OnionCat addresses only. On the other hand when a client
connects to the network directly, he will ignore non-IPv4/IPv6
addresses. Hence an attacker should generate a fingerprint
consisting of IPv4 addresses only. This results in that an
attacker needs to store 2 different types of cookies: OnionCat
and IPv4. At the same time, a client does not limit the types
of addresses he sends as a reply to a GETADDR message. This
means that once a cookie was set it can be queried both over
Tor and directly.
A. Stability of a Cookie
According to the Bitcoin core [10] source code, at the
startup when a client establishes outgoing connections he sends
GETADDR messages, and gets back a set of addresses (typically
2,500, the maximum possible number per GETADDR request).
Given 8 outgoing connection, the client will receive up to
20,000 non-unique addresses. These addresses can potentially
overwrite the address cookie previously set by an attacker.
Below we will try to estimate how this affects the stability of
the cookie. Assume that an attacker managed to set an address
cookie on a user’s computer and disconnected (e.g. the client
ended the session). The client then establishes a new session
sometime later.
First note that if the user reconnects to Bitcoin over Tor
and if the attacker has mounted the attack from section III, he
14By legitimate we mean that there are some Bitcoin servers running at
these addresses.
127127
controls all user’s traffic and the cookie is preserved. Let us
now describe what happens if the client decides to connect to
the Bitcoin network directly.
When a client receives an address IPin he first checks
if it is already contained in his database. If yes, he does
nothing (thus the cookie is not damaged). In case it is a
new IP address the client executes the following procedure.
He computes the bucket number (see section II-A4) based on
the peer which sent the address and the address itself. If this
bucket contains a “terrible”15 address IPterrible, it is replaced
by IPin. Otherwise 4 random addresses are chosen from the
bucket and the one with the oldest timestamp is replaced by
IPin.
In other words, in order for the incoming address IPin to
replace a cookie address IPcookie16 the following conditions
should hold:
1) IPin should not be in the user’s database;
2) IPin should belong to the same bucket Bas IPcookie
and there should be no “terrible” addresses in B;
3) IPcookie should be among the four randomly chosen
addresses, and its timestamp should be the oldest.
These conditions as we will see below make the attacker’s
cookie quite stable for many hours (this also depends on the
number of user sessions since at each startup the address
database is refreshed).
In order to estimate the probability that a cookie address set
by the attacker is preserved we conducted the following exper-
iment. In November 2014 we queried running Bitcoin servers
by sending them GETADDR messages. We received 4,941,815
address-timestamp pairs. Only 303,049 of the addresses were
unique. This can be interpreted as that only about 6% of the
addresses received by a client will not be already contained in
his database (if the client re-connects immediately).
As the second step, we looked at the timestamp distribution
of the non-unique address set. This distribution can serve
as approximation of the distribution of address timestamps
of a client’s database. The results are shown in Table I:
89% of addresses had a timestamp more than 3 hours in the
past. Taking into account conditions stated above, it almost
guarantees that the attacker’s cookie will not be damaged
within the first 3 hours. For 45% of addresses the timestamp
was older than 10 hours (which is the duration of a working
day); 9% of addresses were older than 1 week.
The results above could be summarized as follows: (1)
there is a high chance that an address received by a client will
already be contained in his database, which keeps the cookie
intact; (2) if a cookie IP address is among the 4 nominees for
erasing, it is likely that its timestamp will be fresher than that
of at least one of other nominees (and thus will not be erased).
Finally we conducted the following experiment. We set a
cookie consisting of 100 IPv4 addresses and monitored how
stable this cookie was across different sessions. Table II shows
15An address is called terrible if any of the following holds: 1) its timestamp
is 1 month old or more than 10 minutes in the future; 2) 3 consecutive
connections to this address failed.
16A cookies consists of several IP address, but in order to make the
explanation simpler, we use just one address here.
Address age, hours 1-CDF
3 89%
5 77%
10 45%
15 28%
24 19%
36 15%
48 13%
72 (3 days) 12%
168 (1 week) 9%
TABLE I. COMPLEMENTARY CUMULATIVE DISTRIBUTION FUNCTION
FOR ADDRESSES TIMESTAMPS
the decay rate of the number of cookie addresses over time and
sessions. Note that by session we mean that the client switches
off Bitcoin software and switches it on again, which forces him
to make 8 new outgoing connections and retrieve up to 20,000
addresses.
Session number Time since start, hours Remaining addresses
1 0 100
2 0.5 100
3 1 100
4 1.5 100
5 2 100
6 2.5 100
7 3 98
8 3.5 92
9 5.5 50
10 8 36
TABLE II. ADDRESS COOKIE DECAY RATE (EXAMPLE)
The experiment shows that even after 10 sessions (i.e. after
reception of about 200,000 non-unique IP addresses) and 8
hours, one third of the fingerprint remained in the user’s
database (thus it will be possible to identify the client). Note
that sessions 9 and 10 took 2 and 2.5 hours. On the average
an attacker will need about 90 peers (given that at the time of
writing there are about 7,000 Bitcoin servers) to become one
of the client’s entry nodes during any of these 10 sessions and
update the fingerprint. Running this number of peers will cost
the attacker less than 650 USD per month (see section VII).
In another experiment we checked that in the case of two
sessions with 10 hours between sessions, our client kept 76%
of the initial fingerprint addresses, and in the case of 24
hours between two sessions 55% of the initial fingerprint were
kept (which again allows the user identification). In order to
carry out the experiments from this section we built our own
rudimentary Bitcoin server which is able to connect/accept
connections to/from Bitcoin peers and is capable of send-
ing/receiving different Bitcoin messages on demand. We used
this server as a malicious Bitcoin server which sets new address
cookies and checks previously set cookies. In order to simulate
a user we used the official Bitcoin core software (developed
by the Bitcoin project) [10]. The attack from this section was
experimentally verified by tracking our own clients in the real
Bitcoin and Tor networks.
128128
B. Cookie extraction
The remaining question is how many GETADDR messages
an attacker needs to send to the client to learn that the
database of this client contains a cookie. According to [2],
section 9.2 it can be up to 80 messages to retrieve the full
collection of client’s addresses. However in practice we will
not need to collect all the addresses in a fingerprint, which
significantly reduces the number of requests. About eight
GETADDR messages would be sufficient to retrieve about 90%
of the cookie addresses. This shows that the cookie can be
checked without raising suspicion.
C. Attack vectors
Deanonymization of Bitcoin over Tor users. Consider the
following case. A client uses the same computer for sending
both benign Bitcoin transactions and sensitive transactions. For
benign transactions the user connects to Bitcoin directly, but
for sensitive transactions he forwards his traffic through a chain
of Tor relays or VPNs. If an attacker implements the attack
described in section III, all client’s sensitive transactions with
high probability will go through attacker’s controlled nodes
which will allow her to fingerprint the user and record his
transactions.
When the client later connects to the Bitcoin network directly
to send benign transactions, he will with some probability
choose an entry node controlled by the attacker (in section V
we show how to increase this probability). Once it happens,
the attacker can query the client for the fingerprint and thus
correlate his sensitive transactions with his IP address. Note
that even if the attacker is not implementing the complete man-
in-the-middle attack on Tor, but just injects Sybil peers and
Sybil hidden services she will be able to link many sensitive
transactions to the real IP addresses of users.
Linking different Tor sessions. In the case, when a client uses
a separate computer (or Bitcoin data folder17) to connect to
Bitcoin through Tor, the attacker will not be able to learn his IP
address. However, the attacker will still be able to link different
transactions of the same user (remember that if a client sends
a transaction through Tor the attacker can be certain that it
was generated by this client). This can be done even across
different sessions (computer restarts). This will in turn allow
the attacker to correlate different Bitcoin addresses completely
unrelated via transaction graph analysis.
Domino Effect. Tor multiplexes different streams of the same
user over the same circuits. This means that if the source of one
stream in the circuit is revealed by the fingerprinting attack,
all other streams will also be deanonymized. Specifically, it
is likely that a user who sends a sensitive Bitcoin transaction
through Tor, will also browse a Darkweb site. Similar result
was also noted in [16] but in relation to Bittorrent over Tor
privacy issues. To prevent this it is recommended to enable
option IsolateSOCKSAuth when running Tor (this will pre-
vent sharing circuits with streams for which different SOCKS
authentication was provided).
17A Bitcoin data folder is a directory where Bitcoin clients store their wallets
and dump IP address databases between restarts.
V. LOW-RESOURCE SYBIL ATTACKS ON BITCOIN
In the previous section, we mentioned that a client needs
to connect directly to one of the attacker’s nodes in order to
reveal his IP address so that an attacker can deanonymize his
previous transactions done over Tor. Bitcoin as a peer-to-peer
network is vulnerable to Sybil attacks and just operating many
Bitcoin servers means that a client will sooner or later choose
an entry node controlled by the attacker (i.e. in some number
of sessions). However running too many servers can be costly
(see section VII for attack cost estimation). Fortunately for
the attacker there are a couple of ways to prevent Bitcoin
clients from using non-attacker’s Bitcoin servers (and choose
an attacker’s one instead).
A. Exhausting connections limit
As described in section II, by default a Bitcoin server ac-
cepts up to 117 connections. Once this limit is reached all new
incoming connections are dropped. At the same time a Bitcoin
server neither checks if some of these connections come from
the same IP address18, nor forces clients to provide proof-of-
work. As a result a low-resource attacker can establish many
connections to all but his Bitcoin servers19 and occupy all
free connection slots. If a client connects directly to a Bitcoin
server connection slots of which are occupied, the connection
will be dropped immediately, thus the client will soon end up
connecting to a malicious peer. This straightforward attack has
been known in the Bitcoin community.
B. Port poisoning attack
A less effective but much stealthier new attack exploits
the following fact. Peer addresses are of the following form
(IP,PORT). However when a client decides if to add a received
address to the database, he does not take the port number
into account. For example assume a client receives an address
(IP0,PORT
1)and there is already an entry in the client’s
database (IP0,PORT
0). In such case the client will keep
(IP0,PORT
0)and will not store (IP0,PORT
1).
The attacker can use this fact to flood with clients with ad-
dresses of legitimate Bitcoin servers but wrong port numbers.
If the attacker is the first to send such addresses, the client
will not be able to connect to legitimate nodes.
VI. ESTIMATING CLIENTSDELAYS
The steps described in section III imply that once a client
decides to use Bitcoin network over Tor, he will only be
able to do this by choosing either one of the attacker’s Exit
nodes or one of the attacker’s Bitcoin peers. However for the
attack to be practical a user should not experience significant
increases in connection delays. Otherwise the user will just
give up connecting and decide that Tor-Bitcoin bundle is
malfunctioning. In this section, we estimate the number of
Bitcoin peers and the amount of bandwidth of Tor Exit relays
which the attacker needs to inject, so that the attack does not
degrade the user’s experience.
18One explanation is that if clients are behind the same NAT they will share
the same IP address.
19The list of all running Bitcoin servers can be obtained from e.g. [6].
129129
Once the attacker completes the steps described in sec-
tion III, for each user connecting to the Bitcoin network
through Tor there are several possibilities (see Fig. 1).
1) The user chooses one of the attacker’s Bitcoin peers.
The attacker does nothing in this case: the attacker
automatically gains control over the information for-
warded to the user.
2) The user chooses one of the attacker’s Exit nodes.
The attacker can use the fact that Bitcoin connections
are not encrypted and not authenticated and redirect
the client’s request to Bitcoin peers under her control.
3) The user chooses a non-attacker’s Exit relay and a
running non-attacker’s Bitcoin peer. In this case, due
to the ban the user’s connections will be rejected. And
the user will try to connect to a different Bitcoin peer.
4) The user chooses a non-attacker’s Exit relay and
a non-attacker’s Bitcoin peer which went offline20.
In this case the Bitcoin client will wait until the
connection times-out which can be up to two minutes
(see section II-B1). This delay on the surface looks
like taking prohibitively long time. However since
during these two minutes Tor rebuilds new circuits
every 10-15 seconds, trying new Exits at random, it
actually makes the attacker’s life easier. It increases
the chances that malicious Exit relay will be chosen.
Client 1 Client 2
...
...
1
3
2
Fig. 1. Client’s state after the main steps of the attack
A. Handling unreachable Bitcoin peers
Before estimating the delays we consider case 4 in more
detail. Our experiments show that for a Bitcoin client which
was already used several times prior to the connection over
Tor, the address database contains 10,000 – 15,000 addresses
and the fraction of unreachable Bitcoin peers among them
is between 2/3 and 3/4. Abundance of unreachable addresses
means that case 4 is the most frequent scenario for the client.
20Or never really existed: Bitcoin allows storing fake addresses in client
addresses database.
Consider a client which chose an unreachable Bitcoin server
and a non-attacker’s Exit node.
The Exit relay can send either:21
1) An END cell with a “timeout” error code. In case of a
“timeout” message, Tor sends a “TTL expired” SOCKS error
message to the Bitcoin application which then tries another
Bitcoin peer.
2) An END cell with “resolve failed” error code22. In case of
“resolve failed” message, Tor drops the current circuit and tries
to connect to the unreachable Bitcoin peer through a different
Exit node. After 3 failed resolves, Tor gives up and sends a
“Host unreachable” SOCKS error code, which also results in
Bitcoin trying a different peer.
3) The third and the most common option is that the exit relay
will not send any cell at all during 10-15 seconds. As was
described in the Background section that in case the Exit node
does not send any reply within 10 or 15 seconds (depending
on the number of failed tries) along the circuit attached to the
stream, Tor drops the current circuit and attaches the stream to
another circuit (or to a newly built one if no suitable circuits
exist). In case Tor cannot establish connections during 125
seconds, it gives up and notifies Bitcoin client by sending a
“General failure” SOCKS error message. Bitcoin client then
tries another peer.
B. Estimating delays
The facts that a) Tor tries several different circuits while
connecting to unreachable peers and b) the fraction of unreach-
able peers in the client’s database is very large, significantly
increases the chances that a malicious Exit node is chosen.
The attacker only needs this to happen once, since afterwards
all connections to the other Bitcoin peers will be established
through this Tor circuit; Bitcoin client will work even with one
connection. On the other hand, unreachable nodes increase the
delay before the user establishes its first connection. This delay
depends on the number of attacker’s Bitcoin peers and on how
often the user chooses new circuits.
In order to estimate the latter, we carried out the following
experiment. We were running a Bitcoin client over Tor and
for each connection to an unreachable Bitcoin client we were
measuring the duration of the attempt and the number of
new circuits (and hence different Exit nodes). The cumulative
distribution function of the amount of time a Bitcoin client
spends trying to connect to an unreachable node is shown in
Fig. 2. On the average a Bitcoin peer spends 39.6 seconds
trying to connect to an unreachable peer and tries to establish a
new circuit (and hence a different Exit node) every 8.6 seconds.
This results in 4.6 circuits per unreachable peer on the average.
We now estimate how long it will take a user on the
average to establish his first connection to the Bitcoin network.
This delay obviously depends on the number of the attacker’s
Bitcoin peers and the amount of bandwidth of her Tor Exit
relays. We adopt a simple discrete time absorbing Markov
chain model with only three states (see Fig. 3):
21This is based on the Tor source code analysis and monitoring a running
Tor instance.
22We observed this behaviour not only for hostnames but also for IP
addresses.
130130
0
20
40
60
80
100
0 20 40 60 80 100 120
CDF, %
Time, seconds
Fig. 2. Time spent connecting to an unreachable node
State 1: the Bitcoin client tries to connect to an
unreachable peer;
State 2: the Bitcoin client tries to connect to a reach-
able Bitcoin peer banned by the attacker;
State 3: the Bitcoin client tries to connect to an at-
tacker’s Bitcoin peer or chooses an attacker’s Tor Exit
node. State 3 is absorbing state, once it is reached, the
user thinks that he connected to the Bitcoin network
(while he is now controlled by the attacker).
12
3
0.482 0.336
0.248
0.651
0.270 0.013
Fig. 3. Markov chain with probabilities for 400K of Exit capacity and 100
malicious Bitcoin peers. The client spends about 0.5 seconds in State 2 and
about 40 seconds in State 1
After composing the fundamental matrix for our Markov chain,
we find the average number of steps in two non-absorbing
states. Taking into account the average amount of time spent
by the user in each of the states (we use our experimental data
here), we find the average time before the absorbing state.
We compute this time for different number of Bitcoin peers
and Tor Exit relay bandwidth. The results are presented in
Fig 4. We have taken a conservative estimate that the fraction
of unreachable Bitcoin peers in the client’s database is 2/3 =
66%, also the client spends only about 0.5 seconds in State 2
and about 40 seconds in State 1.
Fig. 4 shows that an attacker having 100,000 of consensus
Exit bandwidth and 1000 Bitcoin peers is able to carry out
the attack while keeping the average delay below 5 minutes.
For example an attacker controlling a small botnet can afford
that many peers (she will need 1000 peers with public IPs
or supporting UPnP protocol). An attacker having consensus
weight of 400,000 and very few peers can decrease the average
delay to about two minutes. Such a bandwidth is achievable
by an economy level attacker as will be shown in section VII.
0
200
400
600
800
1000
20K 50K 100K 200K 400K
Average delay, seconds
Attacker’s aggreated consensus Exit bandwidth
100 peers
1000 peers
1500 peers
2000 peers
4000 peers
Fig. 4. Average time before the first connection
The line corresponding to 4000 attacker’s Bitcoin peers
in Fig. 4 is not as unrealistic as it may seem. Recall (see
Section II-A4) that each Bitcoin peer address can go to up
to 4 “new” buckets at client’s side. This can be used by a
persistent attacker to increase the choice probability for her
peers by a factor 4 (in the best case) which means an attacker
can have significantly less than 4000 peers.
C. Clients with empty addresses cache
As was pointed in Section II-A3, all IPv4 and IPv6 ad-
dresses received from DNS-oneshots are dropped by a Bitcoin
client if Tor is used. If the addresses database of a client is
empty and all the seed nodes are banned, the client can connect
to hidden services only. This is a limitation of our approach.
VII. ATTACK COSTS
A. Tor Exit nodes
During July 2014 we were running a non-Exit Tor relay
for 30 USD per month. We set the bandwidth limit of the
relay to 5 MB/s which resulted in traffic of less than 15GB
per hour. The consensus bandwidth of this relay fluctuated
between 5,000 and 10,000 units23. While the total weighted
consensus bandwidth of all exit nodes was about 7 million
units, the weighted consensus bandwidth of relays allowing
exiting at port 8333 was about 5.7 million units. Assuming
that we could achieve 5,000 – 10,000 units in the consensus
for an Exit node this gives the probability of 0.08%-0.17%
for our relay to be chosen for Exit position by a user. Given
that 10 TB of traffic is included into the server’s price and
one has to pay 2 EUR per additional 1 TB, it would cost an
attacker 360 USD to have 180 TB of traffic per month. The
corresponding speed is 69 MB/s (69,000 consensus bandwidth
units). By running 6 such relays the attacker can achieve 400K
of bandwidth weight in total for the price below 2500 USD
(2160 USD for the traffic and 240 for renting fast servers).
Thus having a consensus weight close to 400,000 is possi-
ble for an economy-level attacker. The attacker can also decide
to play unfair and mount a bandwidth cheating attack which
would allow her to have a high consensus weight while keeping
the budget of the attack even lower [22]. This is especially
23A unit roughly corresponds to 1 KB/s of traffic.
131131
possible since Bitcoin traffic by itself is rather lightweight and
high bandwidth would be needed only in order to drive Tor
path selection algorithm towards attacker’s nodes.
B. Bitcoin peers
The attack described in sections III and IV suggests the
attacker injects a number Bitcoin peers; at the same time
Bitcoin network allows only one peer per IP address. Thus
the attacker is interested in getting as many IP addresses as
possible. Currently there are several options. The cheapest
option would be to rent IP addresses on per hour basis. The
market price for an IP address is 1 cent per hour [21]. This
results in 7200 USD per 1000 IP’s per month. From these
computations it is clear that an attacker would do better by
investing in Exit bandwidth rather than running Bitcoin peers
(unless she controls a small botnet), and the only limitation
for her would be not to become too noticeable. An attacker
that has 400K (7% for port 8333) of Tor Exit capacity would
cost about 2500 USD.
VIII. COUNTERMEASURES
These attacks are very effective due to a feature of Bitcoin
which allows an easy ban of Tor Exit nodes from arbitrary
Bitcoin peers and due to easy user fingerprinting with the
“address cookies”. One possible countermeasure against Tor-
ban could be to relax the reputation-based DoS protection.
For example each Bitcoin peer could have a random variable,
which would decide whether to turn ON or OFF the DoS
protection mechanism with probability 1/2. As a result the
attacker might be able to DoS at most half of the network,
but on the other hand he will not be able to ban any relays or
VPNs from all the Bitcoin peers.
An obvious countermeasure would be to encrypt and
authenticate Bitcoin traffic. This would prevent even oppor-
tunistic man-in-the-middle attacks (i.e. even if the user is
unlucky to choose a malicious Exit relay). Another possible
countermeasure is to run a set of “Tor-aware” Bitcoin peers
which would regularly download Tor consensus and make sure
that Bitcoin DoS countermeasures are not applied to servers
from the Tor consensus. K. Atlas [1] implemented a similar
countermeasure (which maintains historical record of Tor exit
nodes used to connect to the Bitcoin network.)
Finally, Bitcoin developers can maintain and distribute a
safe and stable list of onion addresses. Users which would
like to stay anonymous should choose at least one address
from this list. There currently exists a short and not up-to-date
list of Bitcoin fallback onion addresses [12]. Another advice
for a user would be to run two Bitcoin nodes, one over Tor and
one without, and compare their blockchains and unconfirmed
transactions. This would prevent from creation of virtual reality
for Tor-only users.
With regards to the fingerprinting attack several coun-
termeasures are possible. First, Bitcoin peers can request
performing proof-of-work computation for each sent GETADDR
message, so that it becomes computationally expensive for an
attacker to query each client. Second, according to the Bitcoin
core source, the only time when a client sends a GETADDR
message is when he establishes an outbound connection. Thus
ignoring GETTADDR requests on outbound connections will not
change the usual operation of Bitcoin networking protocol
and will prevent the attacker from requesting the fingerprint24.
Finally an immediate countermeasure would be to remove the
cached address database file before each session and to use
only trusted hidden-services.
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[17] S. Meiklejohn, M. Pomarole, G. Jordan, K. Levchenko, D. McCoy,
G. M. Voelker, and S. Savage, “A fistful of Bitcoins: Characterizing
payments among men with no names,” in Proceedings of the 2013
Conference on Internet Measurement Conference (IMC’13). ACM,
2013.
[18] S. Nakamoto, “Bitcoin: A peer-to-peer electronic cash system,” 2009,
http://www. bitcoin.org/bitcoin.pdf.
[19] OnionCat: An Anonymous VPN-Adapter,
https://www.onioncat.org/about-onioncat/, 2014.
[20] D. Ron and A. Shamir, “Quantitative analysis of the full Bitcoin
transaction graph,” in Proceedings of Financial Cryptography and Data
Security (FC’13). Springer, 2013.
[21] Terremark vCloud Express, http://vcloudexpress.terremark.com/pricing.aspx,
2014.
[22] F. Thill, “Hidden Service Tracking Detection and Bandwidth
Cheating in Tor Anonymity Network. Master Thesis,” 2014,
https://www.cryptolux.org/images/b/bc/.
[23] Tor FAQ, https://www.torproject.org/docs/faq.html.en#TBBFlash, 2014.
[24] Tor security advisory: “relay early” traffic confirmation attack,
https://blog.torproject.org/blog/tor-security-advisory-relay-early-traffic-
confirmation-attack, 2014.
[25] Torsocks: use socks-friendly applications with Tor,
https://code.google.com/p/torsocks/, 2014.
24We implemented this countermeasure and submitted the corresponding
patch.
132132
APPENDIX A
LIST OF REACHABLE BITCOIN ONIONS
In this Appendix we list 39 Bitcoin onion addresses which
we found to be reachable in August 2014 and 46 onion
addresses reachable in November 2014. In order to get this
list we queried reachable for the time of the experiments
Bitcoin peers by sending four GETADDR messages to each
peer. A Bitcoin peer can reply to such message by sending
back 23% of its addresses database but not more than 2500
addresses. A peer can store 20,480 addresses at most which
means that sending 4 GETADDR messages is not enough to
extract the complete peer’s database. However we expect that
there is a big overlap between the databases of different peers.
Some of the discovered reachable onion addresses begin or end
with meaningful text like: BTCNET, BITCOIN and belong to
Bitcoin developers, pools or services.
2fvnnvj2hiljjwck.onion:8333 it2pj4f7657g3rhi.onion:8333
2zdgmicx7obtivug.onion:8333 jq57qrkvvyi4a3o2.onion:8333
3crtkleibhn6qak4.onion:14135 kjy2eqzk4zwi5zd3.onion:8333
3lxko7l4245bxhex.onion:8333 mtzcz5knzjmuclnx.onion:8333
4crhf372poejlc44.onion:8333 nns4r54x3lfbrkq5.onion:8333
5ghqw4wj6hpgfvdg.onion:8333 nzsicg2ksmsrxwyz.onion:8333
5k4vwyy5stro33fb.onion:8333 pqosrh6wfaucet32.onion:8333
6fp3i7f2pbie7w7t.onion:8333 pt2awtcs2ulm75ig.onion:8333
7iyfdkr72hgtdjoh.onion:8333 pxl7ytsd2aiydadi.onion:8333
b6fr7dlbu2kpiysf.onion:8333 qsxhkpvbmt6akrov.onion:8333
bitcoin625tzsusi.onion:8333 syix2554lvyjluzw.onion:8333
bitcoinostk4e4re.onion:8333 t2vapymuu6z55s4d.onion:8333
btcdatxubbzaw4tj.onion:8333 td7tgof3imei3fm6.onion:8333
btcnet3utgzyz2bf.onion:8333 tfu4kqfhsw5slqp2.onion:8333
czsbwh4pq4mh3izl.onion:8333 thfsmmn2jbitcoin.onion:8333
dqretelgl3kjtzei.onion:8333 xdnigz4qn5dbbw2t.onion:8333
e3tn727fywnioxrc.onion:8333 xij5qyrbosw2pzjm.onion:8333
evolynhit7shzeet.onion:8333 zqq6yxxxb7or36br.onion:8333
gb5ypqt63du3wfhn.onion:8333 zy3kdqowmrb7xm7h.onion:8333
hkxy4jpeniuwouiv.onion:8333
TABLE III. BITCOIN ONIONS,ONLINE IN AUGUST 2014
2xylerfjgat6kf3s.onion:8333 h2vlpudzphzqxutd.onion:8333
2zdgmicx7obtivug.onion:8333 hkxy4jpeniuwouiv.onion:8333
3ffk7iumtx3cegbi.onion:8333 iksneq25weneygcj.onion:8333
3lxko7l4245bxhex.onion:8333 k22qrck6cetfj655.onion:8333
4crhf372poejlc44.onion:8333 kjy2eqzk4zwi5zd3.onion:8333
5k4vwyy5stro33fb.onion:8333 lazsruhzupsgpvwm.onion:8333
6fizop6wctokuxyk.onion:8333 lfmwsd65ltykrp74.onion:8333
6fp3i7f2pbie7w7t.onion:8333 luruc27g24y7ewwi.onion:8333
7g7j54btiaxhtsiy.onion:8333 pqosrh6wfaucet32.onion:8333
7pkm6urc5hlgwlyc.onion:8333 pt2awtcs2ulm75ig.onion:8333
b2ykuvob44fn36wo.onion:8333 pxl7ytsd2aiydadi.onion:8333
b6fr7dlbu2kpiysf.onion:8333 qsntokcdbwzmb2i5.onion:8333
bitcoinostk4e4re.onion:8333 sbow7bnje2f4gcvt.onion:8333
bk5ejfe56xakvtkk.onion:8333 td7tgof3imei3fm6.onion:8333
btc4xysqsf3mmab4.onion:8333 tfu4kqfhsw5slqp2.onion:8333
btcnet3utgzyz2bf.onion:8333 thfsmmn2jbitcoin.onion:8333
by4ec3pkia7s7wy2.onion:8333 ukronionufi6qhtl.onion:8333
dioq2yg3l5ptgpge.onion:8333 vqpye2k5rcqvj5mq.onion:8333
dqretelgl3kjtzei.onion:8333 wc5nztpe26jrjmoo.onion:8333
drp4pvejybx2ejdr.onion:8333 xudkoztdfrsuyyou.onion:8333
e3tn727fywnioxrc.onion:8333 z3isvv4llrmv57lh.onion:8333
evolynhit7shzeet.onion:8333 zc6fabqhrjwdle3b.onion:8333
gb5ypqt63du3wfhn.onion:8333 zy3kdqowmrb7xm7h.onion:8333
TABLE IV. B ITCOIN ONIONS,ONLINE IN NOVEMBER 2014
APPENDIX B
DOUBLE-SPENDING
In this section we describe three techniques which an
attacker can use to try to carry out a double spend attack.
These techniques are known to the bitcoin community and
described in e.g. [9].
A. Race attack (0-confirmation attack)
The first method assumes that a merchant accepts a pay-
ment immediately upon receipt of the corresponding transac-
tion (i.e. he does not wait until this transactions is included
into a block) and gives away the product to the client. In such
case a malicious client can communicate one transaction (with
the payment) to the merchant and a different transaction which
spends the same inputs to the rest of the network. This will
result in that only the second transaction will be included by
miners into the new block, and thus accepted by the network
while the first transaction (i.e. the one which the merchant
received) will finally be rejected by the network.
The attack is successful only if the attacker manages
to deliver the second transaction to miners before the first
transaction is broadcasted to the network by the merchant.
In case the attacker controls merchant’s connections to the
network (as in Bitcoin over Tor attack described in this paper),
it is much easier for the attacker to succeed. The common
use-case for this attack would be when it is unlikely that a
client would wait for 10-minutes confirmation before taking
his purchase and go.
B. 1-block confirmation attack25
The target of the second attack is a service which allows
one to make a deposit (in Bitcoins) by sending it in the
corresponding transaction and withdraw the deposited Bitcoins
back as soon as the transaction is included into a block. The
service should use different coins for the withdrawal than the
ones that were just received for the deposit.
An attacker creates a transaction which makes a large
deposit (e.g. 50 BTC) to the service and adds it to a block
that she is currently mining. The transaction is kept private.
When the attacker finds a valid block she does not broadcast
it immediately, but instead waits until someone else mines an-
other block. Once it happens, the attacker sends his block to the
service. If the attacker’s block arrives first, the service accepts
the deposit transaction which will have one confirmation. At
the same time the rest of the network accepts the other block 26.
The attacker immediately request a withdrawal, and the service
generates a transaction sending the large amount of coins to
the attacker. Since the rest of the network is working on a
different blockchain fork, the attacker’s deposit will soon be
invalidated while the withdrawal will be considered valid.
The attack only succeeds if the attacker manages to deliver
his block to the service first while the rest of the network (or
more specifically the majority of miners) accepts a different
block. The success is much more likely if the attacker controls
the service’s network connections.
25This attack was described by user Vector76 in a Bitcoin forum post.
26The attacker also double-spends the inputs from the deposit transaction.
The network (since it did not receive the attacker’s block) will accept it as
valid and work to include it in the next block.
133133
C. Finney attack (block withholding)
The third attack similarly to the Race attack assumes that
a merchant accept a payment upon receipt of the unconfirmed
transaction. A malicious miner generates a transaction in which
he sends bitcoins to himself and includes it to the block he is
working on. The transaction is kept private.
When the block is found, the miner spends the same coins
somewhere else and immediately after that releases the block.
As the result, the network accepts the block and considers the
transaction to the merchant as invalid.
134134
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Could eroding net neutrality hurt bitcoin
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