Remote Timing Attacks are PracticalDavid Brumley
Stanford University
dbrumley@cs.stanford.edu
Abstract
Timing attacks are usually used to attack weak computing devices such as smartcards. We show that timing
attacks apply to general software systems. Specifically,
we devise a timing attack against OpenSSL. Our experiments show that we can extract private keys from an
OpenSSL-based web server running on a machine in thelocal network. Our results demonstrate that timing attacks against network servers are practical and therefore
security systems should defend against them.

1

Introduction

Timing attacks enable an attacker to extract secrets
maintained in a security system by observing the time
it takes the system to respond to various queries. For
example, Kocher [10] designed a timing attack to expose secret keys used for RSA decryption. Until now,these attacks were only applied in the context of hardware security tokens such as smartcards [4, 10, 18]. It
is generally believed that timing attacks cannot be used
to attack general purpose servers, such as web servers,
since decryption times are masked by many concurrent
processes running on the system. It is also believed that
common implementations of RSA (using Chinese Remainder and Montgomery reductions) are not vulnerable
to timing attacks.
We challenge both assumptions by developing a remotetiming attack against OpenSSL [15], an SSL librarycommonly used in web servers and other SSL applications. Our attack client measures the time an OpenSSL
server takes to respond to decryption queries. The clientis able to extract the private key stored on the server. The
attack applies in several environments.
Network. We successfully mounted our timing attack
between two machines on our campus network.

Dan Boneh
Stanford University
dabo@cs.stanford.edu
The attacking machine and the server were indifferent buildings with three routers and multiple switches between them. With this setup wewere able to extract the SSL private key from
common SSL applications such as a web server(Apache+mod SSL) and a SSL-tunnel.
Interprocess. We successfully mounted the attack between two processes running on the same machine.
A hosting center that hosts two domains on thesame machine might give management access tothe admins of each domain. Since both domain are
hosted on the same machine, one admin could use
the attack to extract the secret key belonging to theother domain.
Virtual Machines. A Virtual Machine Monitor (VMM)
is often used to enforce isolation between two Virtual Machines (VM) running on the same processor. One could protect an RSA private key by storing it in one VM and enabling other VM’s to makedecryption queries. For example, a web server
could run in one VM while the private key is stored
in a separate VM. This is a natural way of protecting secret keys since a break-in into the web server
VM does not expose the private key. Our results
show that when using OpenSSL the network server
VM can extract the RSA private key from the secure VM, thus invalidating the isolation provided
by the VMM. This is especially relevant to VMM
projects such as Microsoft’s NGSCB architecture(formerly Palladium). We also note that NGSCB
enables an application to ask the VMM (aka Nexus)
to decrypt (aka unseal) application data. The application could expose the VMM’s secret key by measuring the time the VMM takes to respond to such
requests.
Many crypto libraries completely ignore the timing attack and have no defenses implemented to prevent it. For
example, libgcrypt [14] (used in GNUTLS and GPG)
and Cryptlib [5] do not defend against timing attacks.
OpenSSL 0.9.7 implements a defense against the timing attack as an option. However, common applications
such as mod SSL, the Apache SSL module, do not en-able this option and are therefore vulnerable to the attack. These examples show that timing attacks are alargely ignored vulnerability in many crypto implementations. We hope the results of this paper will help convince developers to implement proper defenses (see Section 6). Interestingly, Mozilla’s NSS crypto library properly defends against the timing attack. We note thatmost crypto acceleration cards also implement defenses
against the timing attack. Consequently, network servers
using these accelerator cards are not vulnerable.
We chose to tailor our timing attack to OpenSSL since
it is the most widely used open source SSL library.
The OpenSSL implementation of RSA is highly optimized using Chinese Remainder, Sliding Windows,
Montgomery multiplication, and Karatsuba’s algorithm.
These optimizations cause both known timing attacks on
RSA [10, 18] to fail in practice. Consequently, we had todevise a new timing attack based on [18, 19, 20, 21, 22]that is able to extract the private key from an OpenSSLbased server. As we will see, the performance of ourattack varies with the exact environment in which it isapplied. Even the exact compiler optimizations used tocompile OpenSSL can make a big difference.

2.1 OpenSSL Decryption
At the heart of RSA decryption is a modular exponentiation m = cd mod N where N = pq is the RSA
modulus, d is the private decryption exponent, and cis the ciphertext being decrypted. OpenSSL uses the
Chinese Remainder Theorem (CRT) to perform this exponentiation. With Chinese remaindering, the functionm = cd mod N is computed in two steps. First, evaluate m1 = cd1 mod p and m2 = cd2 mod q (here d1 andd2 are precomputed from d). Then, combine m1 and m2
using CRT to yield m.
RSA decryption with CRT gives up to a factor of four
speedup, making it essential for competitive RSA implementations. RSA with CRT is not vulnerable to Kocher’soriginal timing attack [10]. Nevertheless, since RSA
with CRT uses the factors of N , a timing attack can expose these factors. Once the factorization of N is revealed it is easy to obtain the decryption key by computing d = e−1 mod (p − 1)(q − 1).

2.2 Exponentiation
In Sections 2 and 3 we describe OpenSSL’s implementation of RSA and the timing attack on OpenSSL. In
Section 4 we discuss how these attacks apply to SSL.
In Section 5 we describe the actual experiments we carried out. We show that using about a million queries we
can remotely extract a 1024-bit RSA private key from an
OpenSSL 0.9.7 server. The attack takes about two hours.
Timing attacks are related to a class of attacks calledside-channel attacks. These include power analysis [9]and attacks based on electromagnetic radiation [16]. Unlike the timing attack, these extended side channel attacks require special equipment and physical access tothe machine. In this paper we only focus on the timing
attack. We also note that our attack targets the implementation of RSA decryption in OpenSSL. Our timing
attack does not depend upon the RSA padding used in
SSL and TLS.

2

OpenSSL’s Implementation of RSA

We begin by reviewing how OpenSSL implements RSA
decryption. We only review the details needed for our
attack. OpenSSL closely follows algorithms describedin the Handbook of Applied Cryptography [11], wheremore information is available.

During an RSA decryption with CRT, OpenSSL computes cd1 mod p and cd2 mod q. Both computations aredone using the same code. For simplicity we describe
how OpenSSL computes g d mod q for some g, d, and q.
The simplest algorithm for computing g d mod q issquare and multiply. The algorithm squares g approximately log2 d times, and performs approximately log22 d
additional multiplications by g. After each step, theproduct is reduced modulo q.
OpenSSL uses an optimization of square and multiply
called sliding windows exponentiation. When using sliding windows a block of bits (window) of d are processed at each iteration, where as simple square-andmultiply processes only one bit of d per iteration. Sliding windows requires pre-computing a multiplication table, which takes time proportional to 2w−1 +1 for a window of size w. Hence, there is an optimal window sizethat balances the time spent during precomputation vs.
actual exponentiation. For a 1024-bit modulus OpenSSL
uses a window size of five so that about five bits of the
exponent d are processed in every iteration.
For our attack, the key fact about sliding windows is thatduring the algorithm there are many multiplications byg, where g is the input ciphertext. By querying on many

2.3 Montgomery Reduction
The sliding windows exponentiation algorithm performs
a modular multiplication at every step. Given two integers x, y, computing xy mod q is done by first multiplying the integers x ∗ y and then reducing the result modulo q. Later we will see each reduction also requires a
few additional multiplications. We first briefly describe
OpenSSL’s modular reduction method and then describe
its integer multiplication algorithm.
Naively, a reduction modulo q is done via multiprecision division and returning the remainder. This isquite expensive. In 1985 Peter Montgomery discovereda method for implementing a reduction modulo q using a series of operations efficient in hardware and software [13].
Montgomery reduction transforms a reduction modulo
q into a reduction modulo some power of 2 denoted by
R. A reduction modulo a power of 2 is faster than areduction modulo q as many arithmetic operations canbe implemented directly in hardware. However, in order
to use Montgomery reduction all variables must first be
put into Montgomery form. The Montgomery form of
number x is simply xR mod q. To multiply two numbers a and b in Montgomery form we do the following.
First, compute their product as integers: aR∗bR = cR2 .
Then, use the fast Montgomery reduction algorithm tocompute cR2 ∗ R−1 = cR mod q. Note that the resultcR mod q is in Montgomery form, and thus can be directly used in subsequent Montgomery operations. At
the end of the exponentiation algorithm the output is put
back into standard (non-Montgomery) form by multiplying it by R−1 mod q. For our attack, it is equivalent to
use R and R−1 mod N , which are public.
Hence, for the small penalty of converting the input g to
Montgomery form, a large gain is achieved during modular reduction. With typical RSA parameters the gainfrom Montgomery reduction outweighs the cost of initially putting numbers in Montgomery form and converting back at the end of the algorithm.

# of extra reductions in Montgery’s algorithm

inputs g the attacker can expose information about bitsof the factor q. We note that a timing attack on sliding
windows is much harder than a timing attack on squareand-multiply since there are far fewer multiplications by
g in sliding windows. As we will see, we had to adapt
our techniques to handle sliding windows exponentiation used in OpenSSL.

discontinuity wheng mod q = 0
discontinuity wheng mod p = 0

q

2q
3q p
4q
values g between 0 and 6q

5q

Figure 1: Number of extra reductions in a Montgomery
reduction as a function (equation 1) of the input g.
The key relevant fact about a Montgomery reduction isat the end of the reduction one checks if the output cR
is greater than q. If so, one subtracts q from the output, to ensure that the output cR is in the range [0, q).
This extra step is called an extra reduction and causes atiming difference for different inputs. Schindler noticed
that the probability of an extra reduction during an exponentiation g d mod q is proportional to how close g is
to q [18]. Schindler showed that the probability for an
extra reduction is:
Pr[Extra Reduction] =g mod q2R(1)

Consequently, as g approaches either factor p or q frombelow, the number of extra reductions during the exponentiation algorithm greatly increases. At exact multiples of p or q, the number of extra reductions drops
dramatically. Figure 1 shows this relationship, with thediscontinuities appearing at multiples of p and q. By detecting timing differences that result from extra reductions we can tell how close g is to a multiple of one of
the factors.

2.4 Multiplication Routines
RSA operations, including those using Montgomery’s
method, must make use of a multi-precision integer multiplication routine. OpenSSL implements two multiplication routines: Karatsuba (sometimes called recursive)and “normal”. Multi-precision libraries represent large
integers as a sequence of words. OpenSSL uses Karatsuba multiplication when multiplying two numbers withan equal number of words. Karatsuba multiplication
takes time O(nlog2 3 ) which is O(n1.58 ). OpenSSL uses

normal multiplication, which runs in time O(nm), when
multiplying two numbers with an unequal number ofwords of size n and m. Hence, for numbers that are approximately the same size (i.e. n is close to m) normal
multiplication takes quadratic time.
Thus, OpenSSL’s integer multiplication routine leaks
important timing information. Since Karatsuba is typically faster, multiplication of two unequal size words
takes longer than multiplication of two equal size words.
Time measurements will reveal how frequently theoperands given to the multiplication routine have thesame length. We use this fact in the timing attack on
OpenSSL.
In both algorithms, multiplication is ultimately done on
individual words. The underlying word multiplication
algorithm dominates the total time for a decryption. For
example, in OpenSSL the underlying word multiplication routine typically takes 30% − 40% of the total runtime. The time to multiply individual words depends on
the number of bits per word. As we will see in experiment 3 the exact architecture on which OpenSSL runs
has an impact on timing measurements used for the attack. In our experiments the word size was 32 bits.

2.5 Comparison of Timing Differences
So far we identified two algorithmic data dependencies
in OpenSSL that cause time variance in RSA decryption:(1) Schindler’s observation on the number of extra reductions in a Montgomery reduction, and (2) the timing
difference due to the choice of multiplication routine,i.e. Karatsuba vs. normal. Unfortunately, the effects ofthese optimizations counteract one another.
Consider a timing attack where we decrypt a ciphertext
g. As g approaches a multiple of the factor q from below, equation (1) tells us that the number of extra reductions in a Montgomery reduction increases. When weare just over a multiple of q, the number of extra reductions decreases dramatically. In other words, decryptionof g < q should be slower than decryption of g > q.
The choice of Karatsuba vs. normal multiplication has
the opposite effect. When g is just below a multipleof q, then OpenSSL almost always uses fast Karatsuba
multiplication. When g is just over a multiple of q theng mod q is small and consequently most multiplications
will be of integers with different lengths. In this case,
OpenSSL uses normal multiplication which is slower.
In other words, decryption of g < q should be fasterthan decryption of g > q — the exact opposite of theeffect of extra reductions in Montgomery’s algorithm.
Which effect dominates is determined by the exact environment. Our attack uses both effects, but each effect isdominant at a different phase of the attack.

3

A Timing Attack on OpenSSL

Our attack exposes the factorization of the RSA modulus. Let N = pq with q < p. We build approximations toq that get progressively closer as the attack proceeds. We
call these approximations guesses. We refine our guessby learning bits of q one at a time, from most significant to least. Thus, our attack can be viewed as a binary
search for q. After recovering the half-most significant
bits of q, we can use Coppersmith’s algorithm [3] to retrieve the complete factorization.
Initially our guess g of q lies between 2512 (i.e.
2log2 N/2 ) and 2511 (i.e. 2log2 (N/2)−1 ). We then time thedecryption of all possible combinations of the top few
bits (typically 2-3). When plotted, the decryption times
will show two peaks: one for q and one for p. We pickthe values that bound the first peak, which in OpenSSL
will always be q.
Suppose we already recovered the top i − 1 bits of q. Let
g be an integer that has the same top i − 1 bits as q andthe remaining bits of g are 0. Then g < q. At a highlevel, we recover the i’th bit of q as follows:• Step 1 - Let ghi be the same value as g, with thei’th bit set to 1. If bit i of q is 1, then g < ghi < q.
Otherwise, g < q < ghi .
• Step 2 - Compute ug = gR−1 mod N and ughi =ghi R−1 mod N . This step is needed because RSA
decryption with Montgomery reduction will calculate ug R = g and ughi R = ghi to put ug and ughi
in Montgomery form before exponentiation duringdecryption.
• Step 3 We measure the time to decrypt both ug
and ughi . Let t1 = DecryptTime(ug ) and t2 =DecryptTime(ughi ).
• Step 4 - We calculate the difference ∆ = |t1 − t2 |.
If g < q < ghi then, by Section 2.5, the difference∆ will be “large”, and bit i of q is 0. If g < ghi < q,
the difference ∆ will be “small”, and bit i of q is 1.
We use previous ∆ values to know what to consider“large” and “small”. Thus we use the value |t1 −t2 |as an indicator for the i’th bit of q.

When the i’th bit is 0, the “large” difference can either be negative or positive. In this case, if t1 − t2 is
positive then DecryptTime(g) > DecryptTime(ghi ), andthe Montgomery reductions dominated the time difference. If t1 − t2 is negative, then DecryptTime(g) <DecryptTime(ghi ), and the multi-precision multiplication dominated the time difference.
Formatting of RSA plaintext, e.g. PKCS 1, does not affect this timing attack. We also do not need the value ofthe decryption, only how long the decryption takes.

with mod SSL [12], and an attack on trusted computing projects such as Microsoft’s NGSCB (formerly Palladium).
During a standard full SSL handshake the SSL server
performs an RSA decryption using its private key. The
SSL server decryption takes place after receiving the
CLIENT- KEY- EXCHANGE message from the SSL client.
The CLIENT- KEY- EXCHANGE message is composed on
the client by encrypting a PKCS 1 padded random bytes
with the server’s public key. The randomness encryptedby the client is used by the client and server to computea shared master secret for end-to-end encryption.

3.1 Exponentiation Revisited
We would like |tg1 −tg2 |  |tg3 −tg4 | when g1 < q < g2
and g3 < g4 < q. Time measurements that have thisproperty we call a strong indicator for bits of q, and thosethat do not are a weak indicator for bits of q. Square andmultiply exponentiation results in a strong indicator because there are approximately log22 d multiplications by
g during decryption. However, in sliding windows withwindow size w (w = 5 in OpenSSL) the expected number of multiplications by g is only:E[# multiply by g] ≈log2 dw−1
2(w + 1)

resulting in a weak indicator.
To overcome this we query at a neighborhood of valuesg, g + 1, g + 2, ..., g + n, and use the result as the decrypt
time for g (and similarly for ghi ). The total decryption
time for g or ghi is then:
Tg =

n
X

DecryptTime(g + i)i=0

We define Tg as the time to compute g with sliding windows when considering a neighborhood of values. Asn grows, |Tg − Tghi | typically becomes a stronger indicator for a bit of q (at the cost of additional decryption
queries).

4

Real-world scenarios

As mentioned in the introduction there are a numberof scenarios where the timing attack applies to networked servers. We discuss an attack on SSL applications, such as stunnel [23] and an Apache web server

Upon receiving a CLIENT- KEY- EXCHANGE message
from the client, the server first decrypts the message with
its private key and then checks the resulting plaintext for
proper PKCS 1 formatting. If the decrypted message
is properly formatted, the client and server can compute a shared master secret. If the decrypted message
is not properly formatted, the server generates its own
random bytes for computing a master secret and continues the SSL protocol. Note that an improperly formatted CLIENT- KEY- EXCHANGE message prevents theclient and server from computing the same master secret,ultimately leading the server to send an ALERT message
to the client indicating the SSL handshake has failed.
In our attack, the client substitutes a properly formatted CLIENT- KEY- EXCHANGE message with our guessg. The server decrypts g as a normal CLIENT- KEYEXCHANGE message, and then checks the resulting
plaintext for proper PKCS 1 padding. Since the decryption of g will not be properly formatted, the server andclient will not compute the same master secret, and theclient will ultimately receive an ALERT message fromthe server. The attacking client computes the time difference from sending g as the CLIENT- KEY- EXCHANGE
message to receiving the response message from theserver as the time to decrypt g. The client repeats thisprocess for each value of of g and ghi needed to calculate Tg and Tghi .
Our experiments are also relevant to trusted computing
efforts such as NGSCB. One goal of NGSCB is to provide sealed storage. Sealed storage allows an application to encrypt data to disk using keys unavailable to theuser. The timing attack shows that by asking NGSCB
to decrypt data in sealed storage a user may learn thesecret application key. Therefore, it is essential that the
secure storage mechanism provided by projects such as
NGSCB defend against this timing attack.

As mentioned in the introduction, RSA applications (andsubsequently SSL applications using RSA for key exchange) using a hardware crypto accelerator are not vulnerable since most crypto accelerators implement defenses against the timing attack. Our attack applies tosoftware based RSA implementations that do not defend
against timing attacks as discussed in section 6.

5

Experiments

We performed a series of experiments to demonstrate theeffectiveness of our attack on OpenSSL. In each case weshow the factorization of the RSA modulus N is vulnerable. We show that a number of factors affect theefficiency of our timing attack.
Our experiments consisted of:
1. Test the effects of increasing the number of decryption requests, both for the same ciphertext and aneighborhood of ciphertexts.
2. Compare the effectiveness of the attack based upon
different keys.
3. Compare the effectiveness of the attack based uponmachine architecture and common compile-time
optimizations.
4. Compare the effectiveness of the attack based uponsource-based optimizations.
5. Compare inter-process vs. local network attacks.
6. Compare the effectiveness of the attack against two
common SSL applications: an Apache web serverwith mod SSL and stunnel.
The first four experiments were carried out inter-process
via TCP, and directly characterize the vulnerability of
OpenSSL’s RSA decryption routine. The fifth experiment demonstrates our attack succeeds on the local
network. The last experiment demonstrates our attack
succeeds on the local network against common SSLenabled applications.

5.1 Experiment Setup
Our attack was performed against OpenSSL 0.9.7,
which does not blind RSA operations by default. All
tests were run under RedHat Linux 7.3 on a 2.4 GHz
Pentium 4 processor with 1 GB of RAM, using gcc
2.96 (RedHat). All keys were generated at random via
OpenSSL’s key generation routine.

For the first 5 experiments we implemented a simple
TCP server that read an ASCII string, converted thestring to OpenSSL’s internal multi-precision representation, then performed the RSA decryption. The server returned 0 to signify the end of decryption. The TCP client
measured the time from writing the ciphertext over thesocket to receiving the reply.
Our timing attack requires a clock with fine resolution.
We use the Pentium cycle counter on the attacking machine as such a clock, giving us a time resolution of
2.4 billion ticks per second. The cycle counter increments once per clock tick, regardless of the actual instruction issued. Thus, the decryption time is the cycle
counter difference between sending the ciphertext to receiving the reply. The cycle counter is accessible via
the “rdtsc” instruction, which returns the 64-bit cycle
count since CPU initialization. The high 32 bits are returned into the EDX register, and the low 32 bits into
the EAX register. As recommended in [7], we use the“cpuid” instruction to serialize the processor to prevent
out-of-order execution from changing our timing measurements. Note that cpuid and rdtsc are only used by
the attacking client, and that neither instruction is a privileged operation. Other architectures have a similar acounter, such as the UltraSparc %tick register.
OpenSSL generates RSA moduli N = pq where q < p.
In each case we target the smaller factor, q. Once q is
known, the RSA modulus is factored and, consequently,
the server’s private key is exposed.

5.2 Experiment 1 - Number of Ciphertexts
This experiment explores the parameters that determine
the number of queries needed to expose a single bit of
an RSA factor. For any particular bit of q, the numberof queries for guess g is determined by two parameters:neighborhood size and sample size.
Neighborhood size. For every bit of q we measure thedecryption time for a neighborhood of values g, g +1, g+2, ..., g+n. We denote this neighborhood size
by n.
Sample size. For each value g + i in a neighborhood
we sample the decryption time multiple times andcompute the mean decryption time. The number oftimes we query on each value g + i is called thesample size and is denoted by s.
The total number of queries needed to compute Tg isthen s ∗ n.

3e+07
Time difference in CPU cycles

Time variation in CPU cycles

80000
60000
40000
Decryption time converges

20000
0-20000-40000-600002.5e+07zero-one gap when a bit of q=0
zero-one gap when a bit of q=1

2e+07
1.5e+07
1e+07
5e+06

zero-one gap0-5e+06

2

4

6

8

10

12

14

# of samples for a particular ciphertext(a) The time variance for decrypting a particular ciphertext
decreases as we increase the number of samples taken.

100 200 300 400 500 600 700 800 900 1000
Neighborhood size(b) By increasing the neighborhood size we increase the zeroone gap between a bit of q that is 0 and a bit of q that is 1.

Figure 2: Parameters that affect the number of decryption queries of g needed to guess a bit of the RSA factor.
To overcome the effects of a multi-user environment, werepeatedly sample g+k and use the median time value as
the effective decryption time. Figure 2(a) shows the difference between median values as sample size increases.
The number of samples required to reach a stable decryption time is surprising small, requiring only 5 samples to give a variation of under 20000 cycles (approximately 8 microseconds), well under that needed to perform a successful attack.
We call the gap between when a bit of q is 0 and 1 thezero-one gap. This gap is related to the difference |Tg −Tghi |, which we expect to be large when a bit of q is 0
and small otherwise. The larger the gap, the stronger theindicator that bit i is 0, and the smaller chance of error.
Figure 2(b) shows that increasing the neighborhood size
increases the size of the zero-one gap when a bit of q is0, but is steady when a bit of q is 1.
The total number of queries to recover a factor is 2ns ∗log2 N/4, where N is the RSA public modulus. Unlessexplicitly stated otherwise, we use a sample size of 7
and a neighborhood size of 400 on all subsequent experiments, resulting in 1433600 total queries. With theseparameters a typical attack takes approximately 2 hours.
In practice, an effective attack may need far fewer samples, as the neighborhood size can be adjusted dynamically to give a clear zero-one gap in the smallest numberof queries.

5.3 Experiment 2 - Different Keys
We attacked several 1024-bit keys, each randomly generated, to determine the ease of breaking different moduli. In each case we were able to recover the factorization of N . Figure 3(a) shows our results for 3 different
keys. For clarity, we include only bits of q that are 0,
as bits of q that are 1 are close to the x-axis. In all our
figures the time difference Tg − Tghi is the zero-one gap.
When the zero-one gap for bit i is far from the x-axis we
can correctly deduce that bit i is 0.
With all keys the zero-one gap is positive for about thefirst 32 bits due to Montgomery reductions, since bothg and ghi use Karatsuba multiplication. After bit 32,
the difference between Karatsuba and normal multiplication dominate until overcome by the sheer size difference between log2 (g mod q) − log2 (ghi mod q). The
size difference alters the zero-one gaps because as bitsof q are guessed, ghi becomes smaller while g remains≈ log2 q. The size difference counteracts the effects ofKaratsuba vs. normal multiplication. Normally the resulting zero-one gap shift happens around multiples of
32 (224 for key 1, 191 for key 2 and 3), our machine
word size. Thus, an attacker should be aware that thezero-one gap may flip signs when guessing bits that arearound multiples of the machine word size.

2e+06

key 1
key 2
key 3

1e+07

Time difference in CPU cycles

Time difference in CPU cycles

1.5e+07

5e+06
0-5e+06-1e+07-1.5e+07

1e+06

Neighborhood=800
Neighborhood=4000-1e+06

increasing neigh. = larger zero-one gap-2e+06-3e+06-4e+06-5e+06

0

50

100

150

200

250

Bits guessed of factor q(a) The zero-one gap Tg − Tghi indicates that we can distinguish between bits that are 0 and 1 of the RSA factor q for 3
different randomly-generated keys. For clarity, bits of q thatare 1 are omitted, as the x-axis can be used for reference forthis case.

190

195

200

205

210

215

220

Bits guessed of factor q(b) When the neighborhood is 400, the zero-one gap is small
for some bits in key 3, making it difficult to distinguish between the 0 and 1 bits of q. By increasing the neighborhood
size to 800, the zero-one gap is increased and we can launcha successful attack.

Figure 3: Breaking 3 RSA Keys by looking at the zero-one gap time difference
As discussed previously we can increase the size of theneighborhood to increase |Tg − Tghi |, giving a stronger
indicator. Figure 3(b) shows the effects of increasing theneighborhood size from 400 to 800 to increase the zeroone gap, resulting in a strong enough indicator to mounta successful attack on bits 190-220 of q in key 3.
The results of this experiment show that the factorizationof each key is exposed by our timing attack by the zeroone gap created by the difference when a bit of q is 0 or
1. The zero-one gap can be increased by increasing theneighborhood size if hard-to-guess bits are encountered.

5.4 Experiment 3 - Architecture and CompileTime Effects
In this experiment we show how the computer architecture and common compile-time optimizations can affect the zero-one gap in our attack. Previously, we haveshown how algorithmically the number of extra Montgomery reductions and whether normal or Karatsuba
multiplication is used results in a timing attack. However, the exact architecture on which decryption is performed can change the zero-one gap.
To show the effect of architecture on the timing attack, we begin by showing the total number of instructions retired agrees with our algorithmic analysis of
OpenSSL’s decryption routines. An instruction is retired when it completes and the results are written to thedestination [8]. However, programs with similar retirement counts may have different execution profiles due
to different run-time factors such as branch predictions,pipeline throughput, and the L1 and L2 cache behavior.
We show that minor changes in the code can change thetiming attack in two programs: “regular” and “extrainst”. Both programs time local calls to the OpenSSL
decryption routine, i.e. unlike other programs presented“regular” and “extra-inst” are not network clients attacking a network server. The “extra-inst” is identical to “regular” except 6 additional nop instructions inserted before timing decryptions. The nop’s only change
subsequent code offsets, including those in the linked
OpenSSL library.
Table 1 shows the timing attack with both programs fortwo bits of q. Montgomery reductions cause a positive
instruction retired difference for bit 30, as expected. The
difference between Karatsuba and normal multiplication
cause a negative instruction retired difference for bit 32,
again as expected. However, the difference Tg − Tghi
does not follow the instructions retired difference. On
bit 30, there is about a 4 million extra cycles difference
between the “regular” and “extra-inst” programs, eventhough the instruction retired count decreases. For bit
32, the change is even more pronounced: the zero-one
gap changes sign between the “normal” and “extra-inst”
programs while the instructions retired are similar!

g − ghi retired
4579248(0.009%)7641653(0.016%)-14275879(-0.029%)-13187257(-0.027%)

Tg − Tghi cycles
6323188(0.057%)2392299(0.022%)-5429545(-0.049%)
1310809(0.012%)

Table 1: Bit 30 of q for both “regular” and “extra-inst”(which has a few additional nop’s) have a positive instructions retired difference due to Montgomery reductions. Similarly, bit 32 has a negative instruction difference due to normal vs. Karatsuba multiplication. However, the addition of a few nop instructions in the “extrainstr” program changes the timing profile, most notably
for bit 32. The percentages given are the difference divided by either the total of instructions retired or cycles
as appropriate.
Extensive profiling using Intel’s VTune [6] shows nosingle cause for the timing differences. However, twoof the most prevalent factors were the L1 and L2 cachebehavior and the number of instructions speculatively
executed incorrectly. For example, while the “regular”
program suffers approximately 0.139% L1 and L2 cache
misses per load from memory on average, “extra-inst”
has approximately 0.151% L1 and L2 cache misses perload. Additionally, the “regular” program speculatively
executed about 9 million micro-operations incorrectly.
Since the timing difference detected in our attack is only
about 0.05% of total execution time, we expect the runtime factors to heavily affect the zero-one gap. However,under normal circumstances some zero-one gap should
be present due to the input data dependencies during decryption.
The total number of decryption queries required for a
successful attack also depends upon how OpenSSL iscompiled. The compile-time optimizations change boththe number of instructions, and how efficiently instructions are executed on the hardware. To test the effectsof compile-time optimizations, we compiled OpenSSL
three different ways:• Optimized (-O3 -fomit-frame-pointer-mcpu=pentium): The default OpenSSL flags forIntel. -O3 is the optimization level,-fomit-frame-pointer omits the frame pointer, thus
freeing up an extra register, and -mcpu=pentium
enables more sophisticated resource scheduling.

2e+07
Time variation in CPU cycles“regular”
bit 30“extra-inst”
bit 30“regular”
bit 32“extra-inst”
bit 32

Optimized
Optimized but w/o -mcpu
Unoptimized

1.5e+07
1e+07
5e+06
0
-5e+06-1e+07-1.5e+07-2e+07
0

50

100
150
Bits guessed of factor q

200

250

Figure 4: Different compile-time flags can shift the zeroone gap by changing the resulting code and how efficiently it can be executed.
• No Pentium flag (-O3 -fomit-frame-pointer): Thesame as the above, but without -mcpu sophisticatedresource scheduling is not done, and an i386 architecture is assumed.
• Unoptimized (-g ): Enable debugging support.
Each different compile-time optimization changed thezero-one gap. Figure 4 compares the results of each test.
For readability, we only show the difference Tg − Tghi
when bit i of q is 0 (g < q < ghi ). The case where bit
i = 1 shows little variance based upon the optimizations,and the x-axis can be used for reference.
Recall we expected Montgomery reductions to dominate
when guessing the first 32 bits (with a positive zero-onegap), switching to Karatsuba vs. normal multiplication(with a negative zero-one gap) thereafter. Surprisingly,
the unoptimized OpenSSL is unaffected by the Karatsuba vs. normal multiplication. Another surprising difference is the zero-one gap is more erratic when the-mcpu flag is omitted.
In these tests we again made about 1.4 million decryption queries. We note that without optimizations (-g),
separate tests allowed us to recover the factorization withless than 359000 queries. This number could be reduced
further by dynamically reducing the neighborhood size
as bits of q are learned. Also, our tests of OpenSSL
0.9.6g were similar to the results of 0.9.7, suggesting
previous versions of OpenSSL are also vulnerable.

1.5e+07

OpenSSL patched (bit=0)
OpenSSL patched (bit=1)
Unpatched (bit=0)
Unpatched (bit=1)

1e+07

Time variation in CPU cycles

Time variation in CPU cycles

1.5e+07

5e+06
0-5e+06-1e+07-1.5e+07

Internetwork (bit=0)
Internetwork (bit=1)
Interprocess bit of (bit=0)
Interprocess (bit=1)

1e+07
5e+06
0
-5e+06-1e+07-1.5e+07

0

50

100
150
Bits guessed of factor q

200

250

Figure 5: Minor source-based optimizations change thezero-one gap as well. As a consequence, code that
doesn’t appear initially vulnerable may become so as thesource is patched.
One conclusion we draw is that users of binary crypto
libraries may find it hard to characterize their risk to ourattack without complete understanding of the compiletime options and exact execution environment. Common flags such as enabling debugging support allow ourattack to recover the factors of a 1024-bit modulus inabout 1/3 million queries. We speculate that less complex architectures will be less affected by minor codechanges, and have the zero-one gap as predicted by the
OpenSSL algorithm analysis.

5.5 Experiment 4 - Source-based Optimizations
Source-based optimizations can also change the zeroone gap. RSA library developers may believe their code
is not vulnerable to the timing attack based upon testing. However, subsequent patches may change the code
profile resulting in a timing vulnerability. To show thatminor source changes also affect our attack, we implemented a minor patch that improves the efficiency of
the OpenSSL 0.9.7 CRT decryption check. Our patch
has been accepted for future incorporation to OpenSSL(tracking ID 475).
After a CRT decryption, OpenSSL re-encrypts the result (mod N ) and verifies the result is identical to theoriginal ciphertext. This verification step prevents an incorrect CRT decryption from revealing the factors of themodulus [2]. By default, OpenSSL needlessly recalculates both Montgomery parameters R and R−1 mod N
on every decryption. Our minor patch allows OpenSSL

0

50

100
150
Bits guessed of factor q

200

250

Figure 6: The timing attack succeeds over a local network. We contrast our results with the attack interprocess.
to cache both values between decryptions with the same
key. Our patch does not affect any other aspect of the
RSA decryption other than caching these values. Figure 5 shows the results of an attack both with and without the patch.
The zero-one gap is shifted because the resulting code
will have a different execution profile, as discussed in theprevious experiment. While our specific patch decreases
the size of the zero-one gap, other patches may increase
the zero-one gap. This shows the danger of assuming a
specific application is not vulnerable due to timing attack tests, as even a small patch can change the run-time
profile and either increase or decrease the zero-one gap.
Developers should instead rely upon proper algorithmic
defenses as discussed in section 6.

5.6 Experiment 5 - Interprocess vs. Local Network Attacks
To show that local network timing attacks are practical,
we connected two computers via a 10/100 Mb Hawking switch, and compared the results of the attack interprocess vs. inter-network. Figure 6 shows that the network does not seriously diminish the effectiveness of
the attack. The noise from the network is eliminatedby repeated sampling, giving a similar zero-one gap tointer-process. We note that in our tests a zero-one gap
of approximately 1 millisecond is sufficient to receivea strong indicator, enabling a successful attack. Thus,
networks with less than 1ms of variance are vulnerable.

2e+07

Apache+modSSL
Stunnel
Simple RSA server

1e+07

Time variation in CPU cycles

Time variation in CPU cycles

1.5e+07

5e+06
0-5e+06-1e+07-1.5e+07-2e+07

Apache+mod_SSL - campus backbone
Apache+mod_SSL - one switch

1.5e+07
1e+07
5e+06
0
-5e+06-1e+07-1.5e+07-2e+07

0

50

100

150

200

250

0

Bits guessed of factor q

50

100

150

200

250

Bits guessed of factor q(a) The zero-one gaps when attacking Apache+mod SSL
and stunnel separated by one switch.

(b) The zero-one gap when attacking Apache+mod SSL
separated by several routers and a network backbone.

Figure 7: Applications using OpenSSL 0.9.7 are vulnerable, even on a large network.
Inter-network attacks allow an attacker to also take advantage of faster CPU speeds for increasing the accuracy of timing measurements. Consider machine 1 witha slower CPU than machine 2. Then if machine 2 attacks machine 1, the faster clock cycle allows for finer
grained measurements of the decryption time on machine 1. Finer grained measurements should result in
fewer queries for the attacker, as the zero-one gap will
be more distinct.

5.7 Experiment 6 - Attacking SSL Applications
on the Local Network
We show that OpenSSL applications are vulnerable toour attack from the network. We compiled Apache
1.3.27 + mod SSL 2.8.12 and stunnel 4.04 per the respective “INSTALL” files accompanying the software.
Apache+mod SSL is a commonly used secure web
server. stunnel allows TCP/IP connections to be tunneled through SSL.
We begin by showing servers connected by a single
switch are vulnerable to our attack. This scenario is relevant when the attacker has access to a machine near
the OpenSSL-based server. Figure 7(a) shows the resultof attacking stunnel and mod SSL where the attacking
client is separated by a single switch. For reference, we
also include the results for a similar attack against thesimple RSA decryption server from the previous experiments.
Interestingly, the zero-one gap is larger for
Apache+mod SSL than either the simple RSA de-cryption server or stunnel. As a result, successfully
attacking Apache+mod SSL requires fewer queries
than stunnel. Both applications have a sufficiently large
zero-one gap to be considered vulnerable.
To show our timing attacks can work on larger networks, we separated the attacking client from theApache+mod SSL server by our campus backbone. The
webserver was hosted in a separate building about a halfmile away, separated by three routers and a number ofswitches on the network backbone. Figure 7(b) showsthe effectiveness of our attack against Apache+mod SSL
on this larger LAN, contrasted with our previous experiment where the attacking client and server are separated
by only one switch.
This experiment highlights the difficulty in determining
the minimum number of queries for a successful attack.
Even though both stunnel and mod SSL use the exact
same OpenSSL libraries and use the same parameters fornegotiating the SSL handshake, the run-time differences
result in different zero-one gaps. More importantly, ourattack works even when the attacking client and application are separated by a large network.

6

Defenses

We discuss three possible defenses. The most widely
accepted defense against timing attacks is to perform
RSA blinding. The RSA blinding operation calculates
x = r e g mod N before decryption, where r is random,e is the RSA encryption exponent, and g is the ciphertext

to be decrypted. x is then decrypted as normal, followedby division by r, i.e. xe /r mod N . Since r is random,x is random and timing the decryption should not reveal
information about the key. Note that r should be a new
random number for every decryption. According to [17]the performance penalty is 2% − 10%, depending upon
implementation. Netscape/Mozilla’s NSS library uses
blinding. Blinding is available in OpenSSL, but not enabled by default in versions prior to 0.9.7b. Figure 8
shows that blinding in OpenSSL 0.9.7b defeats our attack. We hope this paper demonstrates the necessity ofenabling this defense.

Time difference in CPU cycles

3e+06

Apache with blinding (bit=0)
Apache with blinding (bit=1)

2e+06

tions must take the maximum time of any decryption,otherwise, timing information can still be used to leakinformation about the secret key.
Currently, the preferred method for protecting against
timing attacks is to use RSA blinding. The immediate drawbacks to this solution is that a good source ofrandomness is needed to prevent attacks on the blinding
factor, as well as the small performance degradation. In
OpenSSL, neither drawback appears to be a significant
problem.

7

Conclusion

1e+06
0-1e+06-2e+06-3e+06-4e+06-5e+06-6e+06-7e+06-8e+06
0

50

100
150
Bits guessed of factor q

200

250

Figure 8: Our attack against Apache+mod SSL using
OpenSSL 0.9.7b is defeated because blinding is enabled
by default.

We devised and implemented a timing attack against
OpenSSL — a library commonly used in web servers
and other SSL applications. Our experiments show that,counter to current belief, the timing attack is effective
when carried out between machines separated by multiple routers. Similarly, the timing attack is effective between two processes on the same machine and two Virtual Machines on the same computer. As a result of thiswork, several crypto libraries, including OpenSSL, now
implement blinding by default as described in the previous section.

Two other possible defenses are suggested often, but area second choice to blinding. The first is to try and makeall RSA decryptions not dependent upon the input ciphertext. In OpenSSL one would use only one multiplication routine and always carry out the extra reduction
in Montgomery’s algorithm, as proposed by Schindlerin [18]. If an extra reduction is not needed, we carryout a “dummy” extra reduction and do not use the result.
Karatsuba multiplication can always be used by calculating c mod pi ∗ 2m , where c is the ciphertext, pi is oneof the RSA factors, and m = log2 pi − log2 (c mod pi ).
After decryption, the result is divided by 2m d mod q toyield the plaintext. We believe it is harder to create andmaintain code where the decryption time is not dependent upon the ciphertext. For example, since the result isnever used from a dummy extra reduction during Montgomery reductions, it may inadvertently be optimized
away by the compiler.

This material is based upon work supported in partby the National Science Foundation under Grant No.
0121481 and the Packard Foundation. We thank the reviewers, Dr. Monica Lam, Ramesh Chandra, Constantine Sapuntzakis, Wei Dai, Art Manion and CERT/CC,
and Dr. Werner Schindler for their comments while
preparing this paper. We also thank Nelson Bolyard, Geoff Thorpe, Ben Laurie, Dr. Stephen Henson, Richard
Levitte, and the rest of the OpenSSL, mod SSL, andstunnel development teams for their help in preparing
patches to enable and use RSA blinding.

Another alternative is to require all RSA computations
to be quantized, i.e. always take a multiple of some predefined time quantum. Matt Blaze’s quantize library [1]is an example of this approach. Note that all decryp-[1] Matt Blaze.
Quantize wrapper library.
http://islab.oregonstate.edu/
documents/People/blaze.

8

Acknowledgments

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