Weak Keys in RC4

This is an archive of the original posts Andrew Roos made in 1995 to announce his discovery of weak keys in RC4. Even though most Wireless LAN access points avoid these keys since many years, this discovery eventually led to abandoning RC4 for use in WEP („Wired Equivalent Privacy“) for Wi-Fi networks, as the privacy was no longer anywhere near wire-equivalent. The second part of his research is in the next post.

From: andrewr@vironix.co.za (Andrew Roos)
Weak Keys in RC4
22 Sep 1995 09:59:45 GMT






This paper discusses a class of weak keys in RSA’s RC4 stream cipher. It shows
that for at least 1 out of every 256 possible keys the initial byte of the
pseudo-random stream generated by RC4 is strongly correlated with only a few
bytes of the key, which effecitively reduces the work required to exhaustively
search RC4 key spaces.


Although the RC4 algorithm has not been published by RSA Data Security, source
code to implement the algorithm was anonymously posted to the Cypherpunks
mailing list several months ago. The success of the Cypherpunks‘ brute-force
attack on SSL with a 40-bit key indicates that the source code published did
accurately implement RC4.

RC4 uses a variable length key from 1 to 256 bytes to initialize a 256-byte
state table which is used for the subsequent generation of pseudo-random bytes.
The state table is first initialized to the sequence {0,1,2,…,255}. Then:

1    index1 = 0;
2    index2 = 0;
4    for(counter = 0; counter < 256; counter++)
5    {
6        index2 = (key_data_ptr[index1] + state[counter] + index2) % 256;
7        swap_byte(&state[counter], &state[index2]);
8        index1 = (index1 + 1) % key_data_len;
9    }

Note that the only line which directly affects the state table is line 7, when
two bytes in the table are exchanged. The first byte is indexed by „counter“,
which is incremented for each iteration of the loop. The second byte is
indexed by „index2“ which is a function of the key. Hence each element of the
state table will be swapped at least once (although possibly with itself),
when it is indexed by „counter“. It may also be swapped zero, one or more
times when it is indexed by „index2“. If we assume for the moment that
„index2“ is a uniformly distributed pseudo-random number, then the probability
that a particular single element of the state table will be indexed by
„index2“ at some time during the initialization routine is:

     P = 1 - (255/256) ^ 255
       = 0.631

(The exponent is 255 because we can disregard the case when „index2“ and
„counter“ both index the same element, since this will not affect its value.)

Conversely, there is a 37% probability that a particular element will _not_ be
indexed by „index2“ during initialization, so its final value in the state
table will only be affected by a single swap, when it is indexed by „counter“.
Since key bytes are used sequentially (starting again at the beginning when the
key is exhausted), this implies:

A.   Given a key length of K bytes, and E < K, there is a 37% probability that
     element E of the state table depends only on elements 0..E (inclusive) of
     the key.

(This is approximate since „index2“ is unlikely to be uniformly distributed.)

In order to make use of this, we need to determine the most likely values for
elements of the state table. Since each element is swapped at least once (when
it is indexed by „counter“), it is necessary to take into account the likely
effect of this swap. Swapping is a nasty non-linear process which is hard to
analyze. However, when dealing with the first few elements of the state table,
there is a high probability that the byte with which the element is swapped
has not itself been involved in any previous exchanges, and therefore retains
its initial value {0,1,2,…,255}. Similarly, when dealing with the first few
elements of the state table, there is also a significant probability that none
of the state elements added to index2 in line 6 of the algorithm has been
swapped either.

This means that the most likely value of an element in the state table can be
estimated by assuming that state[x] == x in the algorithm above. In this case,
the algorithm becomes:

1    index1 = 0;
2    index2 = 0;
5    {
6        index2 = (key_data_ptr[index1] + counter + index2) % 256;
7        state[counter] = index2;
8        index1 = (index1 + 1) % key_data_len;
9    }

Which can be reduced to:

B.   The most likely value for element E of the state table is:

     S[E] = X(E) + E(E+1)/2
     where X(E) is the sum of bytes 0..E (inclusive) of the key.

(when calculating the sum of key elements, the key is considered to „wrap
around“ on itself).

Given this analysis, we can calculate the probability for each element of the
state table that it’s value is the „most likely value“ of B above. The easiest
way to do this is to evaluate the state tables produced from a number of
pseudo-randomly generated RC4 keys. The following table shows the results for
the first 47 elements from a trial of 100 000 eighty-bit RC4 keys:

          Probability (%)

0-7       37.0  36.8  36.2  35.8  34.9  34.0  33.0  32.2
8-15      30.9  29.8  28.5  27.5  26.0  24.5  22.9  21.6
16-23     20.3  18.9  17.3  16.1  14.7  13.5  12.4  11.2
24-31     10.1   9.0   8.2   7.4   6.4   5.7   5.1   4.4
32-39      3.9   3.5   3.0   2.6   2.3   2.0   1.7   1.4
40-47      1.3   1.2   1.0   0.9   0.8   0.7   0.6   0.6

The table confirms that there is a significant correlation between the first
few values in the state table and the „likely value“ predicted by B.


The RC4 state table is used to generate a pseudo-random stream which is XORed
with the plaintext to give the ciphertext. The algorithm used to generate the
stream is as follows:

     x and y are initialized to 0.

     To generate each byte:

1    x = (x + 1) % 256;
2    y = (state[x] + y) % 256;
3    swap_byte(&state[x], &state[y]);
4    xorIndex = (state[x] + state[y]) % 256;
5    GeneratedByte = state[xorIndex];

One way to exploit our analysis of the state table is to find circumstances
under which one or more generated bytes are strongly correlated with a small
subset of the key bytes.

Consider what happens when generating the first byte if state[1] == 1.

1    x = (0 + 1) % 256;                  /* x == 1 */
2    y = (state[1] + 0) % 256;           /* y == 1 */
3    swap_byte(&state[1], &state[1]);    /* no effect */
4    xorIndex = (state[1] + state[1]);   /* xorIndex = 2 */
5    GeneratedByte = state[2]

And we know that state[2] is has a high probability of being

     S[2] = K[0] + K[1] + K[2] + 2 (2+1) / 2


     S[1] = K[0] + K[1] + 1 (1+1) / 2

So to make it probable that S[1] == 1, we have:

     K[0] + K[1] == 0 (mod 256)

In which case the most likely value for S[2] is:

     S[2] = K[2] + 3

This allows us to identify a class of weak keys:

C.   Given an RC4 key K[0]..K[N] with K[0] + K[1] == 0 (mod 256), there is a
     significant probability that the first byte generated by RC4 will be
     K[2] + 3 (mod 256).

Note that there are two special cases, caused by „unexpected“ swapping during
key generation. When K[0]==1, the „expected“ output byte is k[2] + 2, and when
k[0]==2, the expected value is k[2] + 1.

There are a number of similar classes of „weak keys“ which only affect a few
keys out of every 65536. However the particular symmetry in this class means
that it affects one key in 256, making it the most interesting instance.

Once again I took the easy way out and used simulation to determine the
approximate probability that result C holds for any given key. Probabilities
ranged between 12% and 16% depending on the values of K[0] and K[1], with a
mean of about 13.8%. All these figures are significantly greater than the
0.39% which would be expected from an uncorrelated generator. The key length
used was again 80 bits. This works the other way around as well: given the
first byte B[0] generated by a weak key, the probability that K[2]==B[0]-3
(mod 256) is 13.8%.


Having found a class of weak keys, we need a practical way to attack RC4 based
cryptosystems using them. The most obvious way would be to search potential
weak keys first during an exhaustive attack. However since only one in every
256 keys is weak, the effective reduction in search space is not particularly

The usefulness of weak keys does increase if the opponent is satisfied with
recovering only a percentage of the keys subjected to analysis. Given a known
generator output which includes the first generated byte, one could assume
that the key was weak and search only the weak keys which would generate the
known initial byte. Since 1 in 256 keys is weak, and there is a 13.8% chance
that the assumed value of K[2] will be correct, there is only a 0.054% chance
of finding the key this way. However, you have reduced the search space by 16
bits due to the assumed relationship between K[0] and K[1] and the assumed
value of K[2], so the work factor per key recovered is reduced by a factor of
35, which is equivalent reducing the effective key length by 5.1 bits.

However in particular circumstances, the known relationships between weak keys
may provide a much more significant reduction in workload. The remainder of
this section describes an attack which, although requiring very specific
conditions, illustrates the potential threat.

As a stream cipher, a particular RC4 key can only be used once. When multiple
communications sessions are required, some mechanism must be provided for
generating a new session key each time. Let us suppose that an implementation
chose the simple method of incrementing the previous session key to get the
new session key, and that the session key was treated as a „little endian“
(least significant byte first) integer for this purpose.

We now have the interesting situation that the session keys will „cycle
through“ weak keys in a pattern which repeats every 2^16 keys:

00 00 00 ...    Weak
(510 non-weak keys)
FF 01 00 ...    Weak
(254 non-weak keys)
FE 02 00 ...    Weak
(254 non-weak keys)
FD 03 00 ...    Weak
01 FF 00 ...    Weak
(254 non-weak keys)
00 00 01 ...    Weak
(510 non-weak keys)
FF 01 01 ...    Weak

(Least significant byte on the left)

Now while an isolated weak key cannot be identified simply from a known
generator output, this cycle of weak keys at known intervals can be identified
using statistical techniques since each of the weak keys has a higher than
expected probability of generating the _same_ initial byte. This means that an
opponent who knew the initial generated bytes of about 2^16 session keys could
identify the weak keys, and would also be able to locate the 510-key gap
between successive cycles of weak keys (although not precisely). Since the
510-key gap occurs immediately following a key which begins with 00 00, the
opponent not only knows that the keys are weak, but also knows the first two
bytes of each key. The third byte of each key can be guessed from the first
output byte generated by the key, with a 13.8% chance of a correct guess.
Assuming that the „510-key gap“ is narrowed down to 1 of 8 weak keys, the
attacker can search a key space which is 24 bits less than the size of the
session keys, with a 13.8%/8 chance of success, effectively reducing the key
space by approximately 18 bits.

Although this particular attack depends on a very specific set of
circumstances, it is likely that other RC4 based cryptosystems in which there
are linear relationships between successive session keys could be vulnerable
to similar attacks.


The attacks described in this algorithm result from inadequate „mixing“ of key
bytes during the generation of the RC4 state table. The following measures
could be taken to strengthen cryptosystems based on the RC4 algorithm:

(a) After initializing the algorithm, generate and discard a number of bytes.
    Since the algorithm used to generate bytes also introduces additional
    non-linear dependencies into the state table, this would make analysis
    more difficult.

(b) In systems which require multiple session keys, ensure that session keys
    are not linearly related to each other.

(c) Avoid using the weak keys described.


This preliminary analysis of RC4 shows that the algorithm is vulnerable to
analytic attacks based on statistical analysis of its state table. It is
likely that a more detailed analysis of the algorithm will reveal more
effective ways to exploit the weaknesses described.

Andrew Roos <andrewr@vironix.co.za>
Version: 2.6.2i


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