A regular expression denial of service (ReDoS) is an algorithmic complexity attack that produces a denial-of-service by providing a regular expression (regex) and/or an input that takes a long time to evaluate. The attack exploits the fact that many regular expression implementations have super-linear worst-case complexity; on certain regex-input pairs, the time taken can grow polynomially or exponentially in relation to the input size. An attacker can thus cause a program to spend substantial time by providing a specially crafted regular expression and/or input. The program will then slow down or become unresponsive.
Description Regular expression ("regex") matching can be done by building a finite-state automaton. Regex can be easily converted to nondeterministic automata (NFAs), in which, for each state and input symbol, there may be several possible next states. After building the automaton, several possibilities exist:
the engine may convert it to a deterministic finite-state automaton (DFA) and run the input through the result; the engine may try all the possible paths one by one until a match is found or until all the paths are tried and fail ("backtracking"). the engine may consider all possible paths through the nondeterministic automaton in parallel; the engine may convert the nondeterministic automaton to a DFA lazily (i.e., on the fly, during the match). Of the above algorithms, the first two are problematic. The first is problematic because a deterministic automaton could have up to 2 m {\displaystyle 2^{m}} states where m {\displaystyle m} is the number of states in the nondeterministic automaton; thus, the conversion from NFA to DFA may take exponential time. The second is problematic because a nondeterministic automaton could have an exponential number of paths of length n {\displaystyle n} , so that walking through an input of length n {\displaystyle n} will also take exponential time. The last two algorithms, however, do not exhibit pathological behavior. Note that for non-pathological regular expressions, the problematic algorithms are usually fast, and in practice, one can expect them to "compile" a regex in O ( m ) {\displaystyle O(m)} time and match it in O ( n ) {\displaystyle O(n)} time; instead, simulation of an NFA and lazy computation of the DFA have O ( m ⋅ 2 n ) {\displaystyle O(m\cdot 2^{n})} worst-case complexity. Regex denial of service occurs when these expectations are applied to a regex provided by the user, and malicious regular expressions provided by the user trigger the worst-case complexity of the regex matcher. While regex algorithms can be written in an efficient way, most regex engines in existence extend the regex languages with additional constructs that cannot always be solved efficiently. Such extended patterns essentially force the implementation of regex in most programming languages to use backtracking.
Examples
Exponential backtracking The most severe type of problem happens with backtracking regular expression matches, where some patterns have a runtime that is exponential in the length of the input string. For strings of n {\displaystyle n} characters, the runtime is O ( 2 n ) {\displaystyle O(2^{n})} . This happens when a regular expression has three properties:
the regular expression applies repetition (+, *) to a subexpression; the subexpression can match the same input in multiple ways, or the subexpression can match an input string which is a prefix of a longer possible match; and after the repeated subexpression, there is an expression that matches something which the subexpression does not match. The second condition is best explained with two examples:
in (a|a)+$, repetition is applied to the subexpression a|a, which can match a in two ways on each side of the alternation. in (a+)*$, repetition is applied to the subexpression a+, which can match a or aa, etc. In both of these examples we used $ to match the end of the string, satisfying the third condition, but it is also possible to use another character for this. For example (a|aa)*c has the same problematic structure. All three of the above regular expressions will exhibit exponential runtime when applied to strings of the form a . . . a x {\displaystyle a...ax} . For example, trying to match them against aaaaaaaaaaaaaaaaaaaaaaaax on a backtracking expression engine, it will take a significantly long time to complete, and the runtime will approximately double for each extra a before the x. It is also possible to have backtracking which is polynomial time O ( n x ) {\displaystyle O(n^{x})} , instead of exponential. This can also cause problems for long enough inputs, though less attention has been paid to this problem as malicious input must be much longer to have a significant effect. An example of such a pattern is "a*b?a*c", when the input is an arbitrarily long sequence of "a"s.
Vulnerable regexes in online repositories So-called "evil" or vulnerable regexes have been found in online regular expression repositories. Note that it is enough to find a vulnerable subexpression in order to attack the full regex:
RegExLib, id=1757 (email validation) – see red part^([a-zA-Z0-9])(([\-.]|[_]+)?([a-zA-Z0-9]+))*(@){1}[a-z0-9]+[.]{1}(([a-z]{2,3})|([a-z]{2,3}[.]{1}[a-z]{2,3}))$ OWASP Validation Regex Repository, Java Classname – see red part^(([a-z])+.)+[A-Z]([a-z])+$ These two examples are also susceptible to the input aaaaaaaaaaaaaaaaaaaaaaaa!.
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