security.snyk.io Open in urlscan Pro
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Form analysis
2 forms found in the DOM

<form id="mktoForm_1461" style="display: none; font-family: Helvetica, Arial, sans-serif; font-size: 13px; color: rgb(51, 51, 51); width: 1px;" novalidate="novalidate" class="mktoForm mktoHasWidth mktoLayoutLeft">
  <style type="text/css">
    .mktoForm .mktoButtonWrap.mktoSimple .mktoButton {
      color: #fff;
      border: 1px solid #75ae4c;
      padding: 0.4em 1em;
      font-size: 1em;
      background-color: #99c47c;
      background-image: -webkit-gradient(linear, left top, left bottom, from(#99c47c), to(#75ae4c));
      background-image: -webkit-linear-gradient(top, #99c47c, #75ae4c);
      background-image: -moz-linear-gradient(top, #99c47c, #75ae4c);
      background-image: linear-gradient(to bottom, #99c47c, #75ae4c);
    }

    .mktoForm .mktoButtonWrap.mktoSimple .mktoButton:hover {
      border: 1px solid #447f19;
    }

    .mktoForm .mktoButtonWrap.mktoSimple .mktoButton:focus {
      outline: none;
      border: 1px solid #447f19;
    }

    .mktoForm .mktoButtonWrap.mktoSimple .mktoButton:active {
      background-color: #75ae4c;
      background-image: -webkit-gradient(linear, left top, left bottom, from(#75ae4c), to(#99c47c));
      background-image: -webkit-linear-gradient(top, #75ae4c, #99c47c);
      background-image: -moz-linear-gradient(top, #75ae4c, #99c47c);
      background-image: linear-gradient(to bottom, #75ae4c, #99c47c);
    }
  </style>
  <div class="mktoButtonRow"><span class="mktoButtonWrap mktoSimple" style="margin-left: 120px;"><button type="submit" class="mktoButton">Submit</button></span></div><input type="hidden" name="formid" class="mktoField mktoFieldDescriptor"
    value="1461"><input type="hidden" name="munchkinId" class="mktoField mktoFieldDescriptor" value="677-THP-415">
</form>

<form style="display: none; font-family: Helvetica, Arial, sans-serif; font-size: 13px; color: rgb(51, 51, 51); visibility: hidden; position: absolute; top: -500px; left: -1000px; width: 1600px;" novalidate="novalidate"
  class="mktoForm mktoHasWidth mktoLayoutLeft"></form>

Text Content

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About Snyk
 1. Snyk Vulnerability Database
 2. npm
 3. http-cache-semantics




REGULAR EXPRESSION DENIAL OF SERVICE (REDOS) AFFECTING HTTP-CACHE-SEMANTICS
PACKAGE, VERSIONS <4.1.1

--------------------------------------------------------------------------------

5.3
medium


SNYK CVSS

Attack Complexity Low


See more
Expand this section
Red Hat
5.3 medium

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 * Snyk ID SNYK-JS-HTTPCACHESEMANTICS-3248783
 * published 30 Jan 2023
 * disclosed 26 Jan 2023
 * credit Carter Snook

Report a new vulnerability Found a mistake?

INTRODUCED: 26 JAN 2023

New CVE-2022-25881 Open this link in a new tab

CWE-1333 Open this link in a new tab

First added by Snyk
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HOW TO FIX?

Upgrade http-cache-semantics to version 4.1.1 or higher.


OVERVIEW

Affected versions of this package are vulnerable to Regular Expression Denial of
Service (ReDoS). The issue can be exploited via malicious request header values
sent to a server, when that server reads the cache policy from the request using
this library.


POC

Steps to reproduce:

Run the following script in Node.js after installing the http-cache-semantics
NPM package:

const CachePolicy = require("http-cache-semantics");



for (let i = 0; i <= 5; i++) {


const attack = "a" + " ".repeat(i * 7000) +
"z";


const start = performance.now();
new CachePolicy({
headers: {},
}, {
headers: {
"cache-control": attack,
},


});
console.log(${attack.length}: ${performance.now() - start}ms);
}





DETAILS

Denial of Service (DoS) describes a family of attacks, all aimed at making a
system inaccessible to its original and legitimate users. There are many types
of DoS attacks, ranging from trying to clog the network pipes to the system by
generating a large volume of traffic from many machines (a Distributed Denial of
Service - DDoS - attack) to sending crafted requests that cause a system to
crash or take a disproportional amount of time to process.

The Regular expression Denial of Service (ReDoS) is a type of Denial of Service
attack. Regular expressions are incredibly powerful, but they aren't very
intuitive and can ultimately end up making it easy for attackers to take your
site down.

Let’s take the following regular expression as an example:

regex = /A(B|C+)+D/


This regular expression accomplishes the following:

 * A The string must start with the letter 'A'
 * (B|C+)+ The string must then follow the letter A with either the letter 'B'
   or some number of occurrences of the letter 'C' (the + matches one or more
   times). The + at the end of this section states that we can look for one or
   more matches of this section.
 * D Finally, we ensure this section of the string ends with a 'D'

The expression would match inputs such as ABBD, ABCCCCD, ABCBCCCD and ACCCCCD

It most cases, it doesn't take very long for a regex engine to find a match:

$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCD")'
0.04s user 0.01s system 95% cpu 0.052 total



$ time node -e '/A(B|C+)+D/.test("ACCCCCCCCCCCCCCCCCCCCCCCCCCCCX")'
1.79s user 0.02s system 99% cpu 1.812 total




The entire process of testing it against a 30 characters long string takes
around ~52ms. But when given an invalid string, it takes nearly two seconds to
complete the test, over ten times as long as it took to test a valid string. The
dramatic difference is due to the way regular expressions get evaluated.

Most Regex engines will work very similarly (with minor differences). The engine
will match the first possible way to accept the current character and proceed to
the next one. If it then fails to match the next one, it will backtrack and see
if there was another way to digest the previous character. If it goes too far
down the rabbit hole only to find out the string doesn’t match in the end, and
if many characters have multiple valid regex paths, the number of backtracking
steps can become very large, resulting in what is known as catastrophic
backtracking.

Let's look at how our expression runs into this problem, using a shorter string:
"ACCCX". While it seems fairly straightforward, there are still four different
ways that the engine could match those three C's:

 1. CCC
 2. CC+C
 3. C+CC
 4. C+C+C.

The engine has to try each of those combinations to see if any of them
potentially match against the expression. When you combine that with the other
steps the engine must take, we can use RegEx 101 debugger to see the engine has
to take a total of 38 steps before it can determine the string doesn't match.

From there, the number of steps the engine must use to validate a string just
continues to grow.

String Number of C's Number of steps ACCCX 3 38 ACCCCX 4 71 ACCCCCX 5 136
ACCCCCCCCCCCCCCX 14 65,553

By the time the string includes 14 C's, the engine has to take over 65,000 steps
just to see if the string is valid. These extreme situations can cause them to
work very slowly (exponentially related to input size, as shown above), allowing
an attacker to exploit this and can cause the service to excessively consume
CPU, resulting in a Denial of Service.


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