Find, fix and prevent vulnerabilities in your code.
high severity
- Vulnerable module: lodash
- Introduced through: airtable@0.8.1
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › airtable@0.8.1 › lodash@4.17.15Remediation: Upgrade to airtable@0.10.1.
Overview
lodash is a modern JavaScript utility library delivering modularity, performance, & extras.
Affected versions of this package are vulnerable to Prototype Pollution. The function zipObjectDeep
can be tricked into adding or modifying properties of the Object prototype. These properties will be present on all objects.
PoC
const _ = require('lodash');
_.zipObjectDeep(['__proto__.z'],[123]);
console.log(z); // 123
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named __proto__
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to __proto__.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
Web browser
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade lodash
to version 4.17.20 or higher.
References
high severity
- Vulnerable module: axios
- Introduced through: axios@0.19.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › axios@0.19.2Remediation: Upgrade to axios@0.21.3.
Overview
axios is a promise-based HTTP client for the browser and Node.js.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the trim
function.
PoC
// poc.js
var {trim} = require("axios/lib/utils");
function build_blank (n) {
var ret = "1"
for (var i = 0; i < n; i++) {
ret += " "
}
return ret + "1";
}
var time = Date.now();
trim(build_blank(50000))
var time_cost = Date.now() - time;
console.log("time_cost: " + time_cost)
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:
- CCC
- CC+C
- C+CC
- 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.
Remediation
Upgrade axios
to version 0.21.3 or higher.
References
high severity
- Vulnerable module: lodash
- Introduced through: airtable@0.8.1
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › airtable@0.8.1 › lodash@4.17.15Remediation: Upgrade to airtable@0.10.0.
Overview
lodash is a modern JavaScript utility library delivering modularity, performance, & extras.
Affected versions of this package are vulnerable to Prototype Pollution through the zipObjectDeep
function due to improper user input sanitization in the baseZipObject
function.
PoC
lodash.zipobjectdeep:
const zipObjectDeep = require("lodash.zipobjectdeep");
let emptyObject = {};
console.log(`[+] Before prototype pollution : ${emptyObject.polluted}`);
//[+] Before prototype pollution : undefined
zipObjectDeep(["constructor.prototype.polluted"], [true]);
//we inject our malicious attributes in the vulnerable function
console.log(`[+] After prototype pollution : ${emptyObject.polluted}`);
//[+] After prototype pollution : true
lodash:
const test = require("lodash");
let emptyObject = {};
console.log(`[+] Before prototype pollution : ${emptyObject.polluted}`);
//[+] Before prototype pollution : undefined
test.zipObjectDeep(["constructor.prototype.polluted"], [true]);
//we inject our malicious attributes in the vulnerable function
console.log(`[+] After prototype pollution : ${emptyObject.polluted}`);
//[+] After prototype pollution : true
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named __proto__
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to __proto__.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
Web browser
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade lodash
to version 4.17.17 or higher.
References
high severity
- Vulnerable module: ws
- Introduced through: pdf-puppeteer@1.1.11
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › pdf-puppeteer@1.1.11 › puppeteer@21.11.0 › puppeteer-core@21.11.0 › ws@8.16.0
Overview
ws is a simple to use websocket client, server and console for node.js.
Affected versions of this package are vulnerable to Denial of Service (DoS) when the number of received headers exceed the server.maxHeadersCount
or request.maxHeadersCount
threshold.
Workaround
This issue can be mitigating by following these steps:
Reduce the maximum allowed length of the request headers using the
--max-http-header-size=size
and/or themaxHeaderSize
options so that no more headers than theserver.maxHeadersCount
limit can be sent.Set
server.maxHeadersCount
to 0 so that no limit is applied.
PoC
const http = require('http');
const WebSocket = require('ws');
const server = http.createServer();
const wss = new WebSocket.Server({ server });
server.listen(function () {
const chars = "!#$%&'*+-.0123456789abcdefghijklmnopqrstuvwxyz^_`|~".split('');
const headers = {};
let count = 0;
for (let i = 0; i < chars.length; i++) {
if (count === 2000) break;
for (let j = 0; j < chars.length; j++) {
const key = chars[i] + chars[j];
headers[key] = 'x';
if (++count === 2000) break;
}
}
headers.Connection = 'Upgrade';
headers.Upgrade = 'websocket';
headers['Sec-WebSocket-Key'] = 'dGhlIHNhbXBsZSBub25jZQ==';
headers['Sec-WebSocket-Version'] = '13';
const request = http.request({
headers: headers,
host: '127.0.0.1',
port: server.address().port
});
request.end();
});
Details
Denial of Service (DoS) describes a family of attacks, all aimed at making a system inaccessible to its intended and legitimate users.
Unlike other vulnerabilities, DoS attacks usually do not aim at breaching security. Rather, they are focused on making websites and services unavailable to genuine users resulting in downtime.
One popular Denial of Service vulnerability is DDoS (a Distributed Denial of Service), an attack that attempts to clog network pipes to the system by generating a large volume of traffic from many machines.
When it comes to open source libraries, DoS vulnerabilities allow attackers to trigger such a crash or crippling of the service by using a flaw either in the application code or from the use of open source libraries.
Two common types of DoS vulnerabilities:
High CPU/Memory Consumption- An attacker sending crafted requests that could cause the system to take a disproportionate amount of time to process. For example, commons-fileupload:commons-fileupload.
Crash - An attacker sending crafted requests that could cause the system to crash. For Example, npm
ws
package
Remediation
Upgrade ws
to version 5.2.4, 6.2.3, 7.5.10, 8.17.1 or higher.
References
high severity
- Vulnerable module: airtable
- Introduced through: airtable@0.8.1
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › airtable@0.8.1Remediation: Upgrade to airtable@0.11.6.
Overview
airtable is a javascript client for Airtable.
Affected versions of this package are vulnerable to Insufficiently Protected Credentials due to the usage of misconfigured build script in its source package, which bundles environment variables (AIRTABLE_API_KEY
and AIRTABLE_ENDPOINT_URL
) into the build target of a transpiled bundle.
NOTE: This vulnerability is relevant only if all of the following conditions are met:
the user has cloned the
Airtable.js
source onto their machine.the user runs the npm prepare script
the user has the
AIRTABLE_API_KEY
environment variable set.
Workaround
- Unset the
AIRTABLE_API_KEY
environment variable in your shell and/or remove it from your.bashrc
,.zshrc
, or other shell configuration files.
Recommendations
Regenerate any Airtable API keys you use via https://airtable.com/account, as they may be present in bundled code.
Remediation
Upgrade airtable
to version 0.11.6 or higher.
References
high severity
- Vulnerable module: follow-redirects
- Introduced through: axios@0.19.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › axios@0.19.2 › follow-redirects@1.5.10Remediation: Upgrade to axios@0.20.0.
Overview
Affected versions of this package are vulnerable to Improper Handling of Extra Parameters due to the improper handling of URLs by the url.parse()
function. When new URL()
throws an error, it can be manipulated to misinterpret the hostname. An attacker could exploit this weakness to redirect traffic to a malicious site, potentially leading to information disclosure, phishing attacks, or other security breaches.
PoC
# Case 1 : Bypassing localhost restriction
let url = 'http://[localhost]/admin';
try{
new URL(url); // ERROR : Invalid URL
}catch{
url.parse(url); // -> http://localhost/admin
}
# Case 2 : Bypassing domain restriction
let url = 'http://attacker.domain*.allowed.domain:a';
try{
new URL(url); // ERROR : Invalid URL
}catch{
url.parse(url); // -> http://attacker.domain/*.allowed.domain:a
}
Remediation
Upgrade follow-redirects
to version 1.15.4 or higher.
References
high severity
- Vulnerable module: lodash
- Introduced through: airtable@0.8.1
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › airtable@0.8.1 › lodash@4.17.15Remediation: Upgrade to airtable@0.10.0.
Overview
lodash is a modern JavaScript utility library delivering modularity, performance, & extras.
Affected versions of this package are vulnerable to Prototype Pollution via the set
and setwith
functions due to improper user input sanitization.
PoC
lod = require('lodash')
lod.set({}, "__proto__[test2]", "456")
console.log(Object.prototype)
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named __proto__
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to __proto__.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
Web browser
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade lodash
to version 4.17.17 or higher.
References
high severity
- Vulnerable module: lodash
- Introduced through: airtable@0.8.1
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › airtable@0.8.1 › lodash@4.17.15Remediation: Upgrade to airtable@0.10.1.
Overview
lodash is a modern JavaScript utility library delivering modularity, performance, & extras.
Affected versions of this package are vulnerable to Code Injection via template
.
PoC
var _ = require('lodash');
_.template('', { variable: '){console.log(process.env)}; with(obj' })()
Remediation
Upgrade lodash
to version 4.17.21 or higher.
References
high severity
- Vulnerable module: axios
- Introduced through: axios@0.19.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › axios@0.19.2Remediation: Upgrade to axios@0.28.0.
Overview
axios is a promise-based HTTP client for the browser and Node.js.
Affected versions of this package are vulnerable to Cross-site Request Forgery (CSRF) due to inserting the X-XSRF-TOKEN
header using the secret XSRF-TOKEN
cookie value in all requests to any server when the XSRF-TOKEN
0 cookie is available, and the withCredentials
setting is turned on. If a malicious user manages to obtain this value, it can potentially lead to the XSRF defence mechanism bypass.
Workaround
Users should change the default XSRF-TOKEN
cookie name in the Axios configuration and manually include the corresponding header only in the specific places where it's necessary.
Remediation
Upgrade axios
to version 0.28.0, 1.6.0 or higher.
References
medium severity
- Vulnerable module: follow-redirects
- Introduced through: axios@0.19.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › axios@0.19.2 › follow-redirects@1.5.10Remediation: Upgrade to axios@0.20.0.
Overview
Affected versions of this package are vulnerable to Information Exposure due to the handling of the Proxy-Authorization
header across hosts. When using a dependent library, it only clears the authorization header during cross-domain redirects but allows the proxy-authentication header, which contains credentials, to persist. This behavior may lead to the unintended leakage of credentials if an attacker can trigger a cross-domain redirect and capture the persistent proxy-authentication header.
PoC
const axios = require('axios');
axios.get('http://127.0.0.1:10081/',{
headers: {
'AuThorization': 'Rear Test',
'ProXy-AuthoriZation': 'Rear Test',
'coOkie': 't=1'
}
}).then(function (response) {
console.log(response);
})
Remediation
Upgrade follow-redirects
to version 1.15.6 or higher.
References
medium severity
- Vulnerable module: request
- Introduced through: airtable@0.8.1
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › airtable@0.8.1 › request@2.88.0
Overview
request is a simplified http request client.
Affected versions of this package are vulnerable to Server-side Request Forgery (SSRF) due to insufficient checks in the lib/redirect.js
file by allowing insecure redirects in the default configuration, via an attacker-controller server that does a cross-protocol redirect (HTTP to HTTPS, or HTTPS to HTTP).
NOTE: request
package has been deprecated, so a fix is not expected. See https://github.com/request/request/issues/3142.
Remediation
A fix was pushed into the master
branch but not yet published.
References
medium severity
- Vulnerable module: tough-cookie
- Introduced through: airtable@0.8.1
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › airtable@0.8.1 › request@2.88.0 › tough-cookie@2.4.3
Overview
tough-cookie is a RFC6265 Cookies and CookieJar module for Node.js.
Affected versions of this package are vulnerable to Prototype Pollution due to improper handling of Cookies when using CookieJar in rejectPublicSuffixes=false
mode. Due to an issue with the manner in which the objects are initialized, an attacker can expose or modify a limited amount of property information on those objects. There is no impact to availability.
PoC
// PoC.js
async function main(){
var tough = require("tough-cookie");
var cookiejar = new tough.CookieJar(undefined,{rejectPublicSuffixes:false});
// Exploit cookie
await cookiejar.setCookie(
"Slonser=polluted; Domain=__proto__; Path=/notauth",
"https://__proto__/admin"
);
// normal cookie
var cookie = await cookiejar.setCookie(
"Auth=Lol; Domain=google.com; Path=/notauth",
"https://google.com/"
);
//Exploit cookie
var a = {};
console.log(a["/notauth"]["Slonser"])
}
main();
Details
Prototype Pollution is a vulnerability affecting JavaScript. Prototype Pollution refers to the ability to inject properties into existing JavaScript language construct prototypes, such as objects. JavaScript allows all Object attributes to be altered, including their magical attributes such as __proto__
, constructor
and prototype
. An attacker manipulates these attributes to overwrite, or pollute, a JavaScript application object prototype of the base object by injecting other values. Properties on the Object.prototype
are then inherited by all the JavaScript objects through the prototype chain. When that happens, this leads to either denial of service by triggering JavaScript exceptions, or it tampers with the application source code to force the code path that the attacker injects, thereby leading to remote code execution.
There are two main ways in which the pollution of prototypes occurs:
Unsafe
Object
recursive mergeProperty definition by path
Unsafe Object recursive merge
The logic of a vulnerable recursive merge function follows the following high-level model:
merge (target, source)
foreach property of source
if property exists and is an object on both the target and the source
merge(target[property], source[property])
else
target[property] = source[property]
When the source object contains a property named __proto__
defined with Object.defineProperty()
, the condition that checks if the property exists and is an object on both the target and the source passes and the merge recurses with the target, being the prototype of Object
and the source of Object
as defined by the attacker. Properties are then copied on the Object
prototype.
Clone operations are a special sub-class of unsafe recursive merges, which occur when a recursive merge is conducted on an empty object: merge({},source)
.
lodash
and Hoek
are examples of libraries susceptible to recursive merge attacks.
Property definition by path
There are a few JavaScript libraries that use an API to define property values on an object based on a given path. The function that is generally affected contains this signature: theFunction(object, path, value)
If the attacker can control the value of “path”, they can set this value to __proto__.myValue
. myValue
is then assigned to the prototype of the class of the object.
Types of attacks
There are a few methods by which Prototype Pollution can be manipulated:
Type | Origin | Short description |
---|---|---|
Denial of service (DoS) | Client | This is the most likely attack. DoS occurs when Object holds generic functions that are implicitly called for various operations (for example, toString and valueOf ). The attacker pollutes Object.prototype.someattr and alters its state to an unexpected value such as Int or Object . In this case, the code fails and is likely to cause a denial of service. For example: if an attacker pollutes Object.prototype.toString by defining it as an integer, if the codebase at any point was reliant on someobject.toString() it would fail. |
Remote Code Execution | Client | Remote code execution is generally only possible in cases where the codebase evaluates a specific attribute of an object, and then executes that evaluation. For example: eval(someobject.someattr) . In this case, if the attacker pollutes Object.prototype.someattr they are likely to be able to leverage this in order to execute code. |
Property Injection | Client | The attacker pollutes properties that the codebase relies on for their informative value, including security properties such as cookies or tokens. For example: if a codebase checks privileges for someuser.isAdmin , then when the attacker pollutes Object.prototype.isAdmin and sets it to equal true , they can then achieve admin privileges. |
Affected environments
The following environments are susceptible to a Prototype Pollution attack:
Application server
Web server
Web browser
How to prevent
Freeze the prototype— use
Object.freeze (Object.prototype)
.Require schema validation of JSON input.
Avoid using unsafe recursive merge functions.
Consider using objects without prototypes (for example,
Object.create(null)
), breaking the prototype chain and preventing pollution.As a best practice use
Map
instead ofObject
.
For more information on this vulnerability type:
Arteau, Oliver. “JavaScript prototype pollution attack in NodeJS application.” GitHub, 26 May 2018
Remediation
Upgrade tough-cookie
to version 4.1.3 or higher.
References
medium severity
- Vulnerable module: decompress-tar
- Introduced through: download@8.0.0
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › download@8.0.0 › decompress@4.2.1 › decompress-tar@4.1.1
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › download@8.0.0 › decompress@4.2.1 › decompress-tarbz2@4.1.1 › decompress-tar@4.1.1
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › download@8.0.0 › decompress@4.2.1 › decompress-targz@4.1.1 › decompress-tar@4.1.1
Overview
decompress-tar is a tar plugin for decompress.
Affected versions of this package are vulnerable to Arbitrary File Write via Archive Extraction (Zip Slip). It is possible to bypass the security measures provided by decompress and conduct ZIP path traversal through symlinks.
PoC
const decompress = require('decompress');
decompress('slip.tar.gz', 'dist').then(files => {
console.log('done!');
});
Details
It is exploited using a specially crafted zip archive, that holds path traversal filenames. When exploited, a filename in a malicious archive is concatenated to the target extraction directory, which results in the final path ending up outside of the target folder. For instance, a zip may hold a file with a "../../file.exe" location and thus break out of the target folder. If an executable or a configuration file is overwritten with a file containing malicious code, the problem can turn into an arbitrary code execution issue quite easily.
The following is an example of a zip archive with one benign file and one malicious file. Extracting the malicous file will result in traversing out of the target folder, ending up in /root/.ssh/
overwriting the authorized_keys
file:
+2018-04-15 22:04:29 ..... 19 19 good.txt
+2018-04-15 22:04:42 ..... 20 20 ../../../../../../root/.ssh/authorized_keys
Remediation
There is no fixed version for decompress-tar
.
References
medium severity
- Vulnerable module: axios
- Introduced through: axios@0.19.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › axios@0.19.2Remediation: Upgrade to axios@0.21.1.
Overview
axios is a promise-based HTTP client for the browser and Node.js.
Affected versions of this package are vulnerable to Server-Side Request Forgery (SSRF). An attacker is able to bypass a proxy by providing a URL that responds with a redirect to a restricted host or IP address.
Remediation
Upgrade axios
to version 0.21.1 or higher.
References
medium severity
- Vulnerable module: marked
- Introduced through: marked@0.8.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › marked@0.8.2Remediation: Upgrade to marked@1.1.1.
Overview
marked is a low-level compiler for parsing markdown without caching or blocking for long periods of time.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). The em
regex within src/rules.js
file have multiple unused capture groups which could lead to a denial of service attack if user input is reachable.
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:
- CCC
- CC+C
- C+CC
- 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.
Remediation
Upgrade marked
to version 1.1.1 or higher.
References
medium severity
- Vulnerable module: got
- Introduced through: download@8.0.0 and got@10.7.0
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › download@8.0.0 › got@8.3.2
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › got@10.7.0Remediation: Upgrade to got@11.8.5.
Overview
Affected versions of this package are vulnerable to Open Redirect due to missing verification of requested URLs. It allowed a victim to be redirected to a UNIX socket.
Remediation
Upgrade got
to version 11.8.5, 12.1.0 or higher.
References
medium severity
- Vulnerable module: axios
- Introduced through: axios@0.19.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › axios@0.19.2Remediation: Upgrade to axios@1.6.3.
Overview
axios is a promise-based HTTP client for the browser and Node.js.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS). An attacker can deplete system resources by providing a manipulated string as input to the format method, causing the regular expression to exhibit a time complexity of O(n^2)
. This makes the server to become unable to provide normal service due to the excessive cost and time wasted in processing vulnerable regular expressions.
PoC
const axios = require('axios');
console.time('t1');
axios.defaults.baseURL = '/'.repeat(10000) + 'a/';
axios.get('/a').then(()=>{}).catch(()=>{});
console.timeEnd('t1');
console.time('t2');
axios.defaults.baseURL = '/'.repeat(100000) + 'a/';
axios.get('/a').then(()=>{}).catch(()=>{});
console.timeEnd('t2');
/* stdout
t1: 60.826ms
t2: 5.826s
*/
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:
- CCC
- CC+C
- C+CC
- 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.
Remediation
Upgrade axios
to version 1.6.3 or higher.
References
medium severity
- Vulnerable module: follow-redirects
- Introduced through: axios@0.19.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › axios@0.19.2 › follow-redirects@1.5.10Remediation: Upgrade to axios@0.20.0.
Overview
Affected versions of this package are vulnerable to Information Exposure by leaking the cookie header to a third party site in the process of fetching a remote URL with the cookie in the request body. If the response contains a location
header, it will follow the redirect to another URL of a potentially malicious actor, to which the cookie would be exposed.
Remediation
Upgrade follow-redirects
to version 1.14.7 or higher.
References
medium severity
- Vulnerable module: http-cache-semantics
- Introduced through: download@8.0.0
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › download@8.0.0 › got@8.3.2 › cacheable-request@2.1.4 › http-cache-semantics@3.8.1
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:
- CCC
- CC+C
- C+CC
- 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.
Remediation
Upgrade http-cache-semantics
to version 4.1.1 or higher.
References
medium severity
- Vulnerable module: lodash
- Introduced through: airtable@0.8.1
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › airtable@0.8.1 › lodash@4.17.15Remediation: Upgrade to airtable@0.10.1.
Overview
lodash is a modern JavaScript utility library delivering modularity, performance, & extras.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) via the toNumber
, trim
and trimEnd
functions.
POC
var lo = require('lodash');
function build_blank (n) {
var ret = "1"
for (var i = 0; i < n; i++) {
ret += " "
}
return ret + "1";
}
var s = build_blank(50000)
var time0 = Date.now();
lo.trim(s)
var time_cost0 = Date.now() - time0;
console.log("time_cost0: " + time_cost0)
var time1 = Date.now();
lo.toNumber(s)
var time_cost1 = Date.now() - time1;
console.log("time_cost1: " + time_cost1)
var time2 = Date.now();
lo.trimEnd(s)
var time_cost2 = Date.now() - time2;
console.log("time_cost2: " + time_cost2)
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:
- CCC
- CC+C
- C+CC
- 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.
Remediation
Upgrade lodash
to version 4.17.21 or higher.
References
medium severity
- Vulnerable module: marked
- Introduced through: marked@0.8.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › marked@0.8.2Remediation: Upgrade to marked@4.0.10.
Overview
marked is a low-level compiler for parsing markdown without caching or blocking for long periods of time.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) when passing unsanitized user input to inline.reflinkSearch
, if it is not being parsed by a time-limited worker thread.
PoC
import * as marked from 'marked';
console.log(marked.parse(`[x]: x
\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](\\[\\](`));
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:
- CCC
- CC+C
- C+CC
- 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.
Remediation
Upgrade marked
to version 4.0.10 or higher.
References
medium severity
- Vulnerable module: marked
- Introduced through: marked@0.8.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › marked@0.8.2Remediation: Upgrade to marked@4.0.10.
Overview
marked is a low-level compiler for parsing markdown without caching or blocking for long periods of time.
Affected versions of this package are vulnerable to Regular Expression Denial of Service (ReDoS) when unsanitized user input is passed to block.def
.
PoC
import * as marked from "marked";
marked.parse(`[x]:${' '.repeat(1500)}x ${' '.repeat(1500)} x`);
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:
- CCC
- CC+C
- C+CC
- 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.
Remediation
Upgrade marked
to version 4.0.10 or higher.
References
low severity
- Vulnerable module: follow-redirects
- Introduced through: axios@0.19.2
Detailed paths
-
Introduced through: open-data@Karuna2020/open-data#41f181e3181989a6d14936fe63e2a87216e146fe › axios@0.19.2 › follow-redirects@1.5.10Remediation: Upgrade to axios@0.20.0.
Overview
Affected versions of this package are vulnerable to Information Exposure due a leakage of the Authorization header from the same hostname during HTTPS to HTTP redirection. An attacker who can listen in on the wire (or perform a MITM attack) will be able to receive the Authorization header due to the usage of the insecure HTTP protocol which does not verify the hostname the request is sending to.
Remediation
Upgrade follow-redirects
to version 1.14.8 or higher.