GraphQL Common Attack Vectors
Written by Yuval Donana on
GraphQL Common Attack Vectors
Written by Yuval Donana on
GraphQL has quickly become popular for building APIs due to its flexibility and ease of use. However, like any technology, it is not immune to security vulnerabilities. The main focus of this article is to explore the common attack vectors in GraphQL and explain actionable tips to remain protected against these attacks. Understanding GraphQL common attack vectors and implementing their corresponding mitigations is the first step in improving security level and preventing attackers from performing malicious actions.
What is GraphQL?
Before we delve into the GraphQL common attack vectors, it is pivotal to revise what GraphQL is.
GraphQL is an open-source query language and runtime developed by Facebook in 2012. It offers a more efficient, powerful, and flexible way to communicate with APIs than traditional RESTful API architectures. Clients using GraphQL can easily request the specific data they need, making it possible to optimise network usage and improve performance.
Moreover, it also uses schema-based APIs, providing more clarity for clients and servers. In GraphQL, the server defines a schema that outlines the types of data available. Clients can request the specific data they need by sending queries that match the schema. Hence, increasing its popularity by a great degree.
GraphQL vs. REST API
If you are looking to understand GraphQL common attack vectors but are not sure about the difference between GraphQL and REST API first, this article will enable you to understand exactly that. These two are the different architectural styles that users build APIs with. But they are not exactly similar: the difference arises in how they deal with data fetching.
In REST APIs, clients typically make requests to specific endpoints, and the server returns predefined data for that endpoint. This can lead to over-fetching or under-fetching data, resulting in slower performance and increased network usage.
In contrast, GraphQL allows clients to specify the exact data they need using a single endpoint and a query parameter, allowing the technology to behave similarly to a query language. This means the server only sends the requested data, resulting in faster performance and less network usage.
Graph QL Common Attack Vectors
Misconfigurations in GraphQL can allow attackers to exploit several typical attack vectors against the application. Many common attack vectors associated with GraphQL include injection attacks, DoS, sensitive information disclosure, and authorization issues. Following is a comprehensive explanation of those attack vectors and their corresponding mitigations:
Since GraphQL connects to backend data sources mainly using HTTP requests, injection attacks can pose significant security issues to the entire system. Such security issues include SQL and noSQL injections, command injection, SSRF, and CRLF injection.
GraphQL by itself doesn’t prevent any kind of attacks. Hence, if developers make mistakes during the development process, such as not using parameterized queries, the application might become vulnerable to SQL injection attacks. In the following scenario, we have found a possible injection point in the filter argument of the pastes field.”
By sending the following query and adding an apostrophe (‘) to the filter argument value, it is possible to cause the backend database to return an error message. This allows an adversary to exploit the system further and perform an SQL Injection attack to fetch sensitive data from the system’s database.
One effective mitigation against injection attacks in GraphQL is implementing robust input validation and sanitization mechanisms. This involves carefully validating and sanitizing user input before passing it to the underlying data storage layer.
Denial Of Service (DOS)
Denial of Service (DoS) attack aims to disrupt or disable a system by overwhelming it with a high volume of requests, making it unavailable to legitimate users. As GraphQL APIs are not inherently immune to such attacks, bad actors can exploit its flexibility. This can be done by crafting complex or inefficient queries that place excessive load on the server, thereby consuming valuable system resources and impacting overall performance.
One such method is a Batch query attack, allowing adversaries to send a single HTTP request containing multiple queries at once. Following is a possible exploitation process for this attack vector:
1. First, we must check if batch queries are enabled on the system. This can be done by sending a request containing multiple queries. If the system responds with a valid result for each of the requested queries, then batching is enabled on the system:
2. After confirming that batching feature is enabled, finding a query that utilizes a large amount of the system resource is required. In the following example, a query for updating the system was found to use a large portion of the system’s resources and takes a long time to process (about one minute). By using the batching method and the following query in the code snippet, it is possible to overload the server. This makes it unavailable to the users:
To mitigate this attack vector, it is essential to implement suitable safeguards. Some of these include query depth and complexity limits, rate limiting, and request validation. These measures help ensure the server can efficiently handle incoming requests while protecting against resource exhaustion and maintaining service availability for legitimate users.
Improper Authorization Checks
Authorization bypass in GraphQL refers to an attack vector where an attacker can circumvent the established authorization mechanisms. This allows the attacker unauthorized access to protected resources or perform actions beyond their intended privileges. This bypass can occur for various reasons, such as improper validation of user permissions, inadequate authentication checks, or flaws in the schema design.
In the following example, the application prevents unauthorized users from performing certain queries by default, specifically the systemHealth query.
It is possible to bypass the imposed restrictions and cause the query to return valid data by specifying a valid operation name in the request. Valid operation names can be found by inspecting the HTTP traffic of the application and observing the request being sent to the GraphQL endpoint. The operation name must be configured on the backend server to exploit this attack vector successfully.
To mitigate this attack vector, authorization checks should be performed on the server side, validating the requesting user’s role and permissions before processing the request. This can be done by adopting robust authentication mechanisms like JWT or OAuth, ensuring that only verified users gain access. Implementing a role-based access control (RBAC) system further defines and restricts user privileges within the application.
When it comes to GraphQL, information disclosure refers to revealing data that adversaries could use to exploit further vulnerabilities and access additional information. This could include details about the GraphQL server’s version and type, schema, fields, stack trace errors, and other relevant information.
For example, many GraphQL servers allow users to perform introspection queries. Introspection is a powerful feature that allows users to retrieve the complete GraphQL schema. It can also reveal useful information such as fields, types, mutations, and more. In the following scenario sending the below query in the code snippet will aid in retrieving the complete server’s schema:
By passing the schema received in the response to a visual parser tool called GraphQL Voyager, it is possible to obtain a clear representation of the schema. An adversary could leverage this behavior in order to locate sensitive fields, mutation, and more.
In order to address this attack vector, it is recommended to disable the introspection feature if enabled in the GraphQL implementation being used. You can also opt for an implementation that allows for disabling this feature.
In conclusion, GraphQL, while offering flexibility and efficiency, is not immune to security vulnerabilities. This article explored common attack vectors such as injection, DoS, sensitive information disclosure, and authorization issues. To protect GraphQL applications, robust input validation, query limits, and strong authorization mechanisms are essential. By implementing these measures, developers can harness the power of GraphQL while ensuring the security of their web applications.
Organizations should prioritize cyber security risk assessments and penetration tests to mitigate risks in GrpaphQL deployments. Such deployments have become increasingly popular among companies developing SaaS products. Clear Gate is a trusted cybersecurity provider fit for this purpose. It offers in-depth manual penetration tests to help organizations strengthen their GraphQL security and protect valuable data from potential threats.