Victor Zidon

To fully understand the root cause of the NRDelegationAttack and to analyze its amplification factor, we developed mini- lab setup, disconnected from the Internet, that contains all
the components of the DNS system, a client, a resolver, and authoritative name servers. This setup is built to analyze and examine the behavior of a resolver (or any other component) under the microscope. On the other hand it is not useful for performance analysis (stress analysis).
Here we provide the code and details of this setup enabling to reproduce our analysis. Moreover, researchers may find it useful for farther behavioral analysis and examination of different components in the DNS system.

Today’s software development landscape has witnessed a shift towards microservices architectures. Using this approach, large software systems are composed of multiple separate microservices, each responsible for specific tasks. The breakdown to microservices is also reflected in the infrastructure, where individual microservices can be executed with different hardware configurations and scaling properties. As systems grow larger, incoming traffic can trigger multiple calls between different microservices to handle each request.

Auto-scaling is a technique widely used to adapt systems to fluctuating traffic loads by automatically increasing (scale-up) and decreasing (scale-down) the number of resources used.

Our work shows that when microservices with separate auto-scaling mechanisms work in tandem to process ingress traffic, they can overload each other. This overload results in throttling (DoS)
or the over-provisioning of resources (EDoS).

In the lecture we will demonstrate how an attacker can exploit the tandem behavior of microservices with different auto-scaling mechanisms to create an attack we denote as the Tandem Attack. We demonstrate the attack on a typical and recommended serverless architecture, using AWS Lambda for code execution and DynamoDB as database. Part of the results will be presented as an IEEE INFOCOM’23 poster.

Auto-scaling is a fundamental capability of cloud computing which allows consuming resources dynamically according to changing traffic needed to be served.
By the micro-services architecture paradigm, software systems are built as a set of loosely-coupled applications and services that can be individually scaled.
In this paper, we present a new attack the \emph{Tandem Attack} that exploits the Tandem behavior of micro-services with different scaling properties. Such issues can result in Denial of Service (DoS) and Economic Denial of Sustainability (EDoS) created by malicious attackers or self-inflicted due to wrong configurations set up by administrators. We demonstrate the Tandem attack using a popular AWS serverless infrastructure modeling two services and show that removing servers’ management responsibility from the cloud users does not mitigate the different scaling properties challenge and can even make the problem harder to solve.

It is a common belief that Auto-scaling mechanisms serve as a mitigation for Distributed Denial of Service (DDoS) attacks on cloud computing infrastructures by dynamically adding machines to cope with the additional load. Intuitively, such attacks are mostly associated with Economic Denial of Sustainability (EDoS) derived from paying for the extra resources required to process the malicious incoming traffic.

Contrary to this belief, we present and analyze the Yo-Yo attack, a new attack against the auto-scaling mechanism that can cause significant performance degradation in addition to economic damage. We demonstrate the attack on Amazon EC2, Kubernetes, and serverless architecture. We then present and analyze Tandem Attack, a new attack on Microservices architecture. In this attack, the attacker exploits the tandem behavior of services with different auto-scaling mechanisms, causing both economic and performance damage.

This paper presents a new localhost browser based vulnerability and corresponding attack that opens the door to new attacks on private networks and local devices. We show that this new vulnerability may put hundreds of millions of internet users and their IoT devices at risk. Following the attack presentation, we suggest three new protection mechanisms to mitigate this vulnerability.
This new attack bypasses recently suggested protection mechanisms designed to stop browser-based attacks on private devices and local applications.