ffuf VS prometheus

It seems to me like "fuzzing" has a different meaning in web application penetration testing. Here, "fuzzer" is a term for tools that just generate different request using wordlists, without adding any mutations. For example, the two popular web fuzzers ffuf [1] and wfuzz [2] also call themselves fuzzers.

I see how reusing a term for a different concept is bothersome, but I feel like "fuzzer" is the term that people learning about bug bounty hunting are familiar with.

[1] https://github.com/ffuf/ffuf

[2] https://wfuzz.readthedocs.io/en/latest/

FFUF

I've been using ffuf happily for the past couple of years however I remember seeing a post on reddit/twitter about a new "intelligent" discovery tool.

Learn more about ffuf here: https://github.com/ffuf/ffuf

Also, I see you mentioned using curl. You can checkout ffuf which is closely related but more geared towards what you're doing.

git clone https://github.com/ffuf/ffuf ; cd ffuf ; go get ; go build

So ffuf (https://github.com/ffuf/ffuf) or wfuzz (https://github.com/xmendez/wfuzz) are a better choice to enumerate GET/POST parameters/values.

WriteRequest::timeseries is a vector (https://github.com/prometheus/prometheus/blob/main/prompb/re...) and

Developers widely use Prometheus as a system for operational monitoring and alerting for their projects. Here is a list of tools for monitoring frontend services with Prometheus.

Just to give an example of the power of Go for CLI builds, you may have already used or at least heard of Docker, Kubernetes, Prometheus, Terraform, but what do they all have in common? They all have a large part of their usability via CLI and are developed in Go 🐿.

Distributed system administrators need mechanisms and tools for monitoring individual nodes in order to analyze the system and promptly detect anomalies. Developers also need effective mechanisms for analyzing, diagnosing issues, and identifying bugs in protocol implementations. Logging, tracing, and collecting metrics are common observability techniques to allow monitoring and obtaining diagnostic information from the system; most of the explored code bases use these techniques. OpenTelemetry and Prometheus are popular open-source monitoring solutions, which are used in many of the explored code bases.

Setting up monitoring for a system, especially one involving GRPC communication, provides crucial visibility into its operations. In this guide, we walked through the steps to instrument both a GRPC server and client with Prometheus metrics, exposed those metrics via an HTTP endpoint, and visualized them using Grafana. The Docker-Compose setup simplified the deployment of both Prometheus and Grafana, ensuring a streamlined process.

Alerting and Notification: Select a tool with flexible alerting mechanisms to proactively detect anomalies or deviations from defined thresholds. Consider asking questions like "Does this tool offer customizable alerting options and support notification channels that suit our team's communication preferences?" A tool like Prometheus provides robust alerting capabilities.

Prometheus

Prometheus is an open-source monitoring and alerting toolkit. It is designed to monitor highly dynamic containerized systems, making it an excellent choice for monitoring Docker containers and Kubernetes clusters.

wget https://github.com/prometheus/prometheus/releases/download/v2.46.0/prometheus-2.46.0.linux-amd64.tar.gz

Prometheus: Open-source monitoring system. Often used together with Grafana.