Dockle vs Falco

Dockle is a free, self-hosted container image linter focused on security and Docker best practices. It scans images for vulnerabilities and checks settings against CIS Docker Image Benchmarks, along with Docker- and Linux-specific issues such as empty passwords and duplicate user or group IDs. Results can be displayed or saved in JSON and SARIF formats. Users can set an exit level and ignore selected checkpoints through command options, environment variables, or a .dockleignore file. The project documents continuous-integration use and provides a GitHub Action, with examples for Travis CI, CircleCI, and GitLab CI. Dockle can access private images in Docker Hub, Amazon ECR, Google Container Registry, and self-hosted registries. It can also scan an image archive created with docker save by using the --input option. Installation routes include Homebrew, Linux distribution packages, Windows binaries, version managers, source builds, and Docker. Dockle runs on Linux, macOS, and Windows and is listed under the Apache-2.0 license.

Falco is a free, open source runtime security project for Linux and self-hosted environments. It monitors hosts, containers, Kubernetes, and cloud environments for abnormal behavior, potential threats, and compliance violations in real time. Events are compared with built-in detection rules, and users can create rules for their own environments. Sources include Linux kernel system calls, Kubernetes audit logs, cloud events such as AWS CloudTrail, and events from GitHub and Okta. For kernel event monitoring, Falco uses an eBPF driver by default and offers a kernel module for wider compatibility. Installation options cover Linux hosts, Docker containers, and Kubernetes, including official Helm charts; supported architectures include x86_64 and ARM64. Falcosidekick can forward events to more than 50 systems, including email, chat, message queues, serverless functions, and databases. Falco is hosted by the CNCF as a graduated project. Detection depends on having appropriate rules in place, while resource use varies with server load and workload. The project recommends testing performance in the target environment.