thehardwarestore.ai

Trivy is a comprehensive, production-grade security scanner for container images, filesystems, Git repositories, Kubernetes clusters, cloud infrastructure (IaC), and virtual machines. It detects vulnerabilities in OS packages and language-specific dependencies, scans for IaC misconfigurations (Terraform, CloudFormation, ARM, Ansible, Helm, Dockerfile), secrets, and license compliance. The architecture is modular: a CLI entry point (`cmd/trivy/main.go`) orchestrates scanning via a file analysis engine (`pkg/fanal`), dependency parsers (`pkg/dependency/parser`), vulnerability detectors (`pkg/detector`), IaC scanners with Rego-based policies (`pkg/iac`), and multiple output formats (JSON, SARIF, CycloneDX, SPDX, table, etc.). It supports client-server mode via gRPC, a plugin system, WASM modules, and VEX/SBOM attestation integration. The codebase is well-structured with clear separation of concerns, extensive test coverage, and many reusable utility packages (parallel processing, generic sets, caching, version constraints, license expression parsing). It is actively maintained with frequent releases and a large community.

Tracee is a runtime security and observability tool that uses eBPF to trace system events and detect threats. It consists of multiple Go binaries: a main security agent (`tracee`) that loads eBPF programs, collects kernel events, and runs detection rules; a gRPC client (`traceectl`) for remote management; an event generator (`evt`) for stress testing; and a Kubernetes operator. The architecture is modular with a clear separation between eBPF event collection, event derivation and enrichment pipelines, data stores (process tree, container info, DNS cache, kernel symbols), a detection engine supporting both Go signatures and YAML-based rules using CEL expressions, and filtering/policy subsystems. The system operates as a daemon with HTTP/gRPC endpoints for metrics, health, and streaming event output, and integrates deeply with Kubernetes via CRDs and controllers. The codebase demonstrates strong engineering with clean abstractions (filter interfaces, data store registries, event derivation framework), reusable utility packages (bucketcache, changelog, string interning, fuzzy parsing), and comprehensive testing including unit, integration, and e2e tests. However, it exhibits technical drift in areas like multiple Go modules with `replace` directives, hardcoded kernel version checks in eBPF probe logic, legacy signature conversion code planned for removal, and some global mutable state. The eBPF C code is complex and kernel-version-sensitive, requiring ongoing maintenance. Overall, it is a well-crafted, production-grade security tool suitable for container and Kubernetes environments.

KubeHound is a security analysis tool for Kubernetes clusters that automatically models and computes attack paths. It collects Kubernetes resources (pods, roles, bindings, endpoints, nodes) via a live API or offline dump, transforms them into a property graph stored in JanusGraph (with MongoDB as an intermediate store), and computes privilege escalation and lateral movement paths mapped to MITRE ATT&CK techniques. The system exposes a CLI for local analysis and a gRPC API (KHaaS) for service-oriented deployments, with optional Jupyter notebooks for querying the graph using a custom Gremlin DSL. The architecture is modular: a collector abstraction handles rate-limited streaming from the Kubernetes API; an ingestion pipeline converts raw objects into normalized store models and graph vertices/edges; an edge registry defines over 20 attack types (e.g., PodExec, TokenSteal, ContainerEscape); and a graph builder executes Gremlin traversals to connect vertices into paths. Storage layers use asynchronous writers with micro-batching for high throughput, and an in-memory cache deduplicates identities and roles. Telemetry (Datadog APM, metrics, logs) is integrated throughout. While primarily an application, the repository includes experimental research components (exp/khaudit) for audit reports and MITRE Attack Flow conversion, plus Jupyter notebooks for interactive graph queries, giving it a hybrid character. The codebase demonstrates strong engineering practices: provider interfaces, test mocking, worker pools, retry logic, and comprehensive system tests against real Kind clusters. However, some areas (large cluster optimizations, experimental code, deprecated flags) indicate ongoing evolution and minor technical debt.

CDK (Container Docker/Kubernetes Exploitation Toolkit) is a Go-based CLI penetration testing tool designed for container and Kubernetes environments. It evaluates security posture by running extensive checks against container runtime configurations, K8s API permissions, cloud metadata, and kernel exploit possibilities. It then executes a plethora of exploits covering container escape (cgroups, mount, eBPF, runc/containerd CVEs), K8s credential access (secrets, configmaps, tokens), persistence (backdoor daemonsets, cronjobs, shadow API server), privilege escalation (service account token theft, CVE-2026-31431), and remote control (reverse shells, kubelet exec). The binary bundles stripped-down versions of common tools (kubectl, netcat, vi, etc.) for post-exploitation use. The architecture follows a plugin pattern where exploits register themselves via init(), and evaluation uses a category-based check system. The codebase shows signs of organic growth with some legacy cruft (deprecated auto-escape task, commented-out code, future-dated CVEs) and moderate technical debt (global mutable state, panics, outdated Go idioms), but remains functional and actively maintained for penetration testers and security researchers.

This repository is a curated 'awesome list' of Kubernetes (K8s) security resources. It contains a single README.md file that organizes links to articles, talks, books, certifications, CVEs, tools, and community resources related to Kubernetes security. There is no executable code, no API, no data pipeline, and no software application to run. The repository functions as a reference index for security professionals and developers seeking learning materials and tools. Despite the high star count, the repository's value lies entirely in its curation and organization of external resources, not in any authored code or data.