Infrastructure as Code Security Scanners

Trivy is a comprehensive security scanner designed to identify vulnerabilities and misconfigurations across container images, filesystems, and infrastructure as code files. It functions as a software composition analysis tool and an infrastructure security scanner, providing automated checks for CI/CD pipelines and cloud environments to ensure the integrity of the software supply chain. The tool distinguishes itself through a modular, plugin-based architecture that allows for the independent inspection of diverse targets. It utilizes a declarative policy engine to evaluate configurations against compliance standards and relies on a remote, periodically updated vulnerability database to maintain current detection logic without requiring binary updates. By employing static analysis pattern matching, it maps disparate scan results into a unified output schema for consistent reporting. Beyond its core scanning capabilities, the project supports cloud infrastructure auditing and deep inspection of local and remote environments. It is distributed as a single cross-platform executable, and comprehensive configuration and usage details are available in the project's official user guide.

Trufflehog is a security tool designed to continuously monitor code repositories and cloud environments to detect, verify, and remediate exposed sensitive credentials and API keys. It functions as a comprehensive secret scanning engine that integrates directly into deployment pipelines and version control systems to intercept sensitive data before it is committed or pushed. By utilizing read-only operations and volatile memory processing, the system ensures that discovered credentials are never stored persistently, maintaining strict data privacy throughout the scanning lifecycle. The platform distinguishes itself through a privacy-focused architecture that relies on cryptographic fingerprinting to track and deduplicate findings without ever transmitting or storing raw sensitive values. It supports distributed scanning via independent agents that connect to a central dashboard, allowing for localized analysis while maintaining network isolation. Furthermore, the system provides automated incident response capabilities, including secret rotation and revocation, which help organizations minimize the window of vulnerability for compromised credentials. Beyond core detection, the project offers a broad capability surface for enterprise-wide access governance and security compliance. It includes modular detection logic for custom rule definitions, integration with external identity providers for role-based access control, and extensive monitoring across cloud storage, container infrastructure, and collaboration platforms. The system also provides detailed metadata tracing to link findings to specific users, pipelines, or commits, facilitating efficient remediation and auditability across large-scale development environments.

The OWASP Cheat Sheet Series is a comprehensive, community-driven repository of concise security best practices and defensive coding patterns. It serves as a centralized knowledge base for developers and security professionals, providing actionable guidance to secure applications across the entire software development lifecycle. The project covers a vast array of security domains, ranging from fundamental web application hardening and authentication protocols to specialized controls for modern infrastructure and artificial intelligence systems. What distinguishes this project is its decentralized, collaborative editorial process. By utilizing a version-controlled, markdown-based workflow, the series ensures that security guidance remains vendor-neutral, peer-reviewed, and universally accessible. This structure allows the community to rapidly evolve and maintain technical documentation, ensuring that defensive strategies keep pace with emerging threats and shifting technology stacks. The project provides extensive coverage of critical security areas, including robust input validation, access control enforcement, and supply chain risk management. It offers detailed implementation guides for securing cloud-native architectures, containerized environments, and various language-specific frameworks. Furthermore, the series addresses advanced topics such as artificial intelligence agent safety, prompt injection prevention, and zero-trust architectural principles. The documentation is maintained as an open-source repository, with content transformed into a navigable web format through automated static site generation.

Prowler is an automated cloud infrastructure security scanner and posture management tool. It evaluates cloud environments and infrastructure-as-code templates against security benchmarks to identify misconfigurations, vulnerabilities, and compliance gaps that could compromise system integrity. The platform distinguishes itself through graph-based attack path analysis, which identifies chains of misconfigurations that create exploitable routes for unauthorized access. It utilizes a plugin-based execution model to perform state-based assessments of live environments and static analysis of configuration files, ensuring security coverage across the entire development lifecycle. The tool provides comprehensive capabilities for continuous security integration, allowing teams to automate compliance reporting by mapping findings to regulatory frameworks. It supports risk prioritization and provides actionable remediation guidance, while enabling the integration of security data into external incident management and monitoring systems through automated reporting pipelines.

Gitleaks is a security scanning engine designed to identify hardcoded credentials, API keys, and other sensitive information within version control systems and local file structures. It functions as a static analysis tool that automates the detection of secrets, helping to prevent the accidental exposure of sensitive data during the development lifecycle. The tool distinguishes itself through its ability to perform deep forensic analysis of git history, allowing users to audit entire project timelines or enforce security gates within continuous integration pipelines. It supports complex detection logic through composite rules and provides mechanisms for baseline management, which enables teams to ignore existing findings and focus exclusively on new security risks. By offering pre-commit hook integration and exit-code-based orchestration, it allows for the enforcement of security policies directly within developer workflows and automated build environments. Beyond core scanning, the project provides a broad set of utilities for managing security findings, including support for decoding obfuscated strings, inspecting compressed archives, and filtering results through allowlisting or path exclusions. It facilitates compliance and reporting by exporting structured data, which can be integrated into external dashboards or tracking systems. The tool is built to handle various input sources, including direct file system traversal and standard input streams, ensuring compatibility with diverse development and deployment environments.

Gixy is a static configuration analyzer and security auditor for Nginx. It functions as an infrastructure-as-code security scanner and web server configuration linter designed to identify vulnerabilities and misconfigurations in server definitions before deployment. The tool focuses on detecting high-risk security flaws, including host header spoofing, server-side request forgery, and path traversal. It specifically audits Nginx configurations for risks such as HTTP splitting, multiline header issues, and unauthorized third-party access resulting from incorrect Referer or Origin header patterns. The analysis surface covers configuration dependency auditing through the resolution of include directives and the detection of header redefinition errors caused by block inheritance. Findings are assigned severity levels and can be exported as JSON or text reports for integration with external security tooling.

Conftest is a suite of tools designed for validating structured configurations, testing policy logic, and generating policy documentation. It serves as a configuration file validator that checks YAML, JSON, and Helm charts for security violations and compliance issues using declarative rules. The project functions as an Open Policy Agent testing tool, allowing structured configuration files to be validated against custom policies written in Rego. It includes a policy-as-code testing framework to ensure policy logic is correct and a utility to extract metadata from Rego code to create static markdown reference files. The tool provides capabilities for infrastructure-as-code testing, configuration compliance auditing, and integration into CI/CD pipelines to block non-compliant changes. It supports executing policy validations within containerized environments to maintain consistency across different host operating systems.

This project is a static analysis tool and linter designed to improve the quality, reliability, and portability of shell scripts. By performing deep structural analysis, it identifies common programming pitfalls, syntax errors, and security vulnerabilities before scripts are executed. It functions as an automated code reviewer that enforces best practices and helps developers maintain consistent, robust code across different operating environments. The tool distinguishes itself through its dialect-aware grammar resolution, which adapts its parsing logic based on the specific shell interpreter detected. It utilizes a sophisticated engine that constructs an abstract syntax tree to evaluate logic, quoting, and portability concerns. Developers can exert granular control over the analysis process by using inline directives to suppress specific warnings or configure how the tool resolves external source files. The project covers a comprehensive surface of diagnostic capabilities, ranging from fundamental syntax validation to complex logic checks. It provides guidance on idiomatic script construction, including safe file handling, efficient arithmetic operations, and proper command substitution. These features collectively ensure that scripts adhere to POSIX standards and remain compatible across various shell implementations. The tool is distributed as a command-line utility, allowing for integration into development workflows to provide immediate feedback on script integrity.

tfsec is a static analysis tool and security scanner for infrastructure as code, specifically designed to detect misconfigurations and compliance violations in Terraform and cloud infrastructure definitions before deployment. It functions as a cloud security policy engine that identifies vulnerabilities across multiple cloud platforms. The tool provides capabilities for cloud compliance auditing and scanning of Cloud Development Kit code. It supports custom security policy enforcement and allows for the definition of organization-specific security requirements. The scanner includes features for automating analysis within DevSecOps pipelines and exporting results to security dashboards. It manages analysis noise through check filtering and the suppression of security warnings via inline comments with expiration dates.

Terraform is a declarative infrastructure-as-code tool designed to manage the lifecycle of cloud and on-premises resources. It functions as a workflow engine that reconciles a defined desired state against real-world infrastructure, using a persistent state-tracking layer to maintain consistency and visibility across distributed environments. By mapping infrastructure components into a directed acyclic graph, the system calculates the optimal order for provisioning, updating, or destroying resources. The platform is distinguished by its extensible plugin-based architecture, which decouples core orchestration logic from vendor-specific service APIs. This allows users to manage diverse infrastructure across multiple providers through a unified workflow. The system enforces predictability by separating operations into a three-stage lifecycle—planning, applying, and state-updating—and supports policy-as-code evaluation to validate changes against security and compliance rules before any modifications are executed. Beyond core orchestration, the tool provides robust support for collaborative management, including workspace isolation for environment separation and module sharing for distributing standardized infrastructure patterns. It integrates into broader development ecosystems through support for programmatic definition in various languages, external system hooks, and comprehensive tooling for configuration debugging and editor assistance.

tfsec is a static analysis tool and infrastructure as code linter designed to detect security misconfigurations and compliance violations in Terraform infrastructure code. It functions as a cloud security posture tool and policy enforcement engine that evaluates configurations against established security benchmarks. The tool provides multi-cloud security auditing for providers including AWS, Azure, Google Cloud, and Kubernetes, as well as specialized scanning for DigitalOcean, OpenStack, CloudStack, and GitHub configurations. It identifies insecure settings such as public access or unencrypted storage across compute, networking, and identity services. The engine includes capabilities for complex expression evaluation to resolve functional expressions and resource relationships, ensuring misconfigurations are detected beyond literal string values. It supports custom policy definitions for organization-specific standards and allows for security warning suppression via source code comments or command-line flags. The scanner is designed for CI/CD security integration as a standalone binary or container, with the ability to export findings in structured formats such as JSON, SARIF, and CSV.

Continue is an automated code review platform that integrates AI agents directly into the software development lifecycle. By executing custom validation rules against pull request diffs, it provides immediate feedback through repository status checks, allowing teams to enforce quality, security, and documentation standards before manual review begins. The system distinguishes itself through a file-based configuration model where validation logic is defined in version-controlled markdown files. These files act as system prompts that guide autonomous agents in evaluating code changes. This approach enables agentic task chaining, where specialized workflows—such as security scanning, test coverage validation, and UI rendering verification—are orchestrated to analyze code against project-specific criteria. Beyond automated reviews, the platform includes a local-first execution engine that allows developers to run and refine these checks from the command line before committing changes. The system also incorporates a feedback loop that tracks user acceptance and rejection of suggestions, enabling the refinement of check logic over time to reduce noise and improve the accuracy of automated findings. The project provides a command-line interface for managing these workflows and integrates with repository webhooks to trigger analysis automatically upon pull request submission.

Checkov is a static analysis tool and security scanner designed to identify misconfigurations in infrastructure as code, container images, and Kubernetes configurations. It functions as a cloud security posture tool, an SCA vulnerability scanner, and a secret scanning utility to prevent security breaches and version control leaks. The project distinguishes itself through deep graph analysis and variable resolution, allowing it to map relationships between interconnected resources and evaluate the final state of infrastructure attributes. It provides extensibility for defining custom security policies using Python or YAML and includes a policy generation utility to create new static analysis checks. The tool's capability surface covers a wide range of cloud templates, including Terraform plans, AWS SAM, CloudFormation, Azure ARM, and Bicep files. It also handles container security via Dockerfile and image auditing, and Kubernetes auditing through the analysis of manifests, Helm charts, and Kustomize files. Additionally, it performs software composition analysis to identify known CVEs in package dependencies and uses regex and entropy to detect hardcoded secrets. Automation is supported via native integrations for CI/CD pipelines, git hooks, and IDEs, with results exportable in formats such as JSON, JUnit XML, SARIF, and Markdown.

Ruff is a high-performance static analysis and code formatting tool designed for Python. Built in Rust, it functions as a comprehensive engine that scans source code to detect programming errors, security vulnerabilities, and deviations from established coding standards. By parsing source code into a structured tree representation, it provides both automated linting and style enforcement across entire projects. The tool distinguishes itself through its speed and deep integration into the development lifecycle. It utilizes parallelized file processing to maximize throughput on large codebases and offers a configuration-driven rule engine that allows developers to customize or suppress specific checks. Beyond standard Python scripts, it provides native support for Jupyter notebooks, Markdown files, and documentation strings, ensuring consistent quality across diverse document formats. Ruff serves as a versatile utility for project maintenance, offering automated import management and the ability to apply safe, automatic corrections to identified code quality issues. It integrates directly into development environments via the Language Server Protocol, providing real-time diagnostic highlighting, code actions, and rule documentation hovers. These capabilities extend to continuous integration pipelines and pre-commit hooks, enabling automated quality enforcement throughout the development process.

Shannon is an integrated security platform designed for autonomous penetration testing, static and dynamic analysis, and automated vulnerability remediation within self-hosted, private infrastructure. It functions as a unified security suite that orchestrates the entire lifecycle of vulnerability management, from initial discovery and reachability prioritization to the generation and verification of code-level patches. The platform distinguishes itself through its agentic approach to security, deploying autonomous agents to execute both black-box and white-box exploits against running applications to confirm vulnerabilities. It utilizes graph-based data flow analysis to trace execution paths from user inputs to sensitive sinks, ensuring that security findings are based on reachable threats rather than raw scan results. By operating in isolated or air-gapped environments, the system maintains strict data sovereignty and residency, ensuring that source code and sensitive analysis data remain within the local perimeter. Beyond core testing, the platform provides comprehensive security observability and supply chain auditing. It correlates static code analysis with dynamic runtime exploitation to provide a unified view of risk, while automatically deduplicating findings to reduce alert noise. The system also supports the software supply chain by generating compliant manifests and inspecting container images without requiring a local container runtime. The platform integrates directly into existing development workflows, delivering verified patches to source control and synchronizing remediation status with external project management tools. It includes robust support for compliance reporting, audit trails, and risk acceptance management to meet regulatory requirements.

Dive is a command-line tool designed for the analysis and optimization of container images. It functions as a layered storage inspector, allowing users to decompose image manifests to examine individual filesystem layers and identify opportunities to reduce total image size. The tool features a filesystem diffing engine that calculates net changes between sequential layers to highlight redundant data and storage inefficiencies. Users interact with this data through a terminal-based dashboard that provides keyboard-driven navigation of complex file structures and layer metadata. By abstracting the underlying container runtime, the tool maintains compatibility across various storage formats and engine environments. Beyond manual inspection, the software supports automated quality gates for continuous integration pipelines. It evaluates image metadata against user-defined performance thresholds to validate efficiency and prevent the deployment of suboptimal builds. Configuration files allow for the adjustment of logging levels, interface layouts, and engine preferences to suit specific development workflows.

tfsec is a static analysis tool and security scanner for Terraform configuration files. It functions as an infrastructure as code security scanner and compliance linter designed to detect misconfigurations and vulnerabilities across multiple cloud providers before resources are deployed. The tool identifies security risks by analyzing infrastructure code and variable files to evaluate the final state of the environment. It supports custom policy enforcement and allows for the suppression of specific security warnings through inline comments. Its capabilities cover cloud security posture management, infrastructure as code compliance, and integration into DevSecOps pipelines. The system also provides scan result export and security alert synchronization for centralized vulnerability management.

OpenTofu is a declarative infrastructure orchestrator that automates the provisioning and management of cloud resources. It functions as a platform-agnostic interface, allowing users to define their desired environment state in configuration files, which the system then reconciles against live infrastructure to calculate and execute necessary updates. The project utilizes a graph-based execution engine to determine the optimal sequence for resource operations, enabling the parallel processing of independent components to reduce deployment times. To support complex, multi-platform environments, it employs a provider-based plugin architecture that translates generic configuration definitions into specific API calls for various cloud services and third-party providers. Beyond core provisioning, the system facilitates infrastructure lifecycle management through reusable configuration modules that standardize deployments and enforce consistent patterns. It also provides a synchronization layer for state metadata, enabling distributed teams to coordinate changes and maintain consistent environment status across collaborative workflows.

Kubescape is a Kubernetes security posture management platform designed to scan clusters, manifests, and images for misconfigurations, vulnerabilities, and compliance risks. It functions as a comprehensive security suite incorporating a compliance scanner, a container image vulnerability scanner, an admission controller for policy enforcement, and a runtime security monitor. The platform distinguishes itself through runtime-aware vulnerability filtering, which maps libraries loaded in memory to determine if vulnerabilities are actually reachable. It also integrates with AI assistants via a Model Context Protocol server to enable natural language security querying and real-time streaming of findings. The system covers a broad range of security domains, including compliance auditing against industry benchmarks, runtime threat detection using eBPF and system probes, and the automated generation of network policies. It further provides risk quantification for prioritization, infrastructure-as-code auditing, and automated remediation through image patching and manifest fixes. The project is deployed using a Kubernetes operator to automate the lifecycle of its security components and provides specific support for air-gapped environments through offline scanning and manual framework provisioning.

This project is a static analysis engine designed to identify patterns, enforce coding standards, and automate code quality improvements in software projects. By parsing source code into structured abstract syntax trees, it enables deep programmatic inspection and the automated remediation of identified programming issues. The engine functions as a pluggable linting framework, allowing developers to extend its core capabilities through a modular architecture. Users can inject custom rules, parsers, and processors to support non-standard file formats or domain-specific logic. This extensibility is supported by a multi-stage pipeline that handles everything from initial parsing to the generation of automated code fixes. Configuration is managed through a hierarchical system that resolves settings across project directory structures, allowing for consistent rule enforcement and file exclusion patterns. The tool integrates into development workflows via a command-line interface or a programmatic API, which supports both file-based analysis and raw string processing. Performance is optimized through file-system-aware caching, which ensures that only modified files are re-analyzed during execution.