SShortSingh.
Back to feed

Checkov Tool Catches 35 Security Flaws in 70 Lines of Terraform IaC Code

0
·1 views

Infrastructure as Code (IaC) configurations written in Terraform can carry serious security vulnerabilities, just like application code, according to a developer experiment published on DEV Community. The author deliberately wrote an insecure AWS Terraform setup featuring a public S3 bucket, open security groups, an unencrypted database with a hardcoded password, and a wildcard IAM admin policy. Running Checkov, an open-source SAST tool maintained by Prisma Cloud with over 1,000 built-in policies, against just 70 lines of code surfaced 35 failed security checks in seconds without requiring any AWS credentials. The author then remediated all 35 issues and integrated the Checkov scan into a GitHub Actions CI pipeline to catch misconfigurations automatically before deployment. Similar real-world misconfigurations have been linked to major data breaches, including incidents involving Capital One and exposed US voter records.

Read the full story at DEV Community

This is an AI-generated summary. ShortSingh links to the original source for the complete article.

Discussion (0)

Log in to join the discussion and vote.

Log in

Related stories

0
ProgrammingDEV Community ·

How a Fractional CTO Framework Helps Businesses Find Real AI Automation ROI

Many companies invest in AI tools but fail to see measurable gains in operational metrics such as processing times or error rates, a gap attributed to the lack of a structured integration strategy. A Senior IT Consultant and Fractional CTO with over a decade of experience has developed a step-by-step audit framework to help businesses move beyond mere AI adoption toward strategic integration. The framework begins with precisely mapping existing workflows before any automation is attempted. High-value automation candidates share three traits: high task volume, linear and predictable progression, and low judgment complexity. Vendor invoice processing is cited as a practical example, where repetitive manual data entry from PDF attachments into accounting software can be effectively replaced by OCR and API-based automation.

0
ProgrammingDEV Community ·

Zig Language Integrates Package Management Directly Into Its Build System

Zig, a systems programming language, manages dependencies entirely through its native build system via a build.zig configuration file, eliminating the need for external package managers like Cargo or Go Modules. The system uses checksum-based verification to ensure deterministic, reproducible builds across Windows, Linux, and macOS without manual configuration. Dependencies are fetched automatically, cached locally, and statically linked, producing standalone executables with no runtime overhead. Incremental builds reportedly reduce compile times by 40–60% by recompiling only modules affected by dependency changes. However, the approach still faces challenges including limited tooling maturity compared to established managers like npm, a Zig-specific configuration learning curve, and gaps in legacy C/C++ project support.

0
ProgrammingDEV Community ·

MarketNow Lets MCP Server Developers Sell AI Tools for USDC or Card

MarketNow, a marketplace built by AliceLabs LLC, allows developers to list and sell Model Context Protocol (MCP) servers to AI agents and human developers at one-time prices between $0.99 and $9.99. The platform currently indexes over 8,500 MCP skills and sees around 675 weekly installs, yet most developers offer their tools for free. Sellers retain 80% of each sale, with payments processed via Stripe for card users or x402 and USDC on the Base Ethereum Layer 2 network for crypto transactions. The crypto payment option is aimed at developers in regions where Stripe is unavailable, including India, China, Africa, and Latin America, requiring only a wallet address to receive funds. Each listed tool undergoes a Sentinel L2 security audit covering code scanning, sandbox testing, and dependency checks before going live.

0
ProgrammingDEV Community ·

Four Experiments Show 'Deterministic AI Agent' Claims Fail at the Semantic Layer

A software developer on DEV Community ran four controlled experiments to test the core mechanisms promoted in popular 'production-grade AI agent' articles, which claim deterministic constraints can reliably govern LLM-based agent loops. The three mechanisms tested — lexical-overlap thresholds, temperature-0 evaluators, and phase gates — each proved only formally deterministic, breaking down when applied to real semantic judgments. Lexical overlap alone produced a 50% hard misclassification rate on 30 labeled pairs, including cases where a delete instruction was treated as a continuation of a writing task. The developer also attempted an upgraded fix to address these failures, but that too did not hold up under measurement. While the broader direction of wrapping LLM uncertainty in structured constraints is acknowledged as sound, the article warns that treating unvalidated mechanisms as solved engineering is misleading and potentially incident-grade in production.