Debunking SAST Myths with Bandit and Sonar Cloud

Hardcoding credentials into container images is the original sin of containerised apps. And it's more common than anyone admits. Kubernetes has a native solution: ConfigMaps and Secrets. But most guides stop at "here's how to create one" — they don't tell you the part that actually matters. So in Episode 05 of Kubernetes Zero to Hero, I go further: 🟦 ConfigMaps — 3 ways to inject config into pods and when to use each 🔴 Secrets — how they work, how to use them, and the critical thing most tutorials skip 👉 That critical thing: Kubernetes Secrets are NOT encrypted by default. Base64 is encoding, not encryption. Anyone with etcd access reads your "secrets" in plain text. I break down the full production secret management spectrum — from raw K8s Secrets (fine for dev) all the way to HashiCorp Vault — so you can make the right call for your team's risk profile. Also covered: → The hot-reload trap (env vars vs file mounts behave differently on updates) → Immutable ConfigMaps — the versioning pattern that makes rollbacks clean → Private registry pull secrets → A full-stack wiring example: ConfigMap + Secret + Deployment Full article 👇 https://lnkd.in/dX3mrjdF Don't learn this the way I did. ♻️ Repost if someone on your team is still hardcoding config into their images. #Kubernetes #DevOps #CloudNative #Security #Containers #SRE #PlatformEngineering

Hackers Hijacked a GitHub Actions Workflow to Push Malicious Code to PyPI: Elementary Data's open source CLI was the victim, and v0.23.3 is not a version you want installed. Read more: https://lnkd.in/gfgGEaGN 🎪 Step right up to the DevOps community! Join us for an amazing journey of learning and growth.

A pattern I recently used in a Dockerfile to replace vulnerable JARs using find and -exec. What started as multiple repetitive rm and wget commands turned into a single scalable one-liner. Tried to break it down step by step. Read here: https://lnkd.in/d46n-pwR #docker #dockerfile #devops

The recent TeamPCP exploit was a wake-up call: open execution pipelines are fundamentally broken. Platforms like GitHub Actions and Jenkins run third-party code with flat privileges, making them massive targets for credential harvesting. The recent compromise of Trivy proved exactly how fast a single stolen token can cascade across thousands of enterprise environments. At Harness, we built governed execution pipelines to solve this structural flaw. Instead of open access, we enforce strict outbound-only communication, source-level secret isolation, and environment scoping to stop attacks by design. Read our full breakdown of the exploit and why open execution pipelines fail at scale: https://lnkd.in/gqF7H7jT If your team is currently relying on open execution models, let's schedule a brief meeting to discuss how transitioning to a governed architecture can secure your CI/CD workflows.

Your CI pipeline is not less risky than production. It runs with secrets, has internet access, and most engineers treat it as config, not code. Wiz published a full GitHub Actions threat model this week. A few things that stood out: → Untrusted inputs in `run:` steps can trigger script injection without a single PR approval → `GITHUB_TOKEN` is routinely over-permissioned and scoped to the whole repo → Third-party actions are supply chain risk by default. Pinning to a SHA is not optional. → Secrets in env vars leak into logs more often than most teams realize Full breakdown in the comments ↓ #devops #security #githubactions

⚜ End-to-End 𝐃𝐞𝐯𝐒𝐞𝐜𝐎𝐩𝐬 𝐂𝐈/𝐂𝐃 Pipeline in Action Here’s a streamlined pipeline architecture that integrates security, quality, and deployment into a cohesive workflow: 🔵 𝐂𝐈 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 (𝐉𝐞𝐧𝐤𝐢𝐧𝐬) ● Code pushed to GitHub triggers Jenkins CI ● Dependency checks via OWASP ● Code quality & security analysis using SonarQube ● Container image build with Docker ● Vulnerability scanning using Trivy before push ⚫️ 𝐂𝐃 𝐏𝐢𝐩𝐞𝐥𝐢𝐧𝐞 (𝐉𝐞𝐧𝐤𝐢𝐧𝐬) ● Automated update of Docker image version ● Deployment orchestrated via ArgoCD (GitOps approach) ● Application deployed on Kubernetes (K8s) 🔴 𝐎𝐛𝐬𝐞𝐫𝐯𝐚𝐛𝐢𝐥𝐢𝐭𝐲 & 𝐅𝐞𝐞𝐝𝐛𝐚𝐜𝐤 ● Monitoring powered by Prometheus & Grafana ● Alerts and notifications sent via email 💡 This setup ensures: ✔️ Shift-left security (early vulnerability detection) ✔️ Continuous quality gates ✔️ Automated, reliable deployments ✔️ Real-time monitoring and feedback loop #Docker #Kubernetes #Ansible #Jenkins #Maven #ReplicaSet #ReplicaSetController #CICDPIPELINE #GitHub

I built a security scanner that lives inside Claude Code — and it just caught an XSS bug in its own source code. Introducing Shieldbot — an open-source AI security code review agent you can install directly into Claude Code with 3 commands: ``` /plugin marketplace add balasriharsha/shieldbot /plugin install shieldbot /shieldbot . ``` That's it. No dashboards. No SaaS subscriptions. No copy-pasting code into a browser tab. Here's what it does under the hood: → Runs Semgrep with 5,000+ rules (OWASP Top 10, CWE Top 25, injection, XSS, SSRF, taint analysis) → Bandit + Ruff for Python-specific vulnerabilities → detect-secrets for hardcoded API keys, tokens, and credentials → pip-audit + npm audit for CVE-affected dependencies → All scanners run in parallel. Findings are deduplicated. Claude then synthesizes everything into a prioritized report with plain-English explanations and specific code fixes. The result? A risk score, an executive summary, and a ranked list of what to fix first — in under a minute, without leaving your terminal. When I ran it on the repo itself, it caught a Jinja2 autoescape vulnerability in the HTML reporter. One real finding. Zero false positives on secrets. Risk score: 18/100. It also ships as a GitHub Action — add uses: balasriharsha/shieldbot@main to any workflow and findings show up directly in GitHub's Security > Code Scanning tab, with build gating on high+ severity. I built this because security reviews shouldn't require a separate tool, a separate tab, or a separate team. They should be one command away, in the same context where you're already writing code. It's free, open-source (MIT), and works with any MCP-compatible client — not just Claude Code. GitHub: https://lnkd.in/dUhWMdWx PyPI: shieldbot-mcp If you work in security, DevOps, or just write code that ships to production — give it a try and tell me what it finds. #security #devtools #AI #claude #claudecode #MCP #opensource #appsec #devsecops #python #githubactions #npm

YAM – Local AST-based plagiarism detector (MOSS alternative) I built YAM because I wanted a fast, private alternative to MOSS that runs completely on your own machine. It combines the classic winnowing approach with AST analysis, so it catches structural similarity even when students rename variables, reorder code, or make cosmetic changes. The UI is intentionally minimal — it's built to quickly highlight the files you actually need to look at. Features: - Runs 100% locally (no GDPR/FERPA concerns) - Can also run in server mode if you want to host it behind your own firewall - AST + token-based detection - CLI, web, headless, and server modes - Clean CSV & JSON exports - Fully configurable thresholds and parameters Would love honest feedback from anyone who grades programming assignments. GitLab link: https://lnkd.in/dbSnBBNB

BlackDuck_SCA_CICD is your open-source GitHub repository showcasing a streamlined CI/CD integration for Black Duck SCA (Software Composition Analysis), empowering DevSecOps teams to automate vulnerability scanning in modern pipelines. Core Purpose This repo provides scripts, configs, and workflows (likely GitHub Actions or Jenkins-compatible) to embed Black Duck's Detect tool into CI/CD processes. It scans source code, binaries, containers, and dependencies for open-source risks, generating SBOMs for compliance and rapid remediation—ideal for AWS EKS, Azure, or Kubernetes environments. Key Features - Seamless Black Duck Detect integration for Maven, Gradle, or binary scans in pipelines. - Customizable for enterprise tools like Coverity upgrades or Falco runtime security. - Supports policy enforcement, reducing false positives and supply chain vulnerabilities. Professional Value - Developed amid your cybersecurity master's and roles at IBM/Harvard, it demonstrates hands-on expertise in secure pipelines—proven to accelerate scans and boost AppSec at scale. Fork, contribute, or adapt for your next project! Check it out: https://lnkd.in/eJ4dpGQg. Open to collaborations or DevSecOps chats! #DevSecOps #SCA #BlackDuckmend

Why are we still relying on prompts to enforce behavior? A lot of the conversation around agents still feels centered on prompt engineering. Better instructions. Better system prompts. Better skills. Better docs. All of that helps, but it still feels like adding another layer of “please behave this way.” Hooks feel different. I’ve been going down a rabbit hole with GitHub Copilot lately, trying to get more out of agent workflows. Hooks are the first thing that’s made me feel like I’m not just asking for good behavior, but actually enforcing it. They let you put real checks into each step of an agent’s execution. The image below is a simple example: blocking an agent from interacting with .env files for security. That shift matters. It’s the difference between hoping an agent does the right thing and shaping what it’s actually allowed to do. I’m still early in exploring this, so I’d love to learn from others. What are the most useful hooks you’ve built or seen in practice? #ArtificialIntelligence #HarnessEngineering #AgenticAI #AgentSecurity