GitHub's VS Code Extension Breach Was a Developer-Device Failure, Not a GitHub-Only Story

With the rapid adoption of AI-enabled services into production applications, it’s important that organizations are able to secure the AI/ML components coming into their software supply chain. The good news is that even if you don’t have a tool specifically for scanning models themselves, you can still apply the same DevSecOps best practices to securing…

Provides verifiable, transparent evidence of advanced LLMs performing complex, multi-step binary and browser exploitation autonomously under strict ASLR and memory randomization defenses.

Building security into normal delivery: the SSDLC guide argues security decisions must land at design review, feature kickoff, PR and release time—not as a late gate once architecture is locked.

Secure code review as boundary hunting: trace attacker-controlled input to database, file, network, template and auth decisions, check authorization invariants, and cover workflow files and secrets.

Automating code review without training developers to ignore it: start with narrow, trusted checks like secrets and risky dependency changes, scope comments to diffs, and tune rules from dismissals.

What to trust, verify or keep human in automated code review: automate repeatable diff-aware checks with repo context, but reserve humans for architecture, trust boundaries and abuse cases.