Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
GitHub reports its Copilot Autofix suggestions resolve two-thirds of flagged vulnerabilities with little or no editing — but the other third is where merges go wrong.
319 LLM patches for 64 real CVEs were graded in 2026: only 24.8% were both secure and functional. Speed without validation just merges bugs faster.
GitHub has disclosed that Copilot suggestions match training-set code verbatim about 1% of the time — and a class action over it is still being argued in 2026.
Developers using AI coding assistants wrote less secure code in 4 of 5 tasks in a 2023 Stanford study — and were more confident it was safe.
A 2021 NYU study found roughly 40% of Copilot completions on security-relevant prompts contained exploitable flaws. Here's a field guide to catching them.
One 2025 benchmark found an LLM filter cut Semgrep's false positives by 88.6% — while a separate study found GPT-4 alone flagging vulnerabilities was wrong more often than right.
OWASP's 2025 LLM Top 10 and MITRE ATLAS both treat adversarial testing and posture scanning as separate disciplines — most AI programs still run only one.
NYU found 40% of Copilot-generated code contained exploitable flaws; Samsung banned ChatGPT after three leaks in under 20 days. The productivity math still isn't simple.
Nearly 40% of GitHub Copilot's suggested programs contain exploitable vulnerabilities, and 19.7% of AI-generated code samples reference packages that don't exist.
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